Triglyceride-Glucose and Body Shape Index (TyG-ABSI): A Novel Dual Biomarker for Predicting Stroke Risk in Middle-Aged and Older Chinese Adults—A Nationwide Cohort Study

Triglyceride-Glucose and Body Shape Index (TyG-ABSI): A Novel Dual Biomarker for Predicting Stroke Risk in Middle-Aged and Older Chinese Adults—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 Triglyceride-Glucose and Body Shape Index (TyG-ABSI): A Novel Dual Biomarker for Predicting Stroke Risk in Middle-Aged and Older Chinese Adults—A Nationwide Cohort Study Kai Hu, Bing Xie, Mingxiang Ding This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7699425/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background Stroke is a leading global cause of morbidity and mortality, disproportionately affecting aging populations. Metabolic syndrome, obesity, and related dysfunctions are well-established risk factors. The triglyceride-glucose (TyG) index reflects insulin resistance, while the A Body Shape Index (ABSI) indicates abdominal adiposity. Their combination, TyG-ABSI, integrates metabolic and phenotypic risk for precision stratification, yet its prospective validation for stroke prediction in middle-aged and older adults remains underexplored. Objectives This study examined the association between TyG-ABSI and incident stroke risk in middle-aged and older Chinese adults, evaluating its utility as a novel predictive biomarker. Methods This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) conducted between 2011 and 2018. A cohort of 7,358 participants with no prior history of stroke was selected through a rigorous screening process that excluded individuals based on specific criteria such as age, incomplete TyG-ABSI data, and missing stroke information. The predictive capacity of TyG-ABSI for incident stroke was evaluated using multivariable Cox proportional hazards regression models. Three models were constructed: an unadjusted model, a model adjusted for age and sex, and a fully adjusted model that included potential confounders such as education level, place of residence, marital status, smoking status, alcohol consumption, hypertension, diabetes mellitus, heart disease, dyslipidemia, and BMI. Additionally, restricted cubic spline (RCS) analysis was performed to explore the potential non-linear relationship between TyG-ABSI and incident stroke. Receiver operating characteristic (ROC) curve analysis was conducted to assess the predictive performance of TyG-ABSI for incident stroke across different adjustment models. Results Elevated TyG-ABSI correlated with increased stroke risk in a dose-dependent manner (highest vs. lowest quartile: HR = 1.57, 95% CI = 1.18–2.08, P = 0.0019). Urban residents showed a stronger association (OR = 1.04, 95% CI = 1.03–1.05) than rural counterparts (OR = 1.01, 95% CI = 0.98–1.03; interaction P = 0.038). The AUC in the fully adjusted model was 0.701, indicating moderate predictive accuracy. Conclusion TyG-ABSI robustly predicts incident stroke in middle-aged and older Chinese adults, with a significant dose-dependent relationship. This dual marker enhances precision risk stratification by integrating metabolic and obesity-related factors, offering clinical value for tailored prevention. Urban-rural disparities highlight the need for targeted strategies. Stroke Triglyceride-glucose index A body shape index The China Health and Retirement Longitudinal Study (CHARLS) Cohort study Figures Figure 1 Figure 2 Figure 3 Introduction Stroke is the second leading cause of death worldwide, with annual deaths estimated at 5.7–6.2 million and a high mortality rate, particularly in low- and middle-income countries[ 1 , 2 ]. The weighted prevalence of stroke among Chinese adults aged ≥ 40 years increased from 2.28% in 2013 to 2.58% in 2019, indicating a rising trend over recent years[ 3 ]. Metabolic syndrome, obesity, and related metabolic disturbances are now recognized as central drivers in the development of stroke[ 4 ]. The evidence shows that metabolic abnormalities—such as hypertension, dyslipidemia, and insulin resistance—are more critical for stroke risk than obesity alone [5–7] . In recent years, TyG-ABSI, as a metabolic-obesity dual biomarker, has garnered increasing attention for its ability to reflect both metabolic abnormalities and obesity comprehensively. The Triglyceride-Glucose Index (TyG Index) is a marker calculated based on fasting glucose and triglyceride levels to assess insulin resistance. Individuals with elevated TyG index have a significantly increased risk of developing cardiovascular diseases, such as myocardial infarction, coronary artery disease, and stroke, with the highest quartile group experiencing an approximate 36% increase in risk (HR 1.36) compared to the lowest quartile[ 8 – 10 ]. The A Body Shape Index (ABSI) is a metric based on body shape that can more accurately reflect abdominal obesity. Integrating TyG and ABSI into the TyG-ABSI index enables a more comprehensive assessment of the impact of metabolic abnormalities and obesity on health. Compared with traditional single indicators of obesity or metabolism, such as BMI and waist circumference, TyG-ABSI can more accurately capture individuals' comprehensive metabolic and body shape risk characteristics, which is conducive to the early identification of high-risk populations and guiding individualized interventions[ 11 ]. Despite the promising potential of the TyG-ABSI index in predicting cardiovascular diseases and other chronic conditions[ 12 ], research exploring its association with stroke, particularly ischemic stroke, remains remarkably limited. Currently, the majority of studies have focused on the association between metabolic syndrome and related metabolic indicators with stroke[ 13 , 14 ]. However, the role of the TyG-ABSI index, which integrates both metabolic and obesity factors, in assessing stroke risk remains underexplored. This not only limits the precise identification of high-risk stroke populations by clinicians but also affects the scientific nature and effectiveness of stroke prevention strategies formulated by public health policymakers. Given this, the present study focuses on the middle-aged and elderly population in China to thoroughly investigate the relationship between TyG-ABSI and stroke risk, aiming to fill this research gap and provide new scientific evidence for early warning and precise stroke prevention. Method 2.1 Study population Our data analysis is based on the China Health and Retirement Longitudinal Study (CHARLS), a national survey in China aimed at investigating issues related to aging. The CHARLS national baseline survey was launched in 2011 using a multistage probability-proportional-to-size sampling method. The sample included more than 17,000 individuals from over 10,000 households across 150 counties in 28 provinces. The survey is ongoing, with follow-ups conducted every 2 to 3 years. Respondents were interviewed face-to-face at their residences using computer-assisted personal interviewing. Consent to Participate All participants provided written informed consent prior to enrollment in the CHARLS study. For participants with limited cognitive capacity, informed consent was obtained from their legal guardians. The CHARLS study was initially approved by the Peking University Institutional Review Board (IRB) in 2008 (IRB00001052-1.015). The methodology of this study adheres to the Declaration of Helsinki and all relevant standards and recommendations proposed by CHARLS The survey collected comprehensive data on demographics, family transfers, health status, healthcare and insurance, employment, income, expenditure, and assets. Notably, in 2011 and 2018, venous blood samples were collected from individuals who had fasted for at least 12 hours, with no food or drink consumed. All biomedical procedures were conducted by accredited professionals following strict standards. These samples were kept at an optimal 4°C and promptly transported to the Beijing central laboratory (Youanmen Clinical Laboratory Center of Capital Medical University) for advanced diagnostic assessments. Glucose concentration, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were precisely measured using an enzymatic colorimetric method. Figure 1 provides a detailed flowchart of the participant selection process. Among the 17,705 participants who completed physical examinations and questionnaire assessments in the 2011 baseline survey, individuals were subsequently excluded based on the following specific criteria: age less than 45 years (391 individuals), incomplete TyG-ABSI data (7,745 individuals), missing stroke information in both 2011 and 2018 (1,917 individuals), and a diagnosis of stroke at baseline (58 individuals). Participants with height within the 140–200 cm range, weight within 30–200 kg, and waist circumference within 50–150 cm were selected. Ultimately, 7,358 participants were included in the study, of whom 467 experienced incident stroke after 2011, and 6,891 remained stroke-free until 2018. 2.2 Assessment of the TyG-ABSI Index TyG index = ln [triglyceride concentration (mg/dL) × fasting blood glucose concentration (mg/dL)/2]. ABSI = WC (cm)/ [BMI 2/3 × height 1/2 (m) ]. TyG-ABSI = TyG × ABSI 2.3 Assessment of Incident Stroke Incident stroke was assessed based on self-reported data. Participants were asked by the interviewer: "Have you been diagnosed with a stroke by a doctor?" Individuals who responded affirmatively were classified as having experienced a stroke. For this study, we excluded participants who reported having had a stroke in 2011 (DA007_8_). If a participant was diagnosed with stroke at any time after 2011 and up to the follow-up in 2018, they were included in our analysis as incident stroke cases. 2.4 Covariates Assessment The covariates assessment included social demographic characteristics, lifestyle behaviors, and current health status. Social demographic attributes consisted of age (in years), gender (male/female), marital status, education level (primary school and below/high school and above), and place of residence (rural/urban). Lifestyle behaviors included smoking status (never smoked/former, and current smoker) and alcohol consumption. Current health issues (yes/no) included hypertension, diabetes, heart disease, and dyslipidemia. Laboratory test results included triglycerides, glucose, body mass index (BMI), and waist circumference. Additionally, our BMI classification followed the Chinese adult standards. Underweight was characterized by a BMI below 18.5, normal weight was between 18.5 and 23.9, overweight was between 24 and 27.9, and a BMI of 28 or higher characterized obesity. 2.5 Data Analysis The data originated from the China Health and Retirement Longitudinal Study (CHARLS), which was conducted between 2011 and 2018. Our survey encompassed 7,358 participants. For continuous variables, data were represented by means (standard deviations, SD) or medians (interquartile ranges, IQR), while categorical variables were expressed as frequencies and percentages. We employed multivariable logistic regression models to examine the association between the TyG-ABSI index and incident stroke. 2.5.1 Cox Proportional Hazards Regression Models We employed three multivariable Cox proportional hazards models to examine the prospective association between baseline TyG-ABSI and time-to-incident stroke. Model 1 was an unadjusted model, which included only the TyG-ABSI index as the predictor. Model 2 was adjusted for age and sex, which are well-known confounders in stroke risk. Model 3 was a fully adjusted model, which included the following potential confounders: age, sex, educational level (categorized as primary school and below, high school, and college and higher), place of residence (rural vs. urban), marital status (married vs. non-married), smoking status (never smoked, former smoker, current smoker), alcohol consumption (none, less than once a month, more than once a month), hypertension (yes/no), diabetes mellitus (yes/no), heart disease (yes/no), dyslipidemia (yes/no), and BMI (continuous variable). These covariates were selected based on their known associations with stroke risk in the literature. The proportional hazards assumption for all Cox models was tested using Schoenfeld residuals, and no significant violations were found. The hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for each model to quantify the association between TyG-ABSI and incident stroke. The p-values for trend were calculated to assess the dose-response relationship across TyG-ABSI quartiles. 