Joint association of stress hyperglycemia ratio and body roundness index with stroke incidence: a nationwide cohort study

Joint association of stress hyperglycemia ratio and body roundness index with stroke incidence: a nationwide cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Joint association of stress hyperglycemia ratio and body roundness index with stroke incidence: a nationwide cohort study Aihui Liu, Shutao Qiu, Li Zhang, Han Zhang, Kang He, Runting Ma, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9167358/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background While both the Stress Hyperglycemia Ratio (SHR) and Body Roundness Index (BRI) are recognized risk factors for stroke, their combined prognostic value remains unstudied. This research investigated the individual and joint associations of SHR and BRI with stroke incidence. Methods A prospective cohort analysis was conducted among 8,954 stroke-free participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study (CHARLS). Participants were stratified by SHR and BRI medians. Cox proportional hazards models assessed the association with incident stroke for SHR alone, BRI alone, and their combination. Subgroup analyses evaluated predictive performance. Results Compared to the low SHR/low BRI group, adjusted hazard ratios (aHRs) were: high BRI alone, 1.60 (95% CI: 1.34–1.92); high SHR alone, 1.05 (95% CI: 0.89–1.23); and high SHR/high BRI combined, 1.73 (95% CI: 1.34–2.22). The combined SHR-BRI index significantly enhanced stroke prediction versus either biomarker alone. Subgroup analysis indicated the combined index is particularly predictive within low-risk subgroups—individuals without hypertension, dyslipidemia, diabetes, kidney/liver/cardiac disease, or smoking history—in both sexes ≥ 45 years. Conclusion The novel SHR-BRI combined index exhibits a significant association with stroke incidence in low-risk older adults. It represents a promising tool for early stroke risk prediction and prevention strategies. Stress Hyperglycemia Ratio Body roundness index stroke China Health and Retirement Longitudinal Study Joint indicator Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Stroke represents a prevalent and debilitating cardiovascular disease that seriously threatens global health[ 1 ]. The most comprehensive global burden of disease study on stroke epidemiology data to date showed that stroke ranked the second in the global spectrum of non-communicable diseases (causing approximately seven million deaths) and was also the third leading cause of combined death and disability[ 2 ]. Therefore, identification of relevant factors that can effectively predict the risk of stroke holds key significance for implementing early risk stratification and the development of targeted public health intervention strategies. Stress hyperglycemia markedly exacerbates the pathological process of stroke, consequently increasing the extent of brain tissue damage and elevating the risk of disability and mortality[ 3 , 4 ]. However, relying exclusively on blood glucose levels or glycated haemoglobin A1c (HbA1c) to evaluate this stress condition presents limitations, as these measurements can be significantly influenced by various confounding factors[ 5 , 6 ]. To address this issue, the stress hyperglycemia ratio (SHR) has gained prominence as a method to quantify this acute metabolic response, which is defined as the ratio of blood glucose concentration to HbA1c[ 6 ]. Moreover, obesity has been recognized as an substantial risk factor for stroke[ 7 ]. Although body mass index (BMI) has traditionally employed as the measure of obesity, it fails to differentiate between fat and muscle tissues and does not accurately reflect body fat distribution characteristics[ 8 ]. Noteworthily, the body roundness index (BRI), defined as the ratio of height to waist circumference (WC)[ 9 ]. Furthermore, the BRI is more sensitive to subtle variations in body fat distribution[ 10 , 11 ], making it a superior parameter for assessing health risks associated with obesity. Obesity and blood glucose metabolism are closely interconnected. Free fatty acid (FFA) levels are typically elevated in obese individuals, and FFA metabolic disorders can trigger or worsen insulin resistance [12] , thereby compromising systemic glycemic regulatory capacity during stress states, leading to more severe hyperglycemia under stress [13] . Substantial evidence corroborates the prognostic utility of SHR in evaluating neurological outcomes and predicting adverse prognosis in ischemic stroke patients [14] , with demonstrated significant associations with all-cause mortality risk in this population [15, 16] . Given that SHR can more comprehensively assess the risk of cardiovascular events in patients with chronic total occlusion of the coronary artery when combined with other risk markers [17] , it is scientifically sound to explore the combined effects of SHR and BRI on stroke risk. However, there remains a lack of research evidence regarding the impact of combined exposure to SHR and BRI on stroke incidence. Therefore, this study aimed to conduct a thorough evaluation of the impact of integrating the SHR and BRI on the new-onset stroke risk. The research utilized nationally representative longitudinal data derived from the China Health and Retirement Longitudinal Study (CHARLS) covering the years 2011 to 2018. Furthermore, this study aspired to potentially contribute a novel and easily assessable biomarkers to the field of stroke risk prediction. Methods Study population The study utilized data from the CHARLS, a nationally representative longitudinal cohort study aimed at evaluating the health, socioeconomic, and demographic characteristics of adults aged 45 years and older in China. The study encompassed 150 counties across 28 provinces and employed multistage, stratified, and cluster sampling techniques[18]. CHARLS was initiated in 2011, with follow-up surveys in 2013, 2015, 2018, and 2020, encompassing a total of 17,708 participants from 10,257 households across 450 communities in the aforementioned counties and provinces. Ethical approval for this research was granted by the Peking University Biomedical Ethics Review Committee (IRB00001052-11015), and informed consent was secured from all participants. This study adhered to the Enhanced Reporting of Observational Studies in Epidemiology reporting guidelines[19]. For the analysis presented herein, version D of the harmonized longitudinal CHARLS 2011–2018 dataset, released in June 2021 by the Gateway to Global Aging Data, was employed. The study started with 17,708 participants at baseline in 2011. It excluded 430 participants Missing blood sample 430 participants younger than 45 years or with missing age data, 5861 with missing blood samples, 354 with a stroke diagnosis at baseline or before, 1959 with unavailable SHR or BRI data, and 150 participants who were lost to follow-up. Finally, 8,954 participants were retained for the final analysis. Figure 1 illustrated a detailed flow chart of the inclusion and exclusion process. Data assessment Assessment of SHR and BRI In the CHARLS study, fasting venous blood samples were obtained from participants following an overnight fast at least eight hours, in accordance with a standardized protocol implemented by medical staff at the Chinese Center for Disease Control and Prevention. Plasma glucose levels were assessed enzymatically through the hexokinase method, while HbA1c was quantified via high-performance liquid chromatography utilizing a Tosoh G8 analyzer. These analytical procedures were conducted at the You'anmen Clinical Laboratory Center of Capital Medical University. The formula for calculating the SHR was as follows[20]: The height was measured utilizing a stadiometer while the participant stood barefoot on the instrument platform. Waist circumference was assessed at the level of the umbilicus with the participant in a standing position. All anthropometric measurements were performed according to standardized procedures. The BRI was calculated using the following formula[21]: Assessment of stroke The primary outcome was the incidence of stroke events. Information regarding strokes was systematically gathered by medically trained researchers using a standardized inquiry: “Have you been diagnosed with a stroke by a physician? ” Subsequently, the research team performed data verification and validation procedures to ensure accuracy. According to previous studies[22–24], a stroke event was considered to have occurred. Data Collection and Covariates Trained investigators gathered data regarding sociodemographic characteristics, health status, and self-reported physician-diagnosed diseases, utilizing structured questionnaires. The study also included fundamental anthropometric data (height, weight, waist circumference and BMI) and laboratory test data. The covariates considered in this research comprised age, sex, smoking habits, alcohol consumption, marital status, education level, and place of residence, along with medical conditions including heart disease, diabetes, hypertension, hyperlipidemia, kidney disease, and liver disease[25]. Statistical analysis Descriptive statistics (mean and standard deviation, SD, for continuous data and number and percentage for categorical data) were employed to report the fundamental characteristics of the study population. Differences in baseline characteristics between categories were assessed utilizing a t-test or a Mann-Whitney U test for continuous variables and a chi-square test or Fisher's exact test for categorical variables. Missing data were addressed through multiple imputation with chained equations, and the specific imputation methods are provided in Supplementary Table S1. Following previous literature[23, 25, 26], the median values of BRI (3.952) and SHR (0.999) served as cutoff points to categorize respondents into four distinct groups: low BRI and low SHR, high BRI and low SHR, low BRI and high SHR, and high BRI and high SHR. The distribution of SHR and BRI within the study population was visually represented using histograms. Generalized variance inflation factor analysis was conducted to identify variables with multicollinearity. The Kruskal-Wallis test was used to examine the differences between these groups for continuous variables, and the chi-square test was used to examine the differences for categorical variables. New stroke events were designated as the primary endpoint of the study, and Cox hazard regression models were employed to estimate hazard ratios (HRs) along with their corresponding 95% confidence intervals (95% CIs) associated with the combinations of SHR and BRI across the four groups. The original model and three adjusted models were estimated. Model 1 was adjusted based on age and sex. Model 2 further included smoking status, alcohol consumption, marital status, education level, and residence. Model 3 was based on model 2 for heart problems, diabetes, hypertension, hyperlipidemia, kidney disease, and liver disease. Restricted cubic spline (RCS) regression was utilized to investigate potential nonlinear relationships between SHR or BRI and the incidence of stroke. The cumulative stroke event rates based on the combination of SHR and BRI were presented by Kaplan-Meier curves. