Associations Between the CHG Index, Its Modified Versions, and Incident Stroke in Early-Stage CKM Syndrome: A Nationwide Cohort Study

Associations Between the CHG Index, Its Modified Versions, and Incident Stroke in Early-Stage CKM Syndrome: 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 Associations Between the CHG Index, Its Modified Versions, and Incident Stroke in Early-Stage CKM Syndrome: A Nationwide Cohort Study Li Ke, Ying Li, Si Jiang, Wenli Xing, Lei Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7746070/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2026 Read the published version in European Journal of Medical Research → Version 1 posted 11 You are reading this latest preprint version Abstract Background Cardiovascular-kidney-metabolic (CKM) syndrome is a major health burden. Stroke, the third leading cause of death globally, is strongly linked to insulin resistance (IR). The novel cholesterol, high-density lipoprotein (HDL), and glucose (CHG) index has been shown to have superior diagnostic accuracy for diabetes, but its association with stroke in early CKM syndrome (stages 0–3) is unclear. Methods This nationwide prospective cohort study included 6,836 adults with CKM syndrome stages 0–3 from the CHARLS (2011–2020). Multivariable Cox models assessed associations between baseline CHG indices (and modified variants) and incident stroke. Dose-response relationships were evaluated using restricted cubic splines (RCS) and Kaplan-Meier analysis. Results Over a 9-year follow-up period, 575 incident stroke cases were documented. Per 1-SD increase, the CHG index (HR = 1.18, 95% CI:1.07–1.29), CHG-WC (HR = 1.15, 1.05–1.26), and CHG-WHtR (HR = 1.12, 1.03–1.22) were independently associated with stroke. Quartile analysis revealed the strongest association for CHG-WHtR (Q4 vs. Q1: HR = 1.59, 95% CI: 1.19–2.11). Dose-response relationships were linear. Subgroup analyses indicated enhanced predictive utility in participants aged < 60 years and those with CKM stage 3. No significant association was observed between CHG-BMI and stroke incidence. Conclusion The​​ CHG index and its derivatives incorporating abdominal obesity indices (WC and WHtR) ​​robustly predict​​ incident stroke in early-stage CKM syndrome; their clinical adoption may enhance early detection and prevention of stroke events in ​​populations with vulnerable metabolism​. CKM syndrome Stroke CHG Modified CHG indices China Health and Retirement Longitudinal Study Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction ​​Cardiovascular-Kidney-Metabolic (CKM) syndrome​​ refers to a systemic disease caused by the ​​pathophysiological interplay​​ among metabolic abnormalities, chronic kidney disease, and cardiovascular disease, which significantly increases the global disease burden and mortality risk[ 1 ]. The American Heart Association(AHA) defines five CKM stages (0–4)[ 1 , 2 ]; notably, early CKM (stages 0–3) encompasses a large population with metabolic disorders or subclinical disease, for which preventive measures are most effective[ 1 , 3 ]. According to the 2021 Global Burden of Disease study, stroke ​​continues to be​​ one of the leading causes of disability and death worldwide[ 4 ]. Since stroke is a clinical event associated with advanced CKM (Stage 4), the AHA consequently emphasizes identifying at-risk individuals in preclinical stages (0–3) and calls for targeted research to advance stroke prevention[ 5 ]. Insulin resistance (IR) denotes diminished peripheral responsiveness to insulin, drives metabolic dysfunction, and markedly increases cardiovascular risk[ 6 – 8 ]. The triglyceride-glucose (TyG) index, a well-established biomarker of IR, along with its derived variants, has been extensively investigated in the context of cardiovascular disease (CVD)[ 3 , 9 , 10 ]. Recently proposed as a novel biomarker for type 2 diabetes mellitus, the cholesterol–high-density lipoprotein–glucose (CHG) index shows superior diagnostic performance to the triglyceride-glucose index and correlates closely with IR[ 11 ]. The CHG index is an integrated metabolic marker that reflects glucose and lipid dysregulation and represents a measure of overall metabolic health[ 12 ]. However, research on the association between the CHG index and stroke incidence among individuals in the early stages of CKM syndrome remains limited. ​​To ​​assess​​ obesity-related IR, we ​​developed​​ CHG-body mass index (BMI), CHG-waist-circumference (CHG-WC), and CHG-waist-to-height ​​ratio​​ (CHG-WHtR). Using the nationally representative, prospective China Health and Retirement Longitudinal Study (CHARLS), we examined associations of the CHG index and these derivatives