2.5.2 Restricted Cubic Spline Analysis for Non-linear Relationships To explore the potential non-linear relationship between TyG-ABSI and incident stroke, we conducted restricted cubic spline (RCS) analysis using the "rms" package in RStudio. The RCS analysis was performed with three knots placed at the 10th, 50th, and 90th percentiles of the TyG-ABSI distribution. The overall p-value and the p-value for non-linearity were calculated to determine the significance of the association and the presence of non-linear trends. The RCS analysis was conducted for the fully adjusted model (Model 3) to ensure that other variables did not confound the potential non-linear relationship. 2.5.3 Receiver Operating Characteristic (ROC) Curve Analysis To evaluate the predictive performance of TyG-ABSI for incident stroke, we performed receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC) was calculated for each model to assess the discriminative ability of TyG-ABSI. The AUC values were compared across the three models to determine the incremental predictive value of TyG-ABSI when combined with other clinical risk factors. The AUC values were interpreted as follows: 0.5 indicates no discriminative ability, 0.7 indicates moderate discriminative ability, and 0.8 or higher indicates good discriminative ability. The p-values for the differences in AUC values were calculated using the DeLong test to assess the statistical significance of the improvements in predictive performance. 2.5.4 Multicollinearity Assessment To assess the potential for multicollinearity among the covariates in our multivariable regression models, we calculated the variance inflation factor (VIF) for each predictor. A VIF value greater than 5 typically indicates significant multicollinearity. In our analysis, all VIF values were less than 5, confirming that multicollinearity was not a concern in our models. 2.5.5 Software and Significance Level Statistical analyses were conducted using RStudio version 5.0. A two-tailed p-value of less than 0.05 was considered statistically significant. Table1 Baseline characteristics of study population by stroke status at follow-up Total （ n=7358 ） Nor-stroke (n=6891) Stroke (n=467) P- value Age, （ mean ± SD ） 58.5±8.7 58.3±8.7 61.0±8.4 <0.001 Sex, n (%) 0.572 Male 3332(45.3) 3114(45.2) 218(46.7) Female 4021(54.7) 3772(54.8) 249(53.3) Marital, n (%) 0.009 Married 6574(89.3) 6174(89.6) 400(85.7) Non-Married 784(10.7) 717(10.4) 67(14.3) Education, n (%) 0.938 Junior high school and below 6619(90.0) 6201(90.0) 418(89.5) High school 529(7.2) 494(7.2) 35(7.5) College and highter 208(2.8) 194(2.8) 14(3.0) Location, n (%) <0.001 Urban 6677(90.7) 6275(91.1) 401(85.9) Rural 682(9.3) 616(8.9) 66(14.1) Smoking, n (%) 0.015 Never 4565(62.1) 4289(62.3) 276(59.1) Former smoker 579(7.9) 526(7.6) 53(11.3) Current smoker 2204(30.0) 2066(30.0) 138(29.6) Drinking, n (%) 0.986 None of these 4938(67.1) 4624(67.1) 314(67.2) Drink but less than once a month 582(7.9) 546(7.9) 36(7.7) Drink more than once a month 1838(25) 1721(25.0) 117(25.1) Hypertension, n (%) <0.001 Yes 1711(23.4) 1495(21.8) No 5609(76.6) 5360(78.2) Diabetes, n (%) <0.001 Yes 384(5.3) 48(10.4) 336(4.9) NO 6907(94.7) 415(89.6) 6492(95.1) CVD, n (%) <0.001 Yes 809(11.0) 720(10.5) 89(19.2) NO 6518(89.0) 6143(89.5) 375(80.8) Dyslipidemia, n (%) <0.001 Yes 638(8.8) 554(8.2) 84(18.3) No 6573(91.2) 6199(91.8) 374(81.7) BMI, mean ± SD 23.6±3.9 23.6±3.9 24.7±4.1 <0.001 CRP, mean ± SD 2.5±6.7 2.4±6.5 3.4±9.6 0.001 TyG-ABSI, mean ± SD 72.0±7.9 71.9±7.8 74.3±8.1 <0.001 Note CVD: Cardiovascular Disease; BMI : Body Mass Index, / Result Characteristics of Study Participants According to Incident Stroke and TyG-ABSI Quartiles Table 1 Summarizes the Demographic and Clinical Baseline Characteristics of the Study Participants. A total of 7,358 participants were included in the final cohort, among whom 467 experienced an incident stroke. Compared with the non-stroke population, stroke patients were significantly older (61.0 ± 8.4 vs. 58.3 ± 8.7 years, P < 0.001) and had higher levels of the TyG-ABSI index (74.3 ± 8.1 vs. 71.9 ± 7.8, P < 0.001), BMI (24.7 ± 4.1 vs. 23.6 ± 3.9, P < 0.001), and CRP (3.4 ± 9.6 vs. 2.4 ± 6.5 mg/L, P = 0.001). Traditional cardiovascular risk factors were significantly more prevalent in the stroke group, including hypertension (23.4% in stroke group vs. 21.8% in non-stroke group), diabetes (10.4% vs. 4.9%), history of cardiovascular disease (CVD) (19.2% vs. 10.5%), and dyslipidemia (18.3% vs. 8.2%) (P < 0.001). Notably, the risk of stroke was significantly higher among rural residents (hazard ratio [HR] = 1.71, 95% confidence interval [CI] 1.30–2.24) and unmarried/divorced individuals (HR = 1.43, 95% CI 1.09–1.87). With increasing TyG-ABSI quartiles (Q1–Q4), there was a dose-dependent increase in age (56.1 to 60.6 years), BMI (22.9 to 24.3 kg/m²), and prevalence of chronic diseases (diabetes 2.5% to 10.7%, hypertension 15.0% to 32.0%, CVD 9.6% to 13.4%, dyslipidemia 5.9% to 13.4%, all P < 0.001). In the highest TyG-ABSI quartile (Q4), the proportion of women increased significantly (63.6% vs. 46.9% in Q1), the proportion of rural residents rose (11.1% vs. 7.8% in Q1, P = 0.004), and the incidence of stroke doubled (8.9% vs. 4.1% in Q1, P < 0.001). Metabolic risk factors exhibited sex-specific patterns, with a higher smoking rate among men in the lowest TyG-ABSI quartile (Q1 34.6% vs. Q4 25.4%) and higher levels of education (junior high school and below 91.7% vs. 87.5% in Q1) and unmarried status (12.8% vs. 9.8% in Q1) in the highest TyG-ABSI quartile, forming a cluster of social determinants of risk. This gradient change confirms that TyG-ABSI integrates multidimensional risks of metabolism, demographics, and behavior, and its value as a composite biomarker for stroke risk stratification is of significant clinical importance (Table 2). The Relationship Between TyG-ABSI and Incident Stroke Table 3 presents the results of the multiple regression analysis. The findings indicate a dose-response relationship between the TyG-ABSI index and stroke risk: in the unadjusted model (Model 1), the highest quartile group had a hazard ratio (HR) of 2.24 (95% CI 1.71–2.95, P < 0.0001). After adjustment for age and sex (Model 2), the HR attenuated to 2.02 (95% CI 1.53–2.67, P < 0.0001). In the fully adjusted model (Model 3), which included 14 potential confounders, the HR remained significantly elevated at 1.57 (95% CI 1.18–2.08, P = 0.0019). The P-value for trend was 0.023 in the unadjusted model, 0.018 in the age- and sex-adjusted model, and 0.009 in the fully adjusted model, highlighting the robustness and independent predictive value of the TyG-ABSI index across different models. In Figure 2, we employed restricted cubic splines (RCS) to examine the relationship between TyG-ABSI and incident stroke. Panels A, B, and C all demonstrate a significant overall association between TyG-ABSI and stroke (P-overall all 0.05), suggesting a potential linear relationship between TyG-ABSI and stroke risk. As TyG-ABSI increases, the odds ratio (OR) and its 95% confidence interval (CI) both show an upward trend, indicating that higher TyG-ABSI values may be associated with an increased risk of stroke occurrence. This trend is consistent across different models and subgroup analyses, suggesting that TyG-ABSI may be a potential indicator for assessing the relevant risk. Table 2 Baseline characteristic of the study population according to TyG-ABSI quartiles Characteristic Overall N = 7,358 Q1 N = 1,840 Q2 N = 1,839 Q3 N = 1,839 Q4 N = 1,840 p-value 1 age, Mean±(SD) 58.48±8.73 56.14±8.21 58.09±8.46 59.09±8.62 60.61±9.02 <0·0001 gender, n (%) <0·0001 Female 4,021 (54.69%) 863 (46.93%) 940 (51.14%) 1,048 (57.05%) 1,170 (63.62%) Male 3,332 (45.31%) 976 (53.07%) 898 (48.86%) 789 (42.95%) 669 (36.38%) Unknown 5 1 1 2 1 education, n (%) <0·0001 College and higher 208 (2.83%) 63 (3.42%) 49 (2.66%) 38 (2.07%) 58 (3.15%) High school 529 (7.19%) 168 (9.13%) 149 (8.10%) 118 (6.42%) 94 (5.11%) Junior high school and below 6,619 (89.98%) 1,609 (87.45%) 1,641 (89.23%) 1,681 (91.51%) 1,688 (91.74%) Unknown 2 0 0 2 0 marital, n (%) 0·006 married 6,574 (89.34%) 1,659 (90.16%) 1,646 (89.51%) 1,664 (90.48%) 1,605 (87.23%) single 784 (10.66%) 181 (9.84%) 193 (10.49%) 175 (9.52%) 235 (12.77%) residence, n (%) 0·004 rural 682 (9.27%) 143 (7.77%) 177 (9.62%) 158 (8.59%) 204 (11.09%) urban 6,676 (90.73%) 1,697 (92.23%) 1,662 (90.38%) 1,681 (91.41%) 1,636 (88.91%) smoking, n (%) <0·0001 Current 2,204 (29.99%) 637 (34.64%) 600 (32.64%) 501 (27.27%) 466 (25.41%) Former smoker 579 (7.88%) 141 (7.67%) 142 (7.73%) 142 (7.73%) 154 (8.40%) Never 4,565 (62.13%) 1,061 (57.69%) 1,096 (59.63%) 1,194 (65.00%) 1,214 (66.19%) Unknown 10 1 1 2 6 drinking, n (%) <0·0001 Drink but less than a month 582 (7.91%) 179 (9.73%) 152 (8.27%) 126 (6.85%) 125 (6.79%) Drink more than a month 1,838 (24.98%) 530 (28.80%) 469 (25.50%) 420 (22.84%) 419 (22.77%) None of these 4,938 (67.11%) 1,131 (61.47%) 1,218 (66.23%) 1,293 (70.31%) 1,296 (70.43%) Diabetes, n (%) 384 (5.27%) 45 (2.46%) 50 (2.74%) 95 (5.21%) 194 (10.69%) <0·0001 Unknown 67 10 16 15 26 Hypertension, n (%) 1,711 (23.37%) 274 (14.98%) 367 (20.08%) 485 (26.46%) 585 (31.97%) <0·0001 Unknown 38 11 11 6 10 CVD, n (%) 809 (11.04%) 175 (9.55%) 182 (9.94%) 206 (11.26%) 246 (13.41%) <0·0001 Unknown 31 7 8 10 6 Dyslipidemia, n (%) 638 (8.85%) 106 (5.87%) 125 (6.93%) 165 (9.20%) 242 (13.38%) <0·0001 Unknown 147 34 35 46 32 BMI, Mean±(SD) 23.63±3.90 22.92±4.37 23.31±3.52 23.97±3.66 24.32±3.86 <0·0001 Stroke2018 <0·0001 Yes 467(6.4) 75(4.1) 109(5.9) 119(6.5) 164(8.9) No 6891(93.7) 1765(96.9) 1730(94.1) 1720(93.5) 1676(91.1) Table3 Prospective associations between baseline TyG-ABSI with follow-up incident stroke in Charls Models TyG-ABSI （ As Quartiles ） Q1(Reference) Q2 HR(95%CI) p Q3 HR(95%CI) p Q4 HR(95%CI) p p for trend Model 1 Reference 1.47(1.09-1.97)0.011 1.61(1.21-2.15)0.001 2.24(1.71-2.95) <0.0001 0.023 Model 2 Reference 1.4(1.04-1.88)0.025 1.50(1.12-2.05)0.007 2.02(1.53-2.67) <0.0001 0.018 Model 3 Reference 1.33(0.99-1.88)0.056 1.31(0.98-1.76)0.069 1.57(1.18-2.08) 0.0019 0.009 Note Model 1: Crude model (unadjusted for covariates).Model 2: Adjusted for age and sex. Model 3: Adjusted for age, sex, educational level, residence, marital status, smoking, alcohol consumption, hypertension, diabetes mellitus, heart disease, dyslipidemia, and BMI. Results of ROC Curve Analysis Figure 3 illustrates the predictive performance of the TyG-ABSI index for incident stroke across different adjustment models. In the base model (Model 1) without adjustment for covariates, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.590, indicating limited discriminative ability when TyG-ABSI was used alone. After adjustment for age and sex (Model 2), the AUC increased significantly to 0.622 (P<0.05), reflecting improved predictive performance. Most notably, in the fully adjusted model (Model 3), which included education level, place of residence, marital status, behavioral factors, and metabolic diseases, the AUC reached 0.701, demonstrating moderate-to-good predictive accuracy. This progressive increase in AUC (from 0.590 to 0.701) was statistically significant (P<0.01), confirming that TyG-ABSI, in combination with clinical risk factors, significantly enhances the prediction of incident stroke. The AUC in Model 3 exceeded the clinically meaningful threshold of 0.7, suggesting that this composite indicator may have practical clinical application value. Collectively, these results indicate that TyG-ABSI, as a dual marker of metabolism and obesity, provides important incremental information for stroke risk assessment when combined with traditional risk factors. In addition to the ROC curve analysis, we assessed the multicollinearity among the covariates in our regression models by calculating the variance inflation factor (VIF). All VIF values were less than 5, indicating that multicollinearity was not a significant issue in our models. This further supports the robustness of our findings. Stratified analyses Stratified analyses revealed that the association between TyG-ABSI and stroke risk was consistent across various