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic value. Additionally, subgroup analyses were performed to explore whether the relationship between stroke occurrence and the combination of SHR and BRI varied based on covariate status, including age, sex, education level, and drinking status. All statistical analyses were performed using R version 4.4.1 software, with a two-sided p value of less than 0.05 considered statistically significant. Results Baseline characteristics of study participants A total of 8,954 participants from CHARLS between 2011 and 2018 were included in this study, with an average age of 59.21 (± 9.33). Among the participants, 46.6% were male. Figure 2 showed the distribution of the SHR and BRI. The median of the BRI index was 3.952, and the median of the SHR index was 0.999. Accordingly, the participants were categorized into four groups: T1 (low SHR ≤ 0.999 and low BRI ≤ 3.952) with 2,321 individuals, T2 (low SHR and high BRI) with 2,160 individuals, T3 (high SHR and low BRI) with 2,156 individuals, and T4 (high SHR and high BRI) with 2,317 individuals. The characteristics of the four subgroups concerning the comprehensive SHR and BRI indices are detailed in Table 1 . Table 1 Baseline Characteristic according to SHR and BRI. Variables (mean ± SD, N,%) Total (N = 8954) Low-SHR and low-BRI (N = 2321) Low-SHR and high-BRI (N = 2160) High-SHR and low-BRI (N = 2156) High-SHR and high-BRI (N = 2317) P Age, (years) 59.21 (9.33) 58.67 (9.13) 59.62 (9.43) 59.12 (9.41) 59.43 (9.34) 0.004 Gender, N(%) < 0.001 Male 4173 (46.6) 1369 (59.0) 682 (31.6) 1324 (61.4) 798 (34.4) Female 4781 (53.4) 952 (41.0) 1478 (68.4) 832 (38.6) 1519 (65.6) Marital status, N(%) 0.558 Married and living with spouse 7454 (83.2) 1954 (84.2) 1787 (82.7) 1789 (83.0) 1924 (83.0) Others 1500 (16.8) 367 (15.8) 373 (17.3) 367 (17.0) 393 (17.0) Education level, N(%) 0.003 College or above 265 ( 3.0) 60 ( 2.6) 56 ( 2.6) 68 ( 3.2) 81 ( 3.5) Elementary school or below 6328 (70.7) 1622 (69.9) 1598 (74.0) 1484 (68.8) 1624 (70.1) Middle school 2361 (26.4) 639 (27.5) 506 (23.4) 604 (28.0) 612 (26.4) Residence,N(%) < 0.001 Urban 1529 (17.1) 307 (13.2) 389 (18.0) 337 (15.6) 496 (21.4) Rural 7425 (82.9) 2014 (86.8) 1771 (82.0) 1819 (84.4) 1821 (78.6) Smoking,N(%) < 0.001 Now 2741 (30.6) 964 (41.5) 436 (20.2) 881 (40.9) 460 (19.9) Former 774 ( 8.6) 198 ( 8.5) 162 ( 7.5) 205 ( 9.5) 209 ( 9.0) Never 5439 (60.7) 1159 (49.9) 1562 (72.3) 1070 (49.6) 1648 (71.1) 2160,N(%) < 0.001 Now 2742 (30.6) 821 (35.4) 465 (21.5) 863 (40.0) 593 (25.6) Former 739 ( 8.3) 196 ( 8.4) 203 ( 9.4) 167 ( 7.7) 173 ( 7.5) Never 5473 (61.1) 1304 (56.2) 1492 (69.1) 1126 (52.2) 1551 (66.9) Height,m 1.58 (0.09) 1.60 (0.08) 1.55 (0.10) 1.60 (0.08) 1.56 (0.09) < 0.001 Weight,kg 58.67 (11.62) 53.87 (9.26) 62.56 (11.68) 54.42 (9.65) 63.82 (11.90) < 0.001 Waist,m 0.85 (0.10) 0.78 (0.06) 0.92 (0.08) 0.78 (0.06) 0.93 (0.07) < 0.001 BMI,kg/m2 23.74 (12.05) 21.05 (2.53) 26.42 (17.92) 21.17 (2.75) 26.33 (14.90) < 0.001 Hypertension,N(%) < 0.001 No 6527 (72.9) 1957 (84.3) 1405 (65.0) 1736 (80.5) 1429 (61.7) Yes 2427 (27.1) 364 (15.7) 755 (35.0) 420 (19.5) 888 (38.3) Diabetes,N(%) < 0.001 No 8390 (93.7) 2261 (97.4) 2011 (93.1) 2071 (96.1) 2047 (88.3) Yes 564 ( 6.3) 60 ( 2.6) 149 ( 6.9) 85 ( 3.9) 270 (11.7) Dyslipidemia,N(%) < 0.001 No 8041 (89.8) 2207 (95.1) 1886 (87.3) 2027 (94.0) 1921 (82.9) Yes 913 (10.2) 114 ( 4.9) 274 (12.7) 129 ( 6.0) 396 (17.1) Heart problems < 0.001 No 7776 (86.8) 2058 (88.7) 1827 (84.6) 1956 (90.7) 1935 (83.5) Yes 1178 (13.2) 263 (11.3) 333 (15.4) 200 ( 9.3) 382 (16.5) Kidney disease 0.441 No 8318 (92.9) 2141 (92.2) 2013 (93.2) 2014 (93.4) 2150 (92.8) Yes 636 ( 7.1) 180 ( 7.8) 147 ( 6.8) 142 ( 6.6) 167 ( 7.2) Liver disease 0.107 No 8598 (96.0) 2222 (95.7) 2059 (95.3) 2080 (96.5) 2237 (96.5) Yes 356 ( 4.0) 99 ( 4.3) 101 ( 4.7) 76 ( 3.5) 80 ( 3.5) FBG,mg/dL 110.53 (37.89) 94.94 (16.29) 100.06 (19.10) 117.54 (38.80) 129.39 (52.86) < 0.001 HbA1c 5.27 (0.82) 5.31 (0.62) 5.49 (0.72) 5.00 (0.74) 5.30 (1.06) < 0.001 TG,mg/dL 133.60 (110.49) 102.05 (56.08) 131.46 (75.97) 121.19 (98.25) 178.75 (162.79) < 0.001 TC,mg/dL 193.86 (38.81) 188.75 (35.59) 198.35 (37.75) 187.52 (37.59) 200.70 (42.19) < 0.001 HDL-C,mg/dL 51.29 (15.32) 55.73 (15.13) 49.43 (13.66) 54.05 (16.17) 46.02 (14.26) < 0.001 LDL-C,mg/dL 116.47 (35.17) 114.48 (31.64) 122.94 (33.56) 110.09 (33.69) 118.36 (39.87) < 0.001 SBP, mmHg 129.65 (21.54) 124.34 (20.29) 132.90 (22.33) 126.64 (20.18) 134.74 (21.55) < 0.001 DBP,mmHg 75.39 (12.23) 72.74 (12.14) 77.17 (12.18) 73.57 (11.71) 78.06 (12.00) < 0.001 SHR 1.06 (0.25) 0.91 (0.10) 0.91 (0.10) 1.22 (0.23) 1.23 (0.26) < 0.001 BRI 4.31 (3.37) 3.13 (0.60) 5.49 (3.83) 3.17 (0.59) 5.46 (4.92) < 0.001 stroke,N(%) < 0.001 No 8347 (93.2) 2222 (95.7) 1972 (91.3) 2041 (94.7) 2112 (91.2) Yes 607 ( 6.8) 99 ( 4.3) 188 ( 8.7) 115 ( 5.3) 205 ( 8.8) Analyses of SHR or BRI with incident stroke During the longest follow-up period of seven years, a total of 607 participants (6.8%) had a stroke. Table 2 showed the risk of stroke incidence according to SHR and BRI index. In the group with high BRI index, the stroke incidence was 88% (HR: 1.88; 95% CI: 1.59–2.22) higher compared to the group with low BRI index. Notably, a high BRI index emerged as a protective risk factor for stroke, maintaining statistical significance in the adjusted model 3, demonstrated by a Hazard Ratio of 1.60 (95% CI: 1.34–1.92), indicating a 38% reduction in the risk of stroke (refer to Table 2 ). RCS regression analysis found that the SHR index was nonlinearly related to the incidence of stroke ( P for overall < 0.001, P for nonlinear < 0.001), and the BRI index was linearly related to the incidence of stroke (P for overall = 0.375, P for nonlinear = 0.271, Fig. 3 ). Table 2 Incidence risk of stroke stratiffed by SHR and BRI in baseline Case Crude model Model 1 Model 2 Model 3 HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value SHR low-SHR 287 Ref Ref Ref Ref high-SHR 320 1.12 (0.96–1.32) 0.158 1.12 (0.95–1.31) 0.177 1.12 (0.95–1.31) 0.179 1.05 (0.89–1.23) 0.568 BRI low-BRI 214 Ref Ref Ref Ref high-BRI 393 1.88 (1.59–2.22) <0.001 2.02 (1.70–2.40) <0.001 2.02 (1.69–2.40) <0.001 1.60 (1.34–1.92) <0.001 Joint analyses of SHR and BRI with incident stroke In the joint analysis of SHR and BRI concerning stroke incidence, the study population was divided into four distinct groups. Figure 4 showed the Kaplan-Meier curve illustrating the cumulative incidence of stroke among all participants (P < 0.01). The cumulative incidence of stroke was notably highest among individuals with high BRI and high SHR values, demonstrated by an HR of 2.08 (95% CI: 1.64–2.66) as depicted in Fig. 4 . Taking low-SHR and low-BRI as the reference group, Table 3 showed the stroke risk in other combination groups. Notably, the high-BRI group exhibited the highest incident rate of stroke irrespective of SHR levels. In the adjusted model 3, the risk of stroke for the low-SHR and high-BRI group was recorded at an HR of 1.80 (95% CI: 1.39–2.32), while the high-SHR and high-BRI group exhibited an HR of 1.73 (95% CI: 1.34–2.22). The prevalence of stroke in the reference group was reported at 80% and 73%, as shown in Table 3 . The cumulative incidence of stroke was markedly elevated among individuals demonstrating high values in both BRI and SHR, yielding an HR of 2.08 (95% CI: 1.64–2.66), as illustrated in Fig. 4 . Table 3 Incidence risk of stroke stratified by the joint of SHR and BRI low-SHR and low-BRI Case Crude model Model 1 Model 2 Model 3 99 Ref Ref Ref Ref low-SHR and high-BRI 188 2.08 (1.64–2.66) <0.001 2.23 (1.74–2.86) <0.001 2.23 (1.74–2.86) <0.001 1.80 (1.39–2.32) <0.001 high-SHR and low-BRI 115 1.26 (0.96–1.65) 0.095 1.24 (0.95–1.62) 0.12 1.24 (0.95–1.62) 0.119 1.20 (0.92–1.57) 0.184 high-SHR and high-BRI 205 2.13 (1.67–2.70) <0.001 2.26 (1.77–2.89) <0.001 2.27 (1.78–2.91) <0.001 1.73 (1.34–2.22) <0.001 Subgroup analyses To further refine the relationship between stroke and the combined the SHR and BRI groups, the study performed subgroup analyses. The results were shown in Supplementary Table S2. The results of subgroup analysis indicated that the combined index of SHR and BRI serves as predictor of stroke within the low-risk population. This population includes individuals who have never smoked and do not suffer from hypertension, dyslipidemia, diabetes, kidney disease, liver disease, or cardiac conditions. These results were applicable to both males and females aged 45 years and older. Sensitivity analyses Sensitivity analyses were conducted to evaluate the robustness of the primary findings ( Supplementary Table S3). The relationships between SHR and BRI with stroke risk remained consistent with the principal results following the exclusion of participants with incomplete data. Discussion This study examined 8,954 Chinese adults aged 45 and older who were followed for seven years. The incidence of a first stroke in this group was 6.78%. The results confirmed that a higher BRI index was linked to a increased risk of stroke. When participants were divided based on the median values of the BRI index and SHR, those in the higher BRI and SHR index groups showed more increased risk of stroke. Additionally, in subgroup analysis, the combination of SHR and BRI remained a predictor of stroke even in a low-risk population. The BRI, a newer body measurement index, can more accurately reflect visceral fat[ 9 ]. BRI models the human body as an ellipse with height as the major axis and waist circumference as the minor axis, and calculates the eccentricity of the ellipse. Peng et al. found that for every unit increase in BRI, the risk of stroke in middle-aged and older Chinese people increased by 15.8%[ 27 ]. Cross-sectional data from American adults also confirmed that BRI was positively associated with stroke, with every unit increase in BRI increasing the incidence of stroke by 5.7%[ 28 ]. Yao et al. observed that compared to British residents, rural Chinese residents had higher BRI levels, which made them more susceptible to stroke due to multiple diseases[ 29 ]. Visceral adipose tissue has a significant causal relationship with ischemic stroke, a notable causal effect on cardioembolic stroke, and a potential causal impact on small vessel stroke and large artery atherosclerotic stroke[ 30 ]. There are multiple mechanisms linking BRI and stroke. Firstly, visceral adipose tissue may affect insulin resistance, inflammatory immune response, and coagulation by secreting proinflammatory mediators like adiponectin, C-reactive protein, interleukin-6, and tumor necrosis factor-α, leading to endothelial dysfunction and atherosclerosis[ 31 , 32 ]. Additionally, adipose factors can generate reactive oxygen species. Excessive reactive oxygen species and antioxidant depletion can cause oxidative stress, damage the blood-brain barrier, and ultimately result in brain injury[ 33 ]. Moreover, visceral fat can also affect brain tissue through the “gut-microbiota-brain axis”[ 34 ]. Monitoring changes in BRI can help promptly identify trends in visceral obesity, allowing for timely intervention measures, which aids in understanding the impact of obesity on stroke. The SHR serves as an indicator of immediate fluctuations and is adjusted based on the average estimated blood glucose level over the previous three months, thereby reflecting the relative change from baseline during stress[ 3 ]. Roberts et al. demonstrated that SHR superior prognostic insights compared to the glycemic gap and glucose measurements in evaluating the outcomes of ischemic strokes[ 35 ]. Furthermore, studies indicated that elevated stress hyperglycemia correlates with an increased risk of stroke-related pneumonia and was associated with heightened mortality in individuals experiencing acute ischemic strokes[ 36 ]and heightened mortality in individuals experiencing acute ischemic strokes[ 37 ]. Additionally, previous studies have reported that stroke patients with combined stress hyperglycemia had a worse prognosis[ 38 – 40 ]. Therefore, SHR may be used as both a predictor of stroke risk and an important predictor of poor long-term prognosis in stroke patients[ 16 ], thereby assisting risk assessment and clinical decision-making. Several factors may influence the relationship between SHR and stroke. Firstly, hyperglycemia can stimulate the production of inflammatory cytokines, oxidative stress, and other active substances[ 41 ]. This exacerbated inflammatory response may lead to neuroinflammation and the release of neurotoxins and vasoconstrictor factors, which can further compromise endothelial cell integrity and diminish vascular repair and protection. As a result, the risk of intracranial hemorrhage, potentially induced by vascular treatments, may be heightened. Additionally, the accumulation of lactic acid as a consequence of anaerobic glucose metabolism results in intracellular acidosis, which may exacerbate ischemic brain injury by intensifying the effects of lipid and free radical peroxidation[ 42 ]. Thus, SHR is implicated in the aggravation of ischemic brain damage and the deterioration of neurological deficits through multiple pathways. This study represented, to the best of our knowledge, the first instance of combining SHR and BRI for research. Existing evidence indicates that the combination of SHR or BRI with other biomarkers enhances the capability to effectively assess disease risk[ 43 , 44 ]. Nonetheless, research on the combination of SHR and BRI for stroke risk was still limited. A study conducted by Yao et al. focusing on rural residents in Northeast China, revealed an elevated triglyceride glucose (TyG)-BRI which correlated with a 15% increase in the risk of ischemic stroke[ 29 ]. Furthermore, findings from Wang et al. established that concurrent higher values of BRI and TyG were associated with a 78% increase in stroke risk[ 23 ]. The combined index analysis of this study provided more comprehensive information, including stress blood sugar levels and body obesity. Moreover, this analysis facilitated the identification of distinctions among multiple indicators, thereby allowing for earlier predictions of stroke. In subgroup analysis, the combination of SHR and BRI serve as a predictor of stroke risk in low-risk populations (without hypertension, dyslipidemia, diabetes, kidney disease, liver disease, or heart problems). Both SHR and BRI were noninvasive markers that can be easily obtained in clinical settings. Incorporating these indices into clinical practice may present an innovative and practical approach for the screening and intervention of individuals at heightened risk for stroke. Our study participants came from different urban and rural areas across the country, rendering the sample more representative and significantly enhancing the generalizability of the study findings. However, several limitations must be acknowledged. Firstly, the exclusion of individuals with abnormal or missing values for exposure factors may have introduced a degree of selection bias. Secondly, CHARLS is a database that collects health status information through questionnaires, so the diagnosis of stroke based solely on participant disclosures. However, previous validation supports the reliability of these self-reported stroke events, thereby enhancing their credibility[ 45 , 46 ]. Thirdly, the CHARLS questionnaire does not classify stroke subtypes, which represents a limitation of this study. Moreover, this study did not account for temporal variations in the BRI and SHR indicators. Future research should incorporate the dynamic effects of these indicators over time to provide a more comprehensive assessment of their interrelation. Conclusion This study demonstrated that elevated BRI and SHR were significantly associated with an increased risk of stroke. The synergistic evaluation of BRI and SHR may enable the earlier detection of stroke indicators within the general population, which supports the role of stress, blood glucose, and visceral fat in identifying and screening individuals at high risk of stroke. Declarations Ethics approval and consent to participate: Not applicable. Consent for publication: Not appliable. Competing interests: The authors declare no competing interests. Authors’ contributions AL and SQ performed data analysis and wrote the initial draft of the article. HZ, HS,KH, LZ, LW and CG contributed to the critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. QG provided resources and inspections. All authors have read and agreed to the published version of the manuscript. Funding: No Funding. Author Contribution AL and SQ performed data analysis and wrote the initial draft of the article. HZ, HS,KH, LZ, LW and CG contributed to the critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. QG provided resources and inspections. All authors have read and agreed to the published version of the manuscript. Acknowledgement Special thanks are due to Dr. Qiang Gao for their invaluable guidance and expertise throughout the study. Our gratitude extends to the participants of the study, without whom this research would not have been possible. We also thank the reviewers for their constructive comments and suggestions, which have helped to improve the manuscript. 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Peng L, Wen Z, Xia C, Sun Y, Zhang Y. Association between body roundness index and stroke incidence among middle-aged and older adults in China: a longitudinal analysis of the CHARLS data. Postgrad Med J 2025. Gan J, Yang X, Wu J, Mo P, Deng Y, Liu Y, Wu Y, Liu P, Ji L, Jiang H, et al. Association between body roundness index and stroke results from the 1999–2018 NHANES. J stroke Cerebrovasc diseases: official J Natl Stroke Association. 2025;34(3):108243. Yao F, Cui J, Shen Y, Jiang Y, Li Y, Liu X, Feng H, Jiao Z, Liu C, Hu F, et al. Evaluating a new obesity indicator for stroke risk prediction: comparative cohort analysis in rural settings of two nations. BMC Public Health. 2024;24(1):3301. Xu R, Hu X, Wang T, Yang Y, Jiang N, Luo J, Zhang X, Patel AB, Dmytriw AA, Jiao L. Visceral Adiposity and Risk of Stroke: A Mendelian Randomization Study. Front Neurol. 2022;13:804851. Bi H, Zhang Y, Qin P, Wang C, Peng X, Chen H, Zhao D, Xu S, Wang L, Zhao P, et al. Association of Chinese Visceral Adiposity Index and Its Dynamic Change With Risk of Carotid Plaque in a Large Cohort in China. J Am Heart Association. 2022;11(1):e022633. Mirabelli M, Misiti R, Sicilia L, Brunetti FS, Chiefari E, Brunetti A, Foti DP. Hypoxia in Human Obesity: New Insights from Inflammation towards Insulin Resistance-A Narrative Review. Int J Mol Sci 2024, 25(18). Santos AL, Sinha S. Obesity and aging: Molecular mechanisms and therapeutic approaches. Ageing Res Rev. 2021;67:101268. Asadi A, Shadab Mehr N, Mohamadi MH, Shokri F, Heidary M, Sadeghifard N, Khoshnood S. Obesity and gut-microbiota-brain axis: A narrative review. J Clin Lab Anal. 2022;36(5):e24420. Roberts G, Sires J, Chen A, Thynne T, Sullivan C, Quinn S, Chen WS, Meyer E. A comparison of the stress hyperglycemia ratio, glycemic gap, and glucose to assess the impact of stress-induced hyperglycemia on ischemic stroke outcome. J diabetes. 