subgroups(Fig.4). Specifically, no significant interaction was observed between age and the association of TyG-ABSI with stroke risk (HR = 1.04, 95% CI 1.02–1.06 for those <60 years old; HR = 1.02, 95% CI 1.01–1.04 for those ≥60 years old; P = 0.128 for interaction). Similarly, no significant interaction was detected between sex and the association of TyG-ABSI with stroke risk (HR = 1.04, 95% CI 1.02–1.05 for females; HR = 1.03, 95% CI 1.02–1.05 for males; P = 0.717 for interaction). Stratification by education level, marital status, smoking status, and alcohol consumption frequency also showed no significant interactions with TyG-ABSI and stroke risk (P > 0.05 for all). However, a significant interaction was found for place of residence (P = 0.038), with a stronger association between TyG-ABSI and stroke risk in urban residents (OR = 1.04, 95% CI 1.03–1.05) than in rural residents (OR = 1.01, 95% CI 0.98–1.03). Additionally, no significant interactions were observed in stratified analyses by BMI categories (underweight, normal weight, overweight, and obese), diabetes status (with and without diabetes), hypertension status (with and without hypertension), cardiovascular disease status (with and without CVD), and dyslipidemia status (with and without dyslipidemia) (P > 0.05 for all). These findings suggest that TyG-ABSI is a robust predictor of stroke risk across different demographic, lifestyle, metabolic, and disease subgroups, except for a stronger association in urban residents. Discussion Our study leverages a large-scale cohort from the China Health and Retirement Longitudinal Study (CHARLS) to elucidate the significant correlation between the Triglyceride-Glucose and A Body Shape Index (TyG-ABSI) and stroke risk. TyG-ABSI, as a biomarker integrating metabolic and obesity factors, has demonstrated substantial potential in stroke risk prediction[12, 15]. This finding aligns with recent studies that highlight the pivotal role of metabolic factors in stroke risk. For instance, Liu et al. identified a close correlation between the variability of metabolic syndrome parameters and stroke incidence in hypertensive patients within a community-based cohort study[13]. Similarly, Moghadam-Ahmadi et al. confirmed a significant association between metabolic syndrome and stroke, further substantiating the critical role of metabolic anomalies in stroke pathogenesis[14]. Our study innovatively integrates metabolic and obesity factors into TyG-ABSI, providing a new tool for a more comprehensive stroke risk evaluation. 4.1 Biological Mechanisms Underlying TyG-ABSI The components of TyG-ABSI—TyG index and ABSI—reflect insulin resistance and abdominal obesity levels, respectively. The TyG index, calculated based on fasting triglycerides and blood glucose, is a widely accepted surrogate indicator of insulin resistance and can effectively identify high-risk individuals[16]. ABSI, which is based on waist circumference, height, and weight, more accurately measures abdominal fat distribution and visceral obesity compared to traditional indicators like BMI, and has a stronger predictive power for cardiovascular and metabolic diseases[17-19]. 4.1.1 Insulin Resistance and Metabolic Abnormalities Insulin resistance not only causes abnormalities in blood glucose and lipids but also accelerates atherosclerosis and vascular damage through a variety of mechanisms. The core mechanisms include oxidative stress, chronic inflammation, and endothelial dysfunction. Insulin resistance increases the production of reactive oxygen species (ROS) and reduces the bioavailability of nitric oxide (NO), impairing endothelial cell function and promoting vasoconstriction and thrombosis[20, 21]. Meanwhile, chronic low-grade inflammation often accompanies insulin resistance, which activates the NLRP3 inflammasome, further exacerbating endothelial injury and inflammatory responses[22, 23]. Moreover, selective impairment of the insulin signaling pathway leads to decreased NO production and increased secretion of endothelin-1 (ET-1), resulting in vasoconstriction and stiffness and promoting the progression of atherosclerosis[24]. 4.1.2 Abdominal Obesity and Cardiovascular Risk Abdominal obesity is an important component of metabolic syndrome and is closely related to the risk of cardiovascular disease[25]. ABSI provides a stronger predictive ability than traditional indicators (such as BMI) by more accurately measuring abdominal fat distribution and visceral obesity[26]. Visceral fat tissue not only stores fat but also secretes various bioactive substances, such as cytokines and adipokines, which can affect the overall metabolic state and increase the risk of cardiovascular disease[20, 27]. 4.2 Dose-Response Relationship and Predictive Power Our comprehensive analysis reveals a dose-response relationship between TyG-ABSI and stroke risk across different quartile groups. The highest quartile group showed a significantly elevated hazard ratio (HR = 1.57, 95% CI 1.18–2.08, P = 0.0019) even after adjusting for multiple potential confounders. As TyG-ABSI levels rise, so does stroke risk, reinforcing TyG-ABSI's effectiveness as an independent predictive indicator and its role in stroke risk stratification. Further restricted cubic spline (RCS) analysis reveals a significant linear relationship between TyG-ABSI and stroke risk (P-overall 0.05), indicating that stroke risk increases linearly with rising TyG-ABSI levels. This linear relationship is consistent across different models and subgroup analyses, supporting TyG-ABSI's robustness as a stroke risk predictor. 4.3 Predictive Performance of TyG-ABSI Receiver operating characteristic (ROC) curve analysis further validates TyG-ABSI's predictive performance. In the unadjusted model, TyG-ABSI's area under the curve (AUC) was 0.590, indicating limited predictive power. However, as covariates were progressively added to the model, the AUC significantly increased, reaching 0.701 in the fully adjusted model (P < 0.01). Combining TyG-ABSI with other clinical risk factors significantly enhances stroke risk prediction, highlighting its importance in comprehensive risk assessment. Notably, the AUC in the fully adjusted model exceeded 0.7, suggesting TyG-ABSI's potential clinical utility. 4.4 Urban-Rural Disparities in Stroke Risk In our study, we observed a significant interaction between place of residence and the association between TyG-ABSI and stroke risk, with a stronger association found in urban residents than rural residents (P = 0.038). Urban residents had an hazard ratio (HR) of 1.04 (95% CI 1.03–1.05), while rural residents had an HR of 1.01 (95% CI 0.98–1.03). This discrepancy may stem from differences in lifestyle, dietary habits, and access to medical resources between urban and rural residents[28]. 4.4.1 Biological Factors Urban residents often face higher levels of stress, which can exacerbate metabolic anomalies such as insulin resistance and hypertension[29, 30]. Chronic stress can lead to increased levels of cortisol[30], a hormone that promotes fat storage and insulin resistance, thereby increasing the risk of metabolic syndrome and stroke[31]. Additionally, urban lifestyles are characterized by higher consumption of processed foods and sugary beverages, which can increase triglyceride and glucose levels, further enhancing the TyG-ABSI score. These dietary habits, combined with reduced physical activity due to sedentary jobs[33] and limited recreational spaces, create a conducive environment for the development of metabolic disorders that increase stroke risk[32-34]. 4.4.2 Sociological Factors Access to healthcare is another critical factor that may explain the observed differences. Urban areas generally have better access to medical resources, including diagnostic facilities and specialized stroke care units. This means that urban residents with high TyG-ABSI scores are more likely to receive early diagnosis and intervention, which could mitigate some of the risk factors. However, this increased awareness and diagnosis might also lead to a higher reported incidence of stroke in urban areas. On the other hand, rural residents may face barriers to accessing timely and adequate medical care, which could result in underdiagnosis and undertreatment of stroke risk factors. 4.4.3 Economic Factors Socioeconomic status (SES) also plays a significant role in stroke risk. Urban residents typically have higher SES, which can influence lifestyle choices and access to healthcare. Higher SES is often associated with better education, leading to greater awareness of health risks and the importance of preventive measures. Conversely, lower SES in rural areas may limit access to nutritious food, physical activity opportunities, and healthcare services, thereby exacerbating the risk of metabolic disorders and stroke. 4.5 Future Research Directions Future studies should disentangle these complex interactions by incorporating detailed assessments of lifestyle factors, stress levels, and socioeconomic status. Longitudinal studies that track changes in these variables over time could provide deeper insights into the mechanisms underlying the observed differences. Additionally, interventions targeting urban populations should focus on stress reduction and healthy lifestyle promotion[34], while efforts in rural areas should prioritize improving access to healthcare and addressing socioeconomic disparities. 4.6 Limitations of the Study Despite our study's revelations about TyG-ABSI's potential in stroke risk prediction based on the extensive data from the CHARLS database, certain limitations persist. Firstly, stroke event assessment in the CHARLS database relies on participant self-reports, which may introduce subjectivity and recall bias. Secondly, although the CHARLS database encompasses a wealth of sociodemographic and health-related data, it falls short in detailed records of lifestyle factors such as dietary habits and physical activity levels. Additionally, the CHARLS database's focus on middle-aged and older Chinese individuals may restrict the extrapolation of our findings to other age groups and ethnic backgrounds. Future studies could consider corroborating stroke events with more objective medical records or imaging results to mitigate subjectivity bias. Simultaneously, refining the CHARLS database's records on lifestyle factors or conducting similar studies in cohorts with more comprehensive lifestyle data would aid in a more holistic evaluation of the relationship between TyG-ABSI and stroke risk. Moreover, validation studies in diverse ethnic and geographical populations would help assess TyG-ABSI's applicability in broader populations. Conclusion In summary, this large-cohort study demonstrates that the TyG-ABSI index potently predicts stroke risk in middle-aged and older Chinese adults, showing a stable dose-response relationship across multiple models. By integrating metabolic and obesity factors, TyG-ABSI enables more accurate risk assessment. While the study has a large sample size and sufficient follow-up, it also has limitations like self-reporting bias and incomplete records of lifestyle factors. TyG-ABSI helps clinicians identify high-risk individuals and highlights urban-rural differences, indicating the need for tailored prevention strategies. It has significant implications for clinical practice and public health policy-making. Future validation in diverse populations will further enhance its value in stroke prevention and management. Declarations Acknowledgements We want to extend our sincere thanks to the China Health and Retirement Longitudinal Study (CHARLS) for making the summary data publicly available. Author Contributions K.H. and B.X. conceived and designed the study. K.H. performed the data analysis, wrote the initial draft of the manuscript, prepared all figures (Fig. 1–4) and tables (Table 1–3). B.X. assisted with data collection and validation. M.D. revised the manuscript and provided critical feedback. All authors contributed to the final version of the manuscript and approved the submitted version. Conflict of Interest The authors declare that they have no competing financial interests. Funding No funding. Data availability The datasets generated and/or analyzed during the current study are available in the China Health and Retirement Longitudinal Study (CHARLS) repository. The data used to support this study's findings are available from the corresponding author upon reasonable request. 