2021;13(12):1034–42. Tao J, Hu Z, Lou F, Wu J, Wu Z, Yang S, Jiang X, Wang M, Huang Q, Ren W. Higher Stress Hyperglycemia Ratio Is Associated With a Higher Risk of Stroke-Associated Pneumonia. Front Nutr. 2022;9:784114. Zhang Y, Yin X, Liu T, Ji W, Wang G. Association between the stress hyperglycemia ratio and mortality in patients with acute ischemic stroke. Sci Rep. 2024;14(1):20962. Capes SE, Hunt D, Malmberg K, Pathak P, Gerstein HC. Stress hyperglycemia and prognosis of stroke in nondiabetic and diabetic patients: a systematic overview. Stroke. 2001;32(10):2426–32. Yuan C, Chen S, Ruan Y, Liu Y, Cheng H, Zeng Y, Chen Y, Cheng Q, Huang G, He W, et al. The Stress Hyperglycemia Ratio is Associated with Hemorrhagic Transformation in Patients with Acute Ischemic Stroke. Clin Interv Aging. 2021;16:431–42. Li J, Quan K, Wang Y, Zhao X, Li Z, Pan Y, Li H, Liu L, Wang Y. Effect of Stress Hyperglycemia on Neurological Deficit and Mortality in the Acute Ischemic Stroke People With and Without Diabetes. Front Neurol. 2020;11:576895. Zhou Y, He Y, Yan S, Chen L, Zhang R, Xu J, Hu H, Liebeskind DS, Lou M. Reperfusion Injury Is Associated With Poor Outcome in Patients With Recanalization After Thrombectomy. Stroke. 2023;54(1):96–104. Siesjö BK, Bendek G, Koide T, Westerberg E, Wieloch T. Influence of acidosis on lipid peroxidation in brain tissues in vitro. J Cereb blood flow metabolism: official J Int Soc Cereb Blood Flow Metabolism. 1985;5(2):253–8. Zhang X, Ding L, Hu H, He H, Xiong Z, Zhu X. Associations of Body-Roundness Index and Sarcopenia with Cardiovascular Disease among Middle-Aged and Older Adults: Findings from CHARLS. J Nutr Health Aging. 2023;27(11):953–9. Gu Y, Zhou Z, Zhao X, Ye X, Qin K, Liu J, Zhang X, Ji Y. Inflammatory burden index (IBI) and body roundness index (BRI) in gallstone risk prediction: insights from NHANES 2017–2020. Lipids Health Dis. 2025;24(1):63. Yuan X, Liu T, Wu L, Zou ZY, Li C. Validity of self-reported diabetes among middle-aged and older Chinese adults: the China Health and Retirement Longitudinal Study. BMJ open. 2015;5(4):e006633. Choe S, Lee J, Lee J, Kang D, Lee JK, Shin A. Validity of Self-reported Stroke and Myocardial Infarction in Korea: The Health Examinees (HEXA) Study. J Prev Med public health = Yebang Uihakhoe chi. 2019;52(6):377–83. Additional Declarations No competing interests reported. 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09:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9167358/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9167358/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107675217,"identity":"e91dba17-07d4-4f6f-b578-0351bd25e7ca","added_by":"auto","created_at":"2026-04-24 00:41:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75766,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study population selection.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9167358/v1/245f7c81364d8a2149a74b53.jpg"},{"id":107706413,"identity":"c6245c02-e1d6-4218-b4c5-c370b60ba4a6","added_by":"auto","created_at":"2026-04-24 09:18:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35135,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of SHR index ad BRI index.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9167358/v1/14e610b51c4cd41544d961dd.jpg"},{"id":107675215,"identity":"cdb6fe29-2788-40fb-937f-ad19373627a7","added_by":"auto","created_at":"2026-04-24 00:41:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50089,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear associations of BRI and SHR with stroke incidence. The Graphs show HRs for stroke incidence based on Cox hazaeds regression model 3 (adjusted for Age, sex smoking, drinking, marital status, education , and residence, heart problems, diabetes, hypertension, hyperlipidemia, kidney disease, and liver disease). Solid lines indicate HRs. Shadow shapes indicate 95% CIs.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9167358/v1/e9cb2f9f46f89933d34fad3e.jpg"},{"id":107675216,"identity":"d8605446-6bcf-4cda-87a2-05b76e7cff8a","added_by":"auto","created_at":"2026-04-24 00:41:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18669,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier plot of stroke incidence by SHR and BRI\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9167358/v1/be3eb3b8aff2f722699332d4.jpg"},{"id":107709055,"identity":"35137aaf-ee8d-40ca-b2cc-363abac8e808","added_by":"auto","created_at":"2026-04-24 09:34:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":819161,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9167358/v1/ef50154e-11d5-45ea-8839-248062c74281.pdf"},{"id":107675213,"identity":"021075fb-13f2-423a-a0e7-b800cb194914","added_by":"auto","created_at":"2026-04-24 00:41:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":35363,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9167358/v1/8e2de9500112da44138714a5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint association of stress hyperglycemia ratio and body roundness index with stroke incidence: a nationwide cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eStroke represents a prevalent and debilitating cardiovascular disease that seriously threatens global health[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The most comprehensive global burden of disease study on stroke epidemiology data to date showed that stroke ranked the second in the global spectrum of non-communicable diseases (causing approximately seven million deaths) and was also the third leading cause of combined death and disability[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, identification of relevant factors that can effectively predict the risk of stroke holds key significance for implementing early risk stratification and the development of targeted public health intervention strategies.\u003c/p\u003e \u003cp\u003eStress hyperglycemia markedly exacerbates the pathological process of stroke, consequently increasing the extent of brain tissue damage and elevating the risk of disability and mortality[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, relying exclusively on blood glucose levels or glycated haemoglobin A1c (HbA1c) to evaluate this stress condition presents limitations, as these measurements can be significantly influenced by various confounding factors[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. To address this issue, the stress hyperglycemia ratio (SHR) has gained prominence as a method to quantify this acute metabolic response, which is defined as the ratio of blood glucose concentration to HbA1c[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, obesity has been recognized as an substantial risk factor for stroke[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although body mass index (BMI) has traditionally employed as the measure of obesity, it fails to differentiate between fat and muscle tissues and does not accurately reflect body fat distribution characteristics[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Noteworthily, the body roundness index (BRI), defined as the ratio of height to waist circumference (WC)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, the BRI is more sensitive to subtle variations in body fat distribution[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], making it a superior parameter for assessing health risks associated with obesity.\u003c/p\u003e \u003cp\u003eObesity and blood glucose metabolism are closely interconnected. Free fatty acid (FFA) levels are typically elevated in obese individuals, and FFA metabolic disorders can trigger or worsen insulin resistance\u003csup\u003e[12]\u003c/sup\u003e, thereby compromising systemic glycemic regulatory capacity during stress states, leading to more severe hyperglycemia under stress\u003csup\u003e[13]\u003c/sup\u003e. Substantial evidence corroborates the prognostic utility of SHR in evaluating neurological outcomes and predicting adverse prognosis in ischemic stroke patients\u003csup\u003e[14]\u003c/sup\u003e, with demonstrated significant associations with all-cause mortality risk in this population\u003csup\u003e[15, 16]\u003c/sup\u003e. Given that SHR can more comprehensively assess the risk of cardiovascular events in patients with chronic total occlusion of the coronary artery when combined with other risk markers\u003csup\u003e[17]\u003c/sup\u003e, it is scientifically sound to explore the combined effects of SHR and BRI on stroke risk. However, there remains a lack of research evidence regarding the impact of combined exposure to SHR and BRI on stroke incidence.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to conduct a thorough evaluation of the impact of integrating the SHR and BRI on the new-onset stroke risk. The research utilized nationally representative longitudinal data derived from the China Health and Retirement Longitudinal Study (CHARLS) covering the years 2011 to 2018. Furthermore, this study aspired to potentially contribute a novel and easily assessable biomarkers to the field of stroke risk prediction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eThe study utilized data from the CHARLS, a nationally representative longitudinal cohort study aimed at evaluating the health, socioeconomic, and demographic characteristics of adults aged 45 years and older in China. The study encompassed 150 counties across 28 provinces and employed multistage, stratified, and cluster sampling techniques[18]. CHARLS was initiated in 2011, with follow-up surveys in 2013, 2015, 2018, and 2020, encompassing a total of 17,708 participants from 10,257 households across 450 communities in the aforementioned counties and provinces. Ethical approval for this research was granted by the Peking University Biomedical Ethics Review Committee (IRB00001052-11015), and informed consent was secured from all participants. This study adhered to the Enhanced Reporting of Observational Studies in Epidemiology reporting guidelines[19]. For the analysis presented herein, version D of the harmonized longitudinal CHARLS 2011\u0026ndash;2018 dataset, released in June 2021 by the Gateway to Global Aging Data, was employed.\u003c/p\u003e\n \u003cp\u003eThe study started with 17,708 participants at baseline in 2011. It excluded 430 participants Missing blood sample 430 participants younger than 45 years or with missing age data, 5861 with missing blood samples, 354 with a stroke diagnosis at baseline or before, 1959 with unavailable SHR or BRI data, and 150 participants who were lost to follow-up. Finally, 8,954 participants were retained for the final analysis. Figure\u0026nbsp;1 illustrated a detailed flow chart of the inclusion and exclusion process.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eData assessment\u003c/h3\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003eAssessment of SHR and BRI\u003c/h2\u003e\n \u003cp\u003eIn the CHARLS study, fasting venous blood samples were obtained from participants following an overnight fast at least eight hours, in accordance with a standardized protocol implemented by medical staff at the Chinese Center for Disease Control and Prevention. Plasma glucose levels were assessed enzymatically through the hexokinase method, while HbA1c was quantified via high-performance liquid chromatography utilizing a Tosoh G8 analyzer. These analytical procedures were conducted at the You\u0026apos;anmen Clinical Laboratory Center of Capital Medical University. The formula for calculating the SHR was as follows[20]:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1776972181.png\" style=\"width: 228px;\"\u003e\u003c/p\u003e\n \u003cp\u003eThe height was measured utilizing a stadiometer while the participant stood barefoot on the instrument platform. Waist circumference was assessed at the level of the umbilicus with the participant in a standing position. All anthropometric measurements were performed according to standardized procedures. The BRI was calculated using the following formula[21]:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1776972196.png\" style=\"width: 298px;\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eAssessment of stroke\u003c/h2\u003e\n \u003cp\u003eThe primary outcome was the incidence of stroke events. Information regarding strokes was systematically gathered by medically trained researchers using a standardized inquiry: \u0026ldquo;Have you been diagnosed with a stroke by a physician? \u0026rdquo; Subsequently, the research team performed data verification and validation procedures to ensure accuracy. According to previous studies[22\u0026ndash;24], a stroke event was considered to have occurred.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eData Collection and Covariates\u003c/h2\u003e\n \u003cp\u003eTrained investigators gathered data regarding sociodemographic characteristics, health status, and self-reported physician-diagnosed diseases, utilizing structured questionnaires. The study also included fundamental anthropometric data (height, weight, waist circumference and BMI) and laboratory test data. The covariates considered in this research comprised age, sex, smoking habits, alcohol consumption, marital status, education level, and place of residence, along with medical conditions including heart disease, diabetes, hypertension, hyperlipidemia, kidney disease, and liver disease[25].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics (mean and standard deviation, SD, for continuous data and number and percentage for categorical data) were employed to report the fundamental characteristics of the study population. Differences in baseline characteristics between categories were assessed utilizing a t-test or a Mann-Whitney U test for continuous variables and a chi-square test or Fisher\u0026apos;s exact test for categorical variables. Missing data were addressed through multiple imputation with chained equations, and the specific imputation methods are provided in Supplementary Table S1. Following previous literature[23, 25, 26], the median values of BRI (3.952) and SHR (0.999) served as cutoff points to categorize respondents into four distinct groups: low BRI and low SHR, high BRI and low SHR, low BRI and high SHR, and high BRI and high SHR. The distribution of SHR and BRI within the study population was visually represented using histograms. Generalized variance inflation factor analysis was conducted to identify variables with multicollinearity. The Kruskal-Wallis test was used to examine the differences between these groups for continuous variables, and the chi-square test was used to examine the differences for categorical variables. New stroke events were designated as the primary endpoint of the study, and Cox hazard regression models were employed to estimate hazard ratios (HRs) along with their corresponding 95% confidence intervals (95% CIs) associated with the combinations of SHR and BRI across the four groups.\u003c/p\u003e\n \u003cp\u003eThe original model and three adjusted models were estimated. Model 1 was adjusted based on age and sex. Model 2 further included smoking status, alcohol consumption, marital status, education level, and residence. Model 3 was based on model 2 for heart problems, diabetes, hypertension, hyperlipidemia, kidney disease, and liver disease. Restricted cubic spline (RCS) regression was utilized to investigate potential nonlinear relationships between SHR or BRI and the incidence of stroke. The cumulative stroke event rates based on the combination of SHR and BRI were presented by Kaplan-Meier curves. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic value.\u003c/p\u003e\n \u003cp\u003eAdditionally, subgroup analyses were performed to explore whether the relationship between stroke occurrence and the combination of SHR and BRI varied based on covariate status, including age, sex, education level, and drinking status. All statistical analyses were performed using R version 4.4.1 software, with a two-sided p value of less than 0.05 considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of study participants\u003c/h2\u003e \u003cp\u003eA total of 8,954 participants from CHARLS between 2011 and 2018 were included in this study, with an average age of 59.21 (\u0026plusmn;\u0026thinsp;9.33). Among the participants, 46.6% were male. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the distribution of the SHR and BRI. The median of the BRI index was 3.952, and the median of the SHR index was 0.999. Accordingly, the participants were categorized into four groups: T1 (low SHR\u0026thinsp;\u0026le;\u0026thinsp;0.999 and low BRI\u0026thinsp;\u0026le;\u0026thinsp;3.952) with 2,321 individuals, T2 (low SHR and high BRI) with 2,160 individuals, T3 (high SHR and low BRI) with 2,156 individuals, and T4 (high SHR and high BRI) with 2,317 individuals. The characteristics of the four subgroups concerning the comprehensive SHR and BRI indices are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristic according to SHR and BRI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, N,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;8954)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-SHR and low-BRI (N\u0026thinsp;=\u0026thinsp;2321)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-SHR and high-BRI (N\u0026thinsp;=\u0026thinsp;2160)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-SHR and low-BRI (N\u0026thinsp;=\u0026thinsp;2156)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh-SHR and high-BRI (N\u0026thinsp;=\u0026thinsp;2317)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.21 (9.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.67 (9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.62 (9.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.12 (9.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.43 (9.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4173 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1369 (59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e682 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1324 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e798 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4781 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e952 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1478 (68.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e832 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1519 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried and living with spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7454 (83.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1954 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1787 (82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1789 (83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1924 (83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1500 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e367 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e373 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e367 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e393 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e265 ( 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 ( 2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56 ( 2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68 ( 3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81 ( 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6328 (70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1622 (69.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1598 (74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1484 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1624 (70.