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Zhou, Y., et al., Increased Stroke Risk in Metabolically Abnormal Normal Weight: a 10-Year Follow-up of 102,037 Participants in China. Transl Stroke Res, 2021. 12 (5): p. 725-734. Ishida, A., et al., Association of Obesity and Metabolic Health Status with Cerebral Small-Vessel Disease in Stroke-Free Individuals. J Atheroscler Thromb, 2025. Moon, J.H., et al., Triglyceride-Glucose Index Predicts Future Atherosclerotic Cardiovascular Diseases: A 16-Year Follow-up in a Prospective, Community-Dwelling Cohort Study. Endocrinol Metab (Seoul), 2023. 38 (4): p. 406-417. Hong, S., K. Han, and C.Y. Park, The triglyceride glucose index is a simple and low-cost marker associated with atherosclerotic cardiovascular disease: a population-based study. BMC Med, 2020. 18 (1): p. 361. Park, H.M., et al., Effectiveness of the triglyceride-glucose index and triglyceride-glucose-related indices in predicting cardiovascular disease in middle-aged and older adults: A prospective cohort study. 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Kawachi, The multiple roles of life stress in metabolic disorders. Nat Rev Endocrinol, 2023. 19 (1): p. 10-27. Lee, S.H., S.Y. Park, and C.S. Choi, Insulin Resistance: From Mechanisms to Therapeutic Strategies. Diabetes Metab J, 2022. 46 (1): p. 15-37. Hooker, S.P., et al., Association of Accelerometer-Measured Sedentary Time and Physical Activity With Risk of Stroke Among US Adults. JAMA Netw Open, 2022. 5 (6): p. e2215385. Wang, Z., et al., Sedentary behavior and the risk of stroke: A systematic review and dose-response meta-analysis. Nutr Metab Cardiovasc Dis, 2022. 32 (12): p. 2705-2713. Bai, L., et al., Association of physical activity, sedentary behavior and stroke in older adults. Front Public Health, 2024. 12 : p. 1484765. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviews received at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 15 Dec, 2025 Reviewers invited by journal 27 Oct, 2025 Editor invited by journal 10 Oct, 2025 Editor assigned by journal 24 Sep, 2025 Submission checks completed at journal 24 Sep, 2025 First submitted to journal 24 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":113765,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for the selection of participants in the cohort study from Charls from 2011 to 2018 (n = 7358)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7699425/v1/4ebf6fcb7ead58d1b2f536d0.jpg"},{"id":95285685,"identity":"c881487f-aa97-475a-94e8-e8850b758ee9","added_by":"auto","created_at":"2025-11-06 09:57:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52440,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of the relationship between TyG-ABSI and stroke.\u003c/p\u003e\n\u003cp\u003eNote A: Unadjusted for covariates. B: Adjusted for age and sex. C: Adjusted for age, sex, educational level, residence, marital status, smoking, alcohol consumption, hypertension, diabetes mellitus, heart disease, dyslipidemia, and BMI.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7699425/v1/2054a061bfa0c635955207fb.jpg"},{"id":95285686,"identity":"b78bd1c9-aef5-4bb0-9bc1-38b0cf5e939f","added_by":"auto","created_at":"2025-11-06 09:57:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60644,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis evaluated the predictive performance of TyG-ABSI under three nested adjustment models.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7699425/v1/d29b56ea27dd82e4034dccbf.jpg"},{"id":95523949,"identity":"b71c8963-1554-47f6-acc9-6c1fa5e05dbd","added_by":"auto","created_at":"2025-11-10 10:01:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1713150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7699425/v1/adf9b025-3801-4c31-99a8-90a80d07163b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Triglyceride-Glucose and Body Shape Index (TyG-ABSI): A Novel Dual Biomarker for Predicting Stroke Risk in Middle-Aged and Older Chinese Adults—A Nationwide Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is the second leading cause of death worldwide, with annual deaths estimated at 5.7\u0026ndash;6.2\u0026nbsp;million and a high mortality rate, particularly in low- and middle-income countries[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The weighted prevalence of stroke among Chinese adults aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years increased from 2.28% in 2013 to 2.58% in 2019, indicating a rising trend over recent years[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Metabolic syndrome, obesity, and related metabolic disturbances are now recognized as central drivers in the development of stroke[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The evidence shows that metabolic abnormalities\u0026mdash;such as hypertension, dyslipidemia, and insulin resistance\u0026mdash;are more critical for stroke risk than obesity alone\u003csup\u003e[5\u0026ndash;7]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn recent years, TyG-ABSI, as a metabolic-obesity dual biomarker, has garnered increasing attention for its ability to reflect both metabolic abnormalities and obesity comprehensively. The Triglyceride-Glucose Index (TyG Index) is a marker calculated based on fasting glucose and triglyceride levels to assess insulin resistance. Individuals with elevated TyG index have a significantly increased risk of developing cardiovascular diseases, such as myocardial infarction, coronary artery disease, and stroke, with the highest quartile group experiencing an approximate 36% increase in risk (HR 1.36) compared to the lowest quartile[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The A Body Shape Index (ABSI) is a metric based on body shape that can more accurately reflect abdominal obesity. Integrating TyG and ABSI into the TyG-ABSI index enables a more comprehensive assessment of the impact of metabolic abnormalities and obesity on health. Compared with traditional single indicators of obesity or metabolism, such as BMI and waist circumference, TyG-ABSI can more accurately capture individuals' comprehensive metabolic and body shape risk characteristics, which is conducive to the early identification of high-risk populations and guiding individualized interventions[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the promising potential of the TyG-ABSI index in predicting cardiovascular diseases and other chronic conditions[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], research exploring its association with stroke, particularly ischemic stroke, remains remarkably limited. Currently, the majority of studies have focused on the association between metabolic syndrome and related metabolic indicators with stroke[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the role of the TyG-ABSI index, which integrates both metabolic and obesity factors, in assessing stroke risk remains underexplored. This not only limits the precise identification of high-risk stroke populations by clinicians but also affects the scientific nature and effectiveness of stroke prevention strategies formulated by public health policymakers. Given this, the present study focuses on the middle-aged and elderly population in China to thoroughly investigate the relationship between TyG-ABSI and stroke risk, aiming to fill this research gap and provide new scientific evidence for early warning and precise stroke prevention.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur data analysis is based on the China Health and Retirement Longitudinal Study (CHARLS), a national survey in China aimed at investigating issues related to aging. The CHARLS national baseline survey was launched in 2011 using a multistage probability-proportional-to-size sampling method. The sample included more than 17,000 individuals from over 10,000 households across 150 counties in 28 provinces. The survey is ongoing, with follow-ups conducted every 2 to 3 years. Respondents were interviewed face-to-face at their residences using computer-assisted personal interviewing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent prior to enrollment in the CHARLS study. For participants with limited cognitive capacity, informed consent was obtained from their legal guardians.\u003c/p\u003e\n\u003cp\u003eThe CHARLS study was initially approved by the Peking University Institutional Review Board (IRB) in 2008 (IRB00001052-1.015). The methodology of this study adheres to the Declaration of Helsinki and all relevant standards and recommendations proposed by CHARLS\u003c/p\u003e\n\u003cp\u003eThe survey collected comprehensive data on demographics, family transfers, health status, healthcare and insurance, employment, income, expenditure, and assets. Notably, in 2011 and 2018, venous blood samples were collected from individuals who had fasted for at least 12 hours, with no food or drink consumed. All biomedical procedures were conducted by accredited professionals following strict standards. These samples were kept at an optimal 4\u0026deg;C and promptly transported to the Beijing central laboratory (Youanmen Clinical Laboratory Center of Capital Medical University) for advanced diagnostic assessments. Glucose concentration, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were precisely measured using an enzymatic colorimetric method.\u003c/p\u003e\n\u003cp\u003eFigure 1 provides a detailed flowchart of the participant selection process. Among the 17,705 participants who completed physical examinations and questionnaire assessments in the 2011 baseline survey, individuals were subsequently excluded based on the following specific criteria: age less than 45 years (391 individuals), incomplete TyG-ABSI data (7,745 individuals), missing stroke information in both 2011 and 2018 (1,917 individuals), and a diagnosis of stroke at baseline (58 individuals). Participants with height within the 140\u0026ndash;200 cm range, weight within 30\u0026ndash;200 kg, and waist circumference within 50\u0026ndash;150 cm were selected. Ultimately, 7,358 participants were included in the study, of whom 467 experienced incident stroke after 2011, and 6,891 remained stroke-free until 2018.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Assessment of the TyG-ABSI Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTyG index = ln [triglyceride concentration (mg/dL) \u0026times; fasting blood glucose concentration (mg/dL)/2].\u003c/p\u003e\n\u003cp\u003eABSI = WC (cm)/ [BMI \u003csup\u003e2/3\u003c/sup\u003e \u0026times; height \u003csup\u003e1/2\u003c/sup\u003e (m) ].\u003c/p\u003e\n\u003cp\u003eTyG-ABSI = TyG \u0026times; ABSI\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Assessment of Incident Stroke\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncident stroke was assessed based on self-reported data. Participants were asked by the interviewer: \u0026quot;Have you been diagnosed with a stroke by a doctor?\u0026quot; Individuals who responded affirmatively were classified as having experienced a stroke. For this study, we excluded participants who reported having had a stroke in 2011 (DA007_8_). If a participant was diagnosed with stroke at any time after 2011 and up to the follow-up in 2018, they were included in our analysis as incident stroke cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Covariates Assessment\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe covariates assessment included social demographic characteristics, lifestyle behaviors, and current health status. Social demographic attributes consisted of age (in years), gender (male/female), marital status, education level (primary school and below/high school and above), and place of residence (rural/urban). Lifestyle behaviors included smoking status (never smoked/former, and current smoker) and alcohol consumption. Current health issues (yes/no) included hypertension, diabetes, heart disease, and dyslipidemia. Laboratory test results included triglycerides, glucose, body mass index (BMI), and waist circumference.\u003c/p\u003e\n\u003cp\u003eAdditionally, our BMI classification followed the Chinese adult standards. Underweight was characterized by a BMI below 18.5, normal weight was between 18.5 and 23.9, overweight was between 24 and 27.9, and a BMI of 28 or higher characterized obesity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data originated from the China Health and Retirement Longitudinal Study (CHARLS), which was conducted between 2011 and 2018. Our survey encompassed 7,358 participants. For continuous variables, data were represented by means (standard deviations, SD) or medians (interquartile ranges, IQR), while categorical variables were expressed as frequencies and percentages. We employed multivariable logistic regression models to examine the association between the TyG-ABSI index and incident stroke.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.1 Cox Proportional Hazards Regression Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed three multivariable\u0026nbsp;Cox proportional hazards models\u0026nbsp;to examine the prospective association between baseline TyG-ABSI and time-to-incident stroke. Model 1 was an unadjusted model, which included only the TyG-ABSI index as the predictor. Model 2 was adjusted for age and sex, which are well-known confounders in stroke risk. Model 3 was a fully adjusted model, which included the following potential confounders: age, sex, educational level (categorized as primary school and below, high school, and college and higher), place of residence (rural vs. urban), marital status (married vs. non-married), smoking status (never smoked, former smoker, current smoker), alcohol consumption (none, less than once a month, more than once a month), hypertension (yes/no), diabetes mellitus (yes/no), heart disease (yes/no), dyslipidemia (yes/no), and BMI (continuous variable). These covariates were selected based on their known associations with stroke risk in the literature. The proportional hazards assumption for all Cox models was tested using Schoenfeld residuals, and no significant violations were found. The hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for each model to quantify the association between TyG-ABSI and incident stroke. The p-values for trend were calculated to assess the dose-response relationship across TyG-ABSI quartiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.2 Restricted Cubic Spline Analysis for Non-linear Relationships\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the potential non-linear relationship between TyG-ABSI and incident stroke, we conducted restricted cubic spline (RCS) analysis using the \u0026quot;rms\u0026quot; package in RStudio. The RCS analysis was performed with three knots placed at the 10th, 50th, and 90th percentiles of the TyG-ABSI distribution. The overall p-value and the p-value for non-linearity were calculated to determine the significance of the association and the presence of non-linear trends. The RCS analysis was conducted for the fully adjusted model (Model 3) to ensure that other variables did not confound the potential non-linear relationship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.3 Receiver Operating Characteristic (ROC) Curve Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the predictive performance of TyG-ABSI for incident stroke, we performed receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC) was calculated for each model to assess the discriminative ability of TyG-ABSI. The AUC values were compared across the three models to determine the incremental predictive value of TyG-ABSI when combined with other clinical risk factors. The AUC values were interpreted as follows: 0.5 indicates no discriminative ability, 0.7 indicates moderate discriminative ability, and 0.8 or higher indicates good discriminative ability. The p-values for the differences in AUC values were calculated using the DeLong test to assess the statistical significance of the improvements in predictive performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.4 Multicollinearity Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the potential for multicollinearity among the covariates in our multivariable regression models, we calculated the variance inflation factor (VIF) for each predictor. A VIF value greater than 5 typically indicates significant multicollinearity. In our analysis, all VIF values were less than 5, confirming that multicollinearity was not a concern in our models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.5 Software and Significance Level\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Statistical analyses were conducted using RStudio version 5.0. A two-tailed p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable1 Baseline characteristics of study population by stroke status at follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=7358\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNor-stroke\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=6891)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=467)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge,\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003emean\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e58.5\u0026plusmn;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e58.3\u0026plusmn;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e61.0\u0026plusmn;8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3332(45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3114(45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e218(46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4021(54.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3772(54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e249(53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6574(89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6174(89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e400(85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Non-Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e784(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e717(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e67(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Junior high school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6619(90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6201(90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e418(89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e529(7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e494(7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e35(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; College and highter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e208(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e194(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e14(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6677(90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6275(91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e401(85.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e682(9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e616(8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e66(14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4565(62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4289(62.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e276(59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Former smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e579(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e526(7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e53(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Current smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2204(30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2066(30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e138(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; None of these\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4938(67.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4624(67.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e314(67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Drink but less than once a month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e582(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e546(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e36(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Drink more than once a month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1838(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1721(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e117(25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1711(23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1495(21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5609(76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5360(78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e384(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e48(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e336(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6907(94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e415(89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6492(95.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e809(11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e720(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e89(19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6518(89.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6143(89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e375(80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyslipidemia, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e638(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e554(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e84(18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6573(91.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6199(91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e374(81.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, mean\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e23.6\u0026plusmn;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;23.6\u0026plusmn;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;24.7\u0026plusmn;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP, mean\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2.5\u0026plusmn;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;2.4\u0026plusmn;6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;3.4\u0026plusmn;9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTyG-ABSI, mean\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e72.0\u0026plusmn;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e71.9\u0026plusmn;7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e74.3\u0026plusmn;8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote CVD: Cardiovascular Disease; BMI\u003c/strong\u003e: Body Mass Index,\u003cimg width=\"67\" height=\"10\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e/\u003cimg width=\"45\" height=\"10\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of Study Participants According to Incident Stroke and TyG-ABSI Quartiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 Summarizes the Demographic and Clinical Baseline Characteristics of the Study Participants. A total of 7,358 participants were included in the final cohort, among whom 467 experienced an incident stroke. Compared with the non-stroke population, stroke patients were significantly older (61.0 \u0026plusmn; 8.4 vs. 58.3 \u0026plusmn; 8.7 years, P \u0026lt; 0.001) and had higher levels of the TyG-ABSI index (74.3 \u0026plusmn; 8.1 vs. 71.9 \u0026plusmn; 7.8, P \u0026lt; 0.001), BMI (24.7 \u0026plusmn; 4.1 vs. 23.6 \u0026plusmn; 3.9, P \u0026lt; 0.001), and CRP (3.4 \u0026plusmn; 9.6 vs. 2.4 \u0026plusmn; 6.5 mg/L, P = 0.001). Traditional cardiovascular risk factors were significantly more prevalent in the stroke group, including hypertension (23.4% in stroke group vs. 21.8% in non-stroke group), diabetes (10.4% vs. 4.9%), history of cardiovascular disease (CVD) (19.2% vs. 10.5%), and dyslipidemia (18.3% vs. 8.2%) (P \u0026lt; 0.001). Notably, the risk of stroke was significantly higher among rural residents (hazard ratio [HR] = 1.71, 95% confidence interval [CI] 1.30\u0026ndash;2.24) and unmarried/divorced individuals (HR = 1.43, 95% CI 1.09\u0026ndash;1.87). With increasing TyG-ABSI quartiles (Q1\u0026ndash;Q4), there was a dose-dependent increase in age (56.1 to 60.6 years), BMI (22.9 to 24.3 kg/m\u0026sup2;), and prevalence of chronic diseases (diabetes 2.5% to 10.7%, hypertension 15.0% to 32.0%, CVD 9.6% to 13.4%, dyslipidemia 5.9% to 13.4%, all P \u0026lt; 0.001). In the highest TyG-ABSI quartile (Q4), the proportion of women increased significantly (63.6% vs. 46.9% in Q1), the proportion of rural residents rose (11.1% vs. 7.8% in Q1, P = 0.004), and the incidence of stroke doubled (8.9% vs. 4.1% in Q1, P \u0026lt; 0.001). Metabolic risk factors exhibited sex-specific patterns, with a higher smoking rate among men in the lowest TyG-ABSI quartile (Q1 34.6% vs. Q4 25.4%) and higher levels of education (junior high school and below 91.7% vs. 87.5% in Q1) and unmarried status (12.8% vs. 9.8% in Q1) in the highest TyG-ABSI quartile, forming a cluster of social determinants of risk. This gradient change confirms that TyG-ABSI integrates multidimensional risks of metabolism, demographics, and behavior, and its value as a composite biomarker for stroke risk stratification is of significant clinical importance (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Relationship Between TyG-ABSI and Incident Stroke\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 presents the results of the multiple regression analysis. The findings indicate a dose-response relationship between the TyG-ABSI index and stroke risk: in the unadjusted model (Model 1), the highest quartile group had a hazard ratio (HR) of 2.24 (95% CI 1.71\u0026ndash;2.95, P \u0026lt; 0.0001). After adjustment for age and sex (Model 2), the HR attenuated to 2.02 (95% CI 1.53\u0026ndash;2.67, P \u0026lt; 0.0001). In the fully adjusted model (Model 3), which included 14 potential confounders, the HR remained significantly elevated at 1.57 (95% CI 1.18\u0026ndash;2.08, P = 0.0019). The P-value for trend was 0.023 in the unadjusted model, 0.018 in the age- and sex-adjusted model, and 0.009 in the fully adjusted model, highlighting the robustness and independent predictive value of the TyG-ABSI index across different models.