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2361 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e639 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e506 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e604 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e612 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence,N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1529 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e307 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e389 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e337 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e496 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7425 (82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2014 (86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1771 (82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1819 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1821 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking,N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2741 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e964 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e436 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e881 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e460 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e774 ( 8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198 ( 8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162 ( 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e205 ( 9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e209 ( 9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5439 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1159 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1562 (72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1070 (49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1648 (71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2160,N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2742 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e821 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e465 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e863 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e593 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e739 ( 8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196 ( 8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e203 ( 9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e167 ( 7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e173 ( 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5473 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1304 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1492 (69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1126 (52.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1551 (66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight,m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.60 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.55 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.60 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.56 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight,kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.67 (11.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.87 (9.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.56 (11.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.42 (9.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.82 (11.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist,m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI,kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.74 (12.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.05 (2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.42 (17.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.17 (2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.33 (14.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension,N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6527 (72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1957 (84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1405 (65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1736 (80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1429 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2427 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e364 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e755 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e420 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e888 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes,N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8390 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2261 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2011 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2071 (96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2047 (88.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e564 ( 6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 ( 2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149 ( 6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85 ( 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e270 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia,N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8041 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2207 (95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1886 (87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2027 (94.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1921 (82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e913 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114 ( 4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e274 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e129 ( 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e396 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7776 (86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2058 (88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1827 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1956 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1935 (83.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1178 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e263 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e333 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e200 ( 9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e382 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.441\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8318 (92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2141 (92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2013 (93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2014 (93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2150 (92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e636 ( 7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180 ( 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147 ( 6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e142 ( 6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e167 ( 7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.107\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8598 (96.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2222 (95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2059 (95.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2080 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2237 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e356 ( 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 ( 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101 ( 4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76 ( 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80 ( 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110.53 (37.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.94 (16.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.06 (19.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e117.54 (38.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e129.39 (52.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.27 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.31 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.49 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.00 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.30 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133.60 (110.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102.05 (56.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131.46 (75.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e121.19 (98.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e178.75 (162.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193.86 (38.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188.75 (35.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198.35 (37.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e187.52 (37.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e200.70 (42.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.29 (15.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.73 (15.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.43 (13.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.05 (16.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.02 (14.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116.47 (35.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114.48 (31.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122.94 (33.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110.09 (33.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e118.36 (39.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129.65 (21.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124.34 (20.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e132.90 (22.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e126.64 (20.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134.74 (21.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP,mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.39 (12.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.74 (12.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.17 (12.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.57 (11.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.06 (12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.23 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.31 (3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.13 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.49 (3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.17 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.46 (4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estroke,N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8347 (93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2222 (95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1972 (91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2041 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2112 (91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e607 ( 6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 ( 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e188 ( 8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115 ( 5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e205 ( 8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalyses of SHR or BRI with incident stroke\u003c/h2\u003e \u003cp\u003eDuring the longest follow-up period of seven years, a total of 607 participants (6.8%) had a stroke. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the risk of stroke incidence according to SHR and BRI index. In the group with high BRI index, the stroke incidence was 88% (HR: 1.88; 95% CI: 1.59\u0026ndash;2.22) higher compared to the group with low BRI index. Notably, a high BRI index emerged as a protective risk factor for stroke, maintaining statistical significance in the adjusted model 3, demonstrated by a Hazard Ratio of 1.60 (95% CI: 1.34\u0026ndash;1.92), indicating a 38% reduction in the risk of stroke (refer to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). RCS regression analysis found that the SHR index was nonlinearly related to the incidence of stroke ( P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the BRI index was linearly related to the incidence of stroke (P for overall\u0026thinsp;=\u0026thinsp;0.375, P for nonlinear\u0026thinsp;=\u0026thinsp;0.271, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eIncidence risk of stroke stratiffed by SHR and BRI in baseline\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude model\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 1\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 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow-SHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh-SHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.96\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12 (0.95\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.12 (0.95\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.05 (0.89\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow-BRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh-BRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88 (1.59\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.02 (1.70\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.02 (1.69\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.60 (1.34\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eJoint analyses of SHR and BRI with incident stroke\u003c/h2\u003e \u003cp\u003eIn the joint analysis of SHR and BRI concerning stroke incidence, the study population was divided into four distinct groups. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed the Kaplan-Meier curve illustrating the cumulative incidence of stroke among all participants (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The cumulative incidence of stroke was notably highest among individuals with high BRI and high SHR values, demonstrated by an HR of 2.08 (95% CI: 1.64\u0026ndash;2.66) as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Taking low-SHR and low-BRI as the reference group, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed the stroke risk in other combination groups. Notably, the high-BRI group exhibited the highest incident rate of stroke irrespective of SHR levels. In the adjusted model 3, the risk of stroke for the low-SHR and high-BRI group was recorded at an HR of 1.80 (95% CI: 1.39\u0026ndash;2.32), while the high-SHR and high-BRI group exhibited an HR of 1.73 (95% CI: 1.34\u0026ndash;2.22). The prevalence of stroke in the reference group was reported at 80% and 73%, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The cumulative incidence of stroke was markedly elevated among individuals demonstrating high values in both BRI and SHR, yielding an HR of 2.08 (95% CI: 1.64\u0026ndash;2.66), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\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\u003eIncidence risk of stroke stratified by the joint of SHR and BRI\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u003elow-SHR and low-BRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModel 3\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=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \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-SHR and high-BRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003cp\u003e(1.64\u0026ndash;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003cp\u003e(1.74\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003cp\u003e(1.74\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003cp\u003e(1.39\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh-SHR and low-BRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003cp\u003e(0.96\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003cp\u003e(0.92\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh-SHR and high-BRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003cp\u003e(1.67\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003cp\u003e(1.77\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003cp\u003e(1.78\u0026ndash;2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003cp\u003e(1.34\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eTo further refine the relationship between stroke and the combined the SHR and BRI groups, the study performed subgroup analyses. The results were shown in Supplementary Table S2. The results of subgroup analysis indicated that the combined index of SHR and BRI serves as predictor of stroke within the low-risk population. This population includes individuals who have never smoked and do not suffer from hypertension, dyslipidemia, diabetes, kidney disease, liver disease, or cardiac conditions. These results were applicable to both males and females aged 45 years and older.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eSensitivity analyses were conducted to evaluate the robustness of the primary findings ( Supplementary Table S3). The relationships between SHR and BRI with stroke risk remained consistent with the principal results following the exclusion of participants with incomplete data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined 8,954 Chinese adults aged 45 and older who were followed for seven years. The incidence of a first stroke in this group was 6.78%. The results confirmed that a higher BRI index was linked to a increased risk of stroke. When participants were divided based on the median values of the BRI index and SHR, those in the higher BRI and SHR index groups showed more increased risk of stroke. Additionally, in subgroup analysis, the combination of SHR and BRI remained a predictor of stroke even in a low-risk population.\u003c/p\u003e \u003cp\u003eThe BRI, a newer body measurement index, can more accurately reflect visceral fat[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. BRI models the human body as an ellipse with height as the major axis and waist circumference as the minor axis, and calculates the eccentricity of the ellipse. Peng et al. found that for every unit increase in BRI, the risk of stroke in middle-aged and older Chinese people increased by 15.8%[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Cross-sectional data from American adults also confirmed that BRI was positively associated with stroke, with every unit increase in BRI increasing the incidence of stroke by 5.7%[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Yao et al. observed that compared to British residents, rural Chinese residents had higher BRI levels, which made them more susceptible to stroke due to multiple diseases[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Visceral adipose tissue has a significant causal relationship with ischemic stroke, a notable causal effect on cardioembolic stroke, and a potential causal impact on small vessel stroke and large artery atherosclerotic stroke[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. There are multiple mechanisms linking BRI and stroke. Firstly, visceral adipose tissue may affect insulin resistance, inflammatory immune response, and coagulation by secreting proinflammatory mediators like adiponectin, C-reactive protein, interleukin-6, and tumor necrosis factor-α, leading to endothelial dysfunction and atherosclerosis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, adipose factors can generate reactive oxygen species. Excessive reactive oxygen species and antioxidant depletion can cause oxidative stress, damage the blood-brain barrier, and ultimately result in brain injury[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, visceral fat can also affect brain tissue through the \u0026ldquo;gut-microbiota-brain axis\u0026rdquo;[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Monitoring changes in BRI can help promptly identify trends in visceral obesity, allowing for timely intervention measures, which aids in understanding the impact of obesity on stroke.\u003c/p\u003e \u003cp\u003eThe SHR serves as an indicator of immediate fluctuations and is adjusted based on the average estimated blood glucose level over the previous three months, thereby reflecting the relative change from baseline during stress[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Roberts et al. demonstrated that SHR superior prognostic insights compared to the glycemic gap and glucose measurements in evaluating the outcomes of ischemic strokes[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, studies indicated that elevated stress hyperglycemia correlates with an increased risk of stroke-related pneumonia and was associated with heightened mortality in individuals experiencing acute ischemic strokes[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]and heightened mortality in individuals experiencing acute ischemic strokes[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, previous studies have reported that stroke patients with combined stress hyperglycemia had a worse prognosis[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, SHR may be used as both a predictor of stroke risk and an important predictor of poor long-term prognosis in stroke patients[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], thereby assisting risk assessment and clinical decision-making.