\u0026nbsp;In Figure 2, we employed restricted cubic splines (RCS) to examine the relationship between TyG-ABSI and incident stroke. Panels A, B, and C all demonstrate a significant overall association between TyG-ABSI and stroke (P-overall all \u0026lt; 0.05), with no significant nonlinear associations (P-non-linear all \u0026gt; 0.05), suggesting a potential linear relationship between TyG-ABSI and stroke risk. As TyG-ABSI increases, the odds ratio (OR) and its 95% confidence interval (CI) both show an upward trend, indicating that higher TyG-ABSI values may be associated with an increased risk of stroke occurrence. This trend is consistent across different models and subgroup analyses, suggesting that TyG-ABSI may be a potential indicator for assessing the relevant risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Baseline characteristic of the study population according to TyG-ABSI quartiles\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;N = 7,358\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;N = 1,840\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;N = 1,839\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;N = 1,839\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;N = 1,840\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eage, Mean\u0026plusmn;(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e58.48\u0026plusmn;8.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e56.14\u0026plusmn;8.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e58.09\u0026plusmn;8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e59.09\u0026plusmn;8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e60.61\u0026plusmn;9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003egender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4,021 (54.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e863 (46.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e940 (51.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,048 (57.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,170 (63.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3,332 (45.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e976 (53.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e898 (48.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e789 (42.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e669 (36.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eeducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; College and higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e208 (2.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e63 (3.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e49 (2.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e38 (2.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e58 (3.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e529 (7.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e168 (9.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e149 (8.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e118 (6.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e94 (5.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Junior high school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e6,619 (89.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,609 (87.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,641 (89.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,681 (91.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,688 (91.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003emarital, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0\u0026middot;006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e6,574 (89.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,659 (90.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,646 (89.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,664 (90.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,605 (87.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e784 (10.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e181 (9.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e193 (10.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e175 (9.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e235 (12.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eresidence, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0\u0026middot;004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e682 (9.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e143 (7.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e177 (9.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e158 (8.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e204 (11.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e6,676 (90.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,697 (92.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,662 (90.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,681 (91.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,636 (88.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003esmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2,204 (29.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e637 (34.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e600 (32.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e501 (27.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e466 (25.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Former smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e579 (7.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e141 (7.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e142 (7.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e142 (7.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e154 (8.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4,565 (62.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,061 (57.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,096 (59.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,194 (65.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,214 (66.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003edrinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Drink but less than a month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e582 (7.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e179 (9.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e152 (8.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e126 (6.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e125 (6.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Drink more than a month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1,838 (24.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e530 (28.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e469 (25.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e420 (22.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e419 (22.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; None of these\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4,938 (67.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,131 (61.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,218 (66.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,293 (70.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,296 (70.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e384 (5.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e45 (2.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e50 (2.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e95 (5.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e194 (10.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,711 (23.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e274 (14.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e367 (20.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e485 (26.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e585 (31.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCVD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e809 (11.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e175 (9.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e182 (9.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e206 (11.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e246 (13.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e638 (8.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106 (5.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125 (6.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e165 (9.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e242 (13.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI, Mean\u0026plusmn;(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.63\u0026plusmn;3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.92\u0026plusmn;4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.31\u0026plusmn;3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.97\u0026plusmn;3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.32\u0026plusmn;3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStroke2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0\u0026middot;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e467(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75(4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e109(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e119(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e164(8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6891(93.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1765(96.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1730(94.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1720(93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1676(91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable3 Prospective associations between baseline TyG-ABSI with follow-up incident stroke in Charls\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTyG-ABSI\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eAs Quartiles\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1(Reference)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2 HR(95%CI) \u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3 HR(95%CI) \u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4 HR(95%CI) \u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.47(1.09-1.97)0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.61(1.21-2.15)0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e2.24(1.71-2.95) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.4(1.04-1.88)0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.50(1.12-2.05)0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e2.02(1.53-2.67) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.33(0.99-1.88)0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.31(0.98-1.76)0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.57(1.18-2.08) 0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote Model 1: Crude model (unadjusted for covariates).Model 2: Adjusted for age and sex. Model 3: Adjusted for age, sex, educational level, residence, marital status, smoking, alcohol consumption, hypertension, diabetes mellitus, heart disease, dyslipidemia, and BMI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of ROC Curve Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 illustrates the predictive performance of the TyG-ABSI index for incident stroke across different adjustment models. In the base model (Model 1) without adjustment for covariates, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.590, indicating limited discriminative ability when TyG-ABSI was used alone. After adjustment for age and sex (Model 2), the AUC increased significantly to 0.622 (P\u0026lt;0.05), reflecting improved predictive performance. Most notably, in the fully adjusted model (Model 3), which included education level, place of residence, marital status, behavioral factors, and metabolic diseases, the AUC reached 0.701, demonstrating moderate-to-good predictive accuracy. This progressive increase in AUC (from 0.590 to 0.701) was statistically significant (P\u0026lt;0.01), confirming that TyG-ABSI, in combination with clinical risk factors, significantly enhances the prediction of incident stroke. The AUC in Model 3 exceeded the clinically meaningful threshold of 0.7, suggesting that this composite indicator may have practical clinical application value. Collectively, these results indicate that TyG-ABSI, as a dual marker of metabolism and obesity, provides important incremental information for stroke risk assessment when combined with traditional risk factors.\u003c/p\u003e\n\u003cp\u003eIn addition to the ROC curve analysis, we assessed the multicollinearity among the covariates in our regression models by calculating the variance inflation factor (VIF). All VIF values were less than 5, indicating that multicollinearity was not a significant issue in our models. This further supports the robustness of our findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStratified analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStratified analyses revealed that the association between TyG-ABSI and stroke risk was consistent across various subgroups(Fig.4). Specifically, no significant interaction was observed between age and the association of TyG-ABSI with stroke risk (HR = 1.04, 95% CI 1.02\u0026ndash;1.06 for those \u0026lt;60 years old; HR = 1.02, 95% CI 1.01\u0026ndash;1.04 for those \u0026ge;60 years old; P = 0.128 for interaction). Similarly, no significant interaction was detected between sex and the association of TyG-ABSI with stroke risk (HR = 1.04, 95% CI 1.02\u0026ndash;1.05 for females; HR = 1.03, 95% CI 1.02\u0026ndash;1.05 for males; P = 0.717 for interaction). Stratification by education level, marital status, smoking status, and alcohol consumption frequency also showed no significant interactions with TyG-ABSI and stroke risk (P \u0026gt; 0.05 for all). However, a significant interaction was found for place of residence (P = 0.038), with a stronger association between TyG-ABSI and stroke risk in urban residents (OR = 1.04, 95% CI 1.03\u0026ndash;1.05) than in rural residents (OR = 1.01, 95% CI 0.98\u0026ndash;1.03). Additionally, no significant interactions were observed in stratified analyses by BMI categories (underweight, normal weight, overweight, and obese), diabetes status (with and without diabetes), hypertension status (with and without hypertension), cardiovascular disease status (with and without CVD), and dyslipidemia status (with and without dyslipidemia) (P \u0026gt; 0.05 for all). These findings suggest that TyG-ABSI is a robust predictor of stroke risk across different demographic, lifestyle, metabolic, and disease subgroups, except for a stronger association in urban residents.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study leverages a large-scale cohort from the China Health and Retirement Longitudinal Study (CHARLS) to elucidate the significant correlation between the Triglyceride-Glucose and A Body Shape Index (TyG-ABSI) and stroke risk. TyG-ABSI, as a biomarker integrating metabolic and obesity factors, has demonstrated substantial potential in stroke risk prediction[12, 15]. This finding aligns with recent studies that highlight the pivotal role of metabolic factors in stroke risk. For instance, Liu et al. identified a close correlation between the variability of metabolic syndrome parameters and stroke incidence in hypertensive patients within a community-based cohort study[13]. Similarly, Moghadam-Ahmadi et al. confirmed a significant association between metabolic syndrome and stroke, further substantiating the critical role of metabolic anomalies in stroke pathogenesis[14]. Our study innovatively integrates metabolic and obesity factors into TyG-ABSI, providing a new tool for a more comprehensive stroke risk evaluation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Biological Mechanisms Underlying TyG-ABSI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe components of TyG-ABSI\u0026mdash;TyG index and ABSI\u0026mdash;reflect insulin resistance and abdominal obesity levels, respectively. The TyG index, calculated based on fasting triglycerides and blood glucose, is a widely accepted surrogate indicator of insulin resistance and can effectively identify high-risk individuals[16]. ABSI, which is based on waist circumference, height, and weight, more accurately measures abdominal fat distribution and visceral obesity compared to traditional indicators like BMI, and has a stronger predictive power for cardiovascular and metabolic diseases[17-19].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.1 Insulin Resistance and Metabolic Abnormalities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInsulin resistance not only causes abnormalities in blood glucose and lipids but also accelerates atherosclerosis and vascular damage through a variety of mechanisms. The core mechanisms include oxidative stress, chronic inflammation, and endothelial dysfunction. Insulin resistance increases the production of reactive oxygen species (ROS) and reduces the bioavailability of nitric oxide (NO), impairing endothelial cell function and promoting vasoconstriction and thrombosis[20, 21]. Meanwhile, chronic low-grade inflammation often accompanies insulin resistance, which activates the NLRP3 inflammasome, further exacerbating endothelial injury and inflammatory responses[22, 23]. Moreover, selective impairment of the insulin signaling pathway leads to decreased NO production and increased secretion of endothelin-1 (ET-1), resulting in vasoconstriction and stiffness and promoting the progression of atherosclerosis[24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.2 Abdominal Obesity and Cardiovascular Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbdominal obesity is an important component of metabolic syndrome and is closely related to the risk of cardiovascular disease[25]. ABSI provides a stronger predictive ability than traditional indicators (such as BMI) by more accurately measuring abdominal fat distribution and visceral obesity[26]. Visceral fat tissue not only stores fat but also secretes various bioactive substances, such as cytokines and adipokines, which can affect the overall metabolic state and increase the risk of cardiovascular disease[20, 27].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Dose-Response Relationship and Predictive Power\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur comprehensive analysis reveals a dose-response relationship between TyG-ABSI and stroke risk across different quartile groups. The highest quartile group showed a significantly elevated hazard ratio (HR =\u0026nbsp;1.57, 95% CI\u0026nbsp;1.18\u0026ndash;2.08, P =\u0026nbsp;0.0019) even after adjusting for multiple potential confounders. As TyG-ABSI levels rise, so does stroke risk, reinforcing TyG-ABSI\u0026apos;s effectiveness as an independent predictive indicator and its role in stroke risk stratification. Further restricted cubic spline (RCS) analysis reveals a significant linear relationship between TyG-ABSI and stroke risk (P-overall \u0026lt; 0.05, P-non-linear \u0026gt; 0.05), indicating that stroke risk increases linearly with rising TyG-ABSI levels. This linear relationship is consistent across different models and subgroup analyses, supporting TyG-ABSI\u0026apos;s robustness as a stroke risk predictor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Predictive Performance of TyG-ABSI\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Receiver operating characteristic (ROC) curve analysis further validates TyG-ABSI\u0026apos;s predictive performance. In the unadjusted model, TyG-ABSI\u0026apos;s area under the curve (AUC) was 0.590, indicating limited predictive power. However, as covariates were progressively added to the model, the AUC significantly increased, reaching 0.701 in the fully adjusted model (P \u0026lt; 0.01). Combining TyG-ABSI with other clinical risk factors significantly enhances stroke risk prediction, highlighting its importance in comprehensive risk assessment. Notably, the AUC in the fully adjusted model exceeded 0.7, suggesting TyG-ABSI\u0026apos;s potential clinical utility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Urban-Rural Disparities in Stroke Risk\u003c/strong\u003e\u003cbr\u003eIn our study, we observed a significant interaction between place of residence and the association between TyG-ABSI and stroke risk, with a stronger association found in urban residents than rural residents (P = 0.038). Urban residents had an hazard ratio (HR) of 1.04 (95% CI 1.03\u0026ndash;1.05), while rural residents had an HR of 1.01 (95% CI 0.98\u0026ndash;1.03). This discrepancy may stem from differences in lifestyle, dietary habits, and access to medical resources between urban and rural residents[28].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.1 Biological Factors\u003c/strong\u003e\u003cbr\u003eUrban residents often face higher levels of stress, which can exacerbate metabolic anomalies such as insulin resistance and hypertension[29, 30]. Chronic stress can lead to increased levels of cortisol[30], a hormone that promotes fat storage and insulin resistance, thereby increasing the risk of metabolic syndrome and stroke[31]. Additionally, urban lifestyles are characterized by higher consumption of processed foods and sugary beverages, which can increase triglyceride and glucose levels, further enhancing the TyG-ABSI score. These dietary habits, combined with reduced physical activity due to sedentary jobs[33] and limited recreational spaces, create a conducive environment for the development of metabolic disorders that increase stroke risk[32-34].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.2 Sociological Factors\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Access to healthcare is another critical factor that may explain the observed differences. Urban areas generally have better access to medical resources, including diagnostic facilities and specialized stroke care units. This means that urban residents with high TyG-ABSI scores are more likely to receive early diagnosis and intervention, which could mitigate some of the risk factors. However, this increased awareness and diagnosis might also lead to a higher reported incidence of stroke in urban areas. On the other hand, rural residents may face barriers to accessing timely and adequate medical care, which could result in underdiagnosis and undertreatment of stroke risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.3 Economic Factors\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Socioeconomic status (SES) also plays a significant role in stroke risk. Urban residents typically have higher SES, which can influence lifestyle choices and access to healthcare. Higher SES is often associated with better education, leading to greater awareness of health risks and the importance of preventive measures. Conversely, lower SES in rural areas may limit access to nutritious food, physical activity opportunities, and healthcare services, thereby exacerbating the risk of metabolic disorders and stroke.