\u003c/p\u003e \u003cp\u003eSeveral factors may influence the relationship between SHR and stroke. Firstly, hyperglycemia can stimulate the production of inflammatory cytokines, oxidative stress, and other active substances[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This exacerbated inflammatory response may lead to neuroinflammation and the release of neurotoxins and vasoconstrictor factors, which can further compromise endothelial cell integrity and diminish vascular repair and protection. As a result, the risk of intracranial hemorrhage, potentially induced by vascular treatments, may be heightened. Additionally, the accumulation of lactic acid as a consequence of anaerobic glucose metabolism results in intracellular acidosis, which may exacerbate ischemic brain injury by intensifying the effects of lipid and free radical peroxidation[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Thus, SHR is implicated in the aggravation of ischemic brain damage and the deterioration of neurological deficits through multiple pathways.\u003c/p\u003e \u003cp\u003eThis study represented, to the best of our knowledge, the first instance of combining SHR and BRI for research. Existing evidence indicates that the combination of SHR or BRI with other biomarkers enhances the capability to effectively assess disease risk[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Nonetheless, research on the combination of SHR and BRI for stroke risk was still limited. A study conducted by Yao et al. focusing on rural residents in Northeast China, revealed an elevated triglyceride glucose (TyG)-BRI which correlated with a 15% increase in the risk of ischemic stroke[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, findings from Wang et al. established that concurrent higher values of BRI and TyG were associated with a 78% increase in stroke risk[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The combined index analysis of this study provided more comprehensive information, including stress blood sugar levels and body obesity. Moreover, this analysis facilitated the identification of distinctions among multiple indicators, thereby allowing for earlier predictions of stroke. In subgroup analysis, the combination of SHR and BRI serve as a predictor of stroke risk in low-risk populations (without hypertension, dyslipidemia, diabetes, kidney disease, liver disease, or heart problems). Both SHR and BRI were noninvasive markers that can be easily obtained in clinical settings. Incorporating these indices into clinical practice may present an innovative and practical approach for the screening and intervention of individuals at heightened risk for stroke.\u003c/p\u003e \u003cp\u003e Our study participants came from different urban and rural areas across the country, rendering the sample more representative and significantly enhancing the generalizability of the study findings. However, several limitations must be acknowledged. Firstly, the exclusion of individuals with abnormal or missing values for exposure factors may have introduced a degree of selection bias. Secondly, CHARLS is a database that collects health status information through questionnaires, so the diagnosis of stroke based solely on participant disclosures. However, previous validation supports the reliability of these self-reported stroke events, thereby enhancing their credibility[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Thirdly, the CHARLS questionnaire does not classify stroke subtypes, which represents a limitation of this study. Moreover, this study did not account for temporal variations in the BRI and SHR indicators. Future research should incorporate the dynamic effects of these indicators over time to provide a more comprehensive assessment of their interrelation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that elevated BRI and SHR were significantly associated with an increased risk of stroke. The synergistic evaluation of BRI and SHR may enable the earlier detection of stroke indicators within the general population, which supports the role of stress, blood glucose, and visceral fat in identifying and screening individuals at high risk of stroke.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot appliable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e \u003cp\u003eAL and SQ performed data analysis and wrote the initial draft of the article. HZ, HS,KH, LZ, LW and CG contributed to the critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. QG provided resources and inspections. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo Funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAL and SQ performed data analysis and wrote the initial draft of the article. HZ, HS,KH, LZ, LW and CG contributed to the critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. QG provided resources and inspections. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eSpecial thanks are due to Dr. Qiang Gao for their invaluable guidance and expertise throughout the study. Our gratitude extends to the participants of the study, without whom this research would not have been possible. We also thank the reviewers for their constructive comments and suggestions, which have helped to improve the manuscript.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials:\u003c/h2\u003e \u003cp\u003eThe datasets generated during this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHe C, Breitner S, Zhang S, Huber V, Naumann M, Traidl-Hoffmann C, Hammel G, Peters A, Ertl M, Schneider A. Nocturnal heat exposure and stroke risk. Eur Heart J. 2024;45(24):2158\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal. regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. 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J Nutr Health Aging. 2023;27(11):953\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Y, Zhou Z, Zhao X, Ye X, Qin K, Liu J, Zhang X, Ji Y. Inflammatory burden index (IBI) and body roundness index (BRI) in gallstone risk prediction: insights from NHANES 2017\u0026ndash;2020. Lipids Health Dis. 2025;24(1):63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan X, Liu T, Wu L, Zou ZY, Li C. Validity of self-reported diabetes among middle-aged and older Chinese adults: the China Health and Retirement Longitudinal Study. BMJ open. 2015;5(4):e006633.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoe S, Lee J, Lee J, Kang D, Lee JK, Shin A. Validity of Self-reported Stroke and Myocardial Infarction in Korea: The Health Examinees (HEXA) Study. J Prev Med public health = Yebang Uihakhoe chi. 2019;52(6):377\u0026ndash;83.\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-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stress Hyperglycemia Ratio, Body roundness index, stroke, China Health and Retirement Longitudinal Study, Joint indicator","lastPublishedDoi":"10.21203/rs.3.rs-9167358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9167358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWhile both the Stress Hyperglycemia Ratio (SHR) and Body Roundness Index (BRI) are recognized risk factors for stroke, their combined prognostic value remains unstudied. This research investigated the individual and joint associations of SHR and BRI with stroke incidence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective cohort analysis was conducted among 8,954 stroke-free participants aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years from the China Health and Retirement Longitudinal Study (CHARLS). Participants were stratified by SHR and BRI medians. Cox proportional hazards models assessed the association with incident stroke for SHR alone, BRI alone, and their combination. Subgroup analyses evaluated predictive performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to the low SHR/low BRI group, adjusted hazard ratios (aHRs) were: high BRI alone, 1.60 (95% CI: 1.34\u0026ndash;1.92); high SHR alone, 1.05 (95% CI: 0.89\u0026ndash;1.23); and high SHR/high BRI combined, 1.73 (95% CI: 1.34\u0026ndash;2.22). The combined SHR-BRI index significantly enhanced stroke prediction versus either biomarker alone. Subgroup analysis indicated the combined index is particularly predictive within low-risk subgroups\u0026mdash;individuals without hypertension, dyslipidemia, diabetes, kidney/liver/cardiac disease, or smoking history\u0026mdash;in both sexes\u0026thinsp;\u0026ge;\u0026thinsp;45 years.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe novel SHR-BRI combined index exhibits a significant association with stroke incidence in low-risk older adults. It represents a promising tool for early stroke risk prediction and prevention strategies.\u003c/p\u003e","manuscriptTitle":"Joint association of stress hyperglycemia ratio and body roundness index with stroke incidence: a nationwide cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 00:41:28","doi":"10.21203/rs.3.rs-9167358/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-28T14:53:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T10:21:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210842192419183181792720325056200191654","date":"2026-04-23T05:53:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68900297480461446868298983171901923258","date":"2026-04-15T18:06:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T10:41:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T08:19:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-21T11:09:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-21T11:08:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-03-19T08:56:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a7e8c3f-1d3e-4d4f-92eb-38a66f4e8145","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T00:41:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 00:41:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9167358","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9167358","identity":"rs-9167358","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}