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Future Research Directions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Future studies should disentangle these complex interactions by incorporating detailed assessments of lifestyle factors, stress levels, and socioeconomic status. Longitudinal studies that track changes in these variables over time could provide deeper insights into the mechanisms underlying the observed differences. Additionally, interventions targeting urban populations should focus on stress reduction and healthy lifestyle promotion[34], while efforts in rural areas should prioritize improving access to healthcare and addressing socioeconomic disparities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Limitations of the Study\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Despite our study\u0026apos;s revelations about TyG-ABSI\u0026apos;s potential in stroke risk prediction based on the extensive data from the CHARLS database, certain limitations persist. Firstly, stroke event assessment in the CHARLS database relies on participant self-reports, which may introduce subjectivity and recall bias. Secondly, although the CHARLS database encompasses a wealth of sociodemographic and health-related data, it falls short in detailed records of lifestyle factors such as dietary habits and physical activity levels. Additionally, the CHARLS database\u0026apos;s focus on middle-aged and older Chinese individuals may restrict the extrapolation of our findings to other age groups and ethnic backgrounds. Future studies could consider corroborating stroke events with more objective medical records or imaging results to mitigate subjectivity bias. Simultaneously, refining the CHARLS database\u0026apos;s records on lifestyle factors or conducting similar studies in cohorts with more comprehensive lifestyle data would aid in a more holistic evaluation of the relationship between TyG-ABSI and stroke risk. Moreover, validation studies in diverse ethnic and geographical populations would help assess TyG-ABSI\u0026apos;s applicability in broader populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this large-cohort study demonstrates that the TyG-ABSI index potently predicts stroke risk in middle-aged and older Chinese adults, showing a stable dose-response relationship across multiple models. By integrating metabolic and obesity factors, TyG-ABSI enables more accurate risk assessment. While the study has a large sample size and sufficient follow-up, it also has limitations like self-reporting bias and incomplete records of lifestyle factors. TyG-ABSI helps clinicians identify high-risk individuals and highlights urban-rural differences, indicating the need for tailored prevention strategies. It has significant implications for clinical practice and public health policy-making. Future validation in diverse populations will further enhance its value in stroke prevention and management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to extend our sincere thanks to the China Health and Retirement Longitudinal Study (CHARLS) for making the summary data publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.H. and B.X. conceived and designed the study. K.H. performed the data analysis, wrote the initial draft of the manuscript,\u0026nbsp;prepared all figures (Fig. 1\u0026ndash;4) and tables (Table 1\u0026ndash;3). B.X. assisted with data collection and validation. M.D. revised the manuscript and provided critical feedback. All authors contributed to the final version of the manuscript and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the China Health and Retirement Longitudinal Study (CHARLS) repository. The data used to support this study\u0026apos;s findings are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKrishnamurthi, R.V., T. Ikeda, and V.L. 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Pagliaro, \u003cem\u003eEndothelial Dysfunction: Redox Imbalance, NLRP3 Inflammasome, and Inflammatory Responses in Cardiovascular Diseases.\u003c/em\u003e Antioxidants (Basel), 2025. \u003cstrong\u003e14\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eZhao, N., et al., \u003cem\u003eDiabetes Mellitus to Accelerated Atherosclerosis: Shared Cellular and Molecular Mechanisms in Glucose and Lipid Metabolism.\u003c/em\u003e J Cardiovasc Transl Res, 2024. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 133-152.\u003c/li\u003e\n\u003cli\u003eTakeda, Y., et al., \u003cem\u003eEndothelial Dysfunction in Diabetes.\u003c/em\u003e Biomedicines, 2020. \u003cstrong\u003e8\u003c/strong\u003e(7).\u003c/li\u003e\n\u003cli\u003ePowell-Wiley, T.M., et al., \u003cem\u003eObesity and Cardiovascular Disease: A Scientific Statement From the American Heart Association.\u003c/em\u003e Circulation, 2021. \u003cstrong\u003e143\u003c/strong\u003e(21): p. e984-e1010.\u003c/li\u003e\n\u003cli\u003eChristakoudi, S., et al., \u003cem\u003eA Body Shape Index (ABSI) achieves better mortality risk stratification than alternative indices of abdominal obesity: results from a large European cohort.\u003c/em\u003e Sci Rep, 2020. \u003cstrong\u003e10\u003c/strong\u003e(1): p. 14541.\u003c/li\u003e\n\u003cli\u003eGutierrez-Cuevas, J., et al., \u003cem\u003eMolecular Mechanisms of Obesity-Linked Cardiac Dysfunction: An Up-Date on Current Knowledge.\u003c/em\u003e Cells, 2021. \u003cstrong\u003e10\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eVerma, S.J., et al., \u003cem\u003eComparing Stroke Profiles and Outcomes between Urban and Rural India: A Secondary Analysis of the SPRINT INDIA Trial.\u003c/em\u003e Cerebrovasc Dis, 2025: p. 1-11.\u003c/li\u003e\n\u003cli\u003eNsabimana, P., et al., \u003cem\u003eAssociation between urbanization and metabolic syndrome in low- and middle-income countries: A systematic review and meta-analysis.\u003c/em\u003e Nutr Metab Cardiovasc Dis, 2024. \u003cstrong\u003e34\u003c/strong\u003e(2): p. 235-250.\u003c/li\u003e\n\u003cli\u003eKivimaki, M., A. Bartolomucci, and I. Kawachi, \u003cem\u003eThe multiple roles of life stress in metabolic disorders.\u003c/em\u003e Nat Rev Endocrinol, 2023. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 10-27.\u003c/li\u003e\n\u003cli\u003eLee, S.H., S.Y. Park, and C.S. Choi, \u003cem\u003eInsulin Resistance: From Mechanisms to Therapeutic Strategies.\u003c/em\u003e Diabetes Metab J, 2022. \u003cstrong\u003e46\u003c/strong\u003e(1): p. 15-37.\u003c/li\u003e\n\u003cli\u003eHooker, S.P., et al., \u003cem\u003eAssociation of Accelerometer-Measured Sedentary Time and Physical Activity With Risk of Stroke Among US Adults.\u003c/em\u003e JAMA Netw Open, 2022. \u003cstrong\u003e5\u003c/strong\u003e(6): p. e2215385.\u003c/li\u003e\n\u003cli\u003eWang, Z., et al., \u003cem\u003eSedentary behavior and the risk of stroke: A systematic review and dose-response meta-analysis.\u003c/em\u003e Nutr Metab Cardiovasc Dis, 2022. \u003cstrong\u003e32\u003c/strong\u003e(12): p. 2705-2713.\u003c/li\u003e\n\u003cli\u003eBai, L., et al., \u003cem\u003eAssociation of physical activity, sedentary behavior and stroke in older adults.\u003c/em\u003e Front Public Health, 2024. \u003cstrong\u003e12\u003c/strong\u003e: p. 1484765.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stroke, Triglyceride-glucose index, A body shape index, The China Health and Retirement Longitudinal Study (CHARLS), Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-7699425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7699425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eStroke is a leading global cause of morbidity and mortality, disproportionately affecting aging populations. Metabolic syndrome, obesity, and related dysfunctions are well-established risk factors. The triglyceride-glucose (TyG) index reflects insulin resistance, while the A Body Shape Index (ABSI) indicates abdominal adiposity. Their combination, TyG-ABSI, integrates metabolic and phenotypic risk for precision stratification, yet its prospective validation for stroke prediction in middle-aged and older adults remains underexplored.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eThis study examined the association between TyG-ABSI and incident stroke risk in middle-aged and older Chinese adults, evaluating its utility as a novel predictive biomarker.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) conducted between 2011 and 2018. A cohort of 7,358 participants with no prior history of stroke was selected through a rigorous screening process that excluded individuals based on specific criteria such as age, incomplete TyG-ABSI data, and missing stroke information. The predictive capacity of TyG-ABSI for incident stroke was evaluated using multivariable Cox proportional hazards regression models. Three models were constructed: an unadjusted model, a model adjusted for age and sex, and a fully adjusted model that included potential confounders such as education level, place of residence, marital status, smoking status, alcohol consumption, hypertension, diabetes mellitus, heart disease, dyslipidemia, and BMI. Additionally, restricted cubic spline (RCS) analysis was performed to explore the potential non-linear relationship between TyG-ABSI and incident stroke. Receiver operating characteristic (ROC) curve analysis was conducted to assess the predictive performance of TyG-ABSI for incident stroke across different adjustment models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eElevated TyG-ABSI correlated with increased stroke risk in a dose-dependent manner (highest vs. lowest quartile: HR\u0026thinsp;=\u0026thinsp;1.57, 95% CI\u0026thinsp;=\u0026thinsp;1.18\u0026ndash;2.08, P\u0026thinsp;=\u0026thinsp;0.0019). Urban residents showed a stronger association (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.05) than rural counterparts (OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;1.03; interaction P\u0026thinsp;=\u0026thinsp;0.038). The AUC in the fully adjusted model was 0.701, indicating moderate predictive accuracy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eTyG-ABSI robustly predicts incident stroke in middle-aged and older Chinese adults, with a significant dose-dependent relationship. This dual marker enhances precision risk stratification by integrating metabolic and obesity-related factors, offering clinical value for tailored prevention. Urban-rural disparities highlight the need for targeted strategies.\u003c/p\u003e","manuscriptTitle":"Triglyceride-Glucose and Body Shape Index (TyG-ABSI): A Novel Dual Biomarker for Predicting Stroke Risk in Middle-Aged and Older Chinese Adults—A Nationwide Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 09:57:51","doi":"10.21203/rs.3.rs-7699425/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-06T04:24:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T07:39:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T10:02:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5212923220249730797148000443924650009","date":"2026-03-30T05:02:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174168494748345733414128116332154503748","date":"2026-03-30T01:37:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252771275523975046735564933014394856420","date":"2026-03-28T03:07:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T00:55:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338385229852548767207153702582765930325","date":"2026-03-17T00:19:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182325239874393950701875283132745992612","date":"2025-12-15T08:48:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T13:20:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-10T21:36:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T01:47:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T01:46:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-09-24T04:58:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"894e5f79-6618-489f-aa6e-5ad3e4539f99","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T01:23:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 09:57:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7699425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7699425","identity":"rs-7699425","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}