with stroke risk among Chinese adults at CKM stages 0–3. Methods Study population CHARLS, a nationally representative cohort study conducted by the National School of Development at Peking University, monitors longitudinal changes in health, socioeconomic status, and demographics among Chinese adults aged ≥45 years[13]. The study, which encompasses 450 villages across 28 provinces, consisted of a baseline survey (2011-2012) and four follow-up waves (2013, 2015, 2018, and 2020). All participants in the CHARLS study provided written informed consent. The consent process was approved by the Peking University Institutional Review Board (IRB00001052-11015). As this study uses de-identified public data from CHARLS, additional consent was not required. This study was conducted in accordance with the STROBE guidelines. CHARLS data are accessible online (https://charls.pku.edu.cn/) upon registration and application. In this study, we initially included 17,708 participants from Wave 1. The following exclusion criteria were applied: (1) missing key variables necessary for calculating the CHG and related indices (weight, height, WC, age, gender, TC, FBG, and HDL-C); (2) baseline age less than 45 years; (3) classified as CKM stage 4 or missing CKM data at baseline; (4) missing other required covariates; (5) absence of follow-up data on incident stroke in subsequent waves. After these exclusions, a total of 6,836 participants were included in the final analytical cohort. The detailed flow of participant selection is illustrated in Figure 1. Definition of CKM syndrome stages 0 to 4 According to the AHA advisory[1], CKM syndrome is classified into five stages (0 to 4). Stage 0 denotes the absence of all CKM risk factors. Stage 1 is characterized by early metabolic disturbances, including overweight/obesity, abdominal obesity, and/or impaired glucose tolerance. Stage 2 encompasses individuals with metabolic risk factors—such as hypertriglyceridemia, hypertension, metabolic syndrome, or type 2 diabetes—as well as those with moderate- to high-risk chronic kidney disease (CKD), or both. Stage 3 refers to individuals at high risk or with subclinical CVD. Stage 4 corresponds to overt clinical cardiovascular disease attributable to underlying CKM-related pathologies, including diagnosed coronary artery disease, heart failure, stroke, peripheral artery disease, or atrial fibrillation[3,5]. Subclinical CVD risk equivalents were defined as either a high predicted 10-year CVD risk, assessed using the Framingham risk score with a threshold of >20%, or the presence of very high-risk CKD[14]. We classified CKD stages according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria[1]. According to a 2019 JAMA review, high-risk CKD was characterized by an estimated glomerular filtration rate (eGFR) of less than 30 mL/min per 1.73 m[15]². We defined the eGFR according to the Chinese Modification of Diet in Renal Disease (C-MDRD) formula: 175 × Scr−1.234 × age−0.179 × 0.79 (for females), reported in mL/min/1.73 m²[16]. Outcome ascertainment The primary endpoint of this study was the incidence of stroke. During the follow-up period (Waves 2–5), stroke cases were identified based on participant self-report. Specifically, participants who responded "yes" to either of the following questions were identified as having an incident stroke event: “Since the last interview, have you been diagnosed with a stroke by a doctor?” or “Are you currently undergoing any treatment (Traditional Chinese Medicine/Western medicine/physical therapy/acupuncture/occupational therapy) to manage your stroke?”. Participants who experienced a stroke during any follow-up wave prior to 2020 were not followed further. Otherwise, participants were followed until 2020. Time to stroke onset was defined as the time interval between the follow-up wave at which stroke was reported and the baseline survey. Assessment of CHG and modified CHG indices Laboratory analyses were carried out at the Youanmen Clinical Laboratory of Capital Medical University. Sample material consisted of frozen plasma or whole blood. Levels of fasting blood glucose (FBG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) were quantified via enzymatic colorimetry after an overnight fast. Anthropometric data—including height, body weight, and waist circumference (WC)—were collected three times per individual, and the average of these measurements was used in the analysis. The formula for calculating the CHG index is: CHG index = Ln [TC (mg/dL) × FBG (mg/dL) / 2 × HDL (mg/dL)][11]. BMI was calculated as“Weight (kg) /Height (m) 2 ” and WHtR was defined as“Waist (cm) /Height (cm)”. Through multiplying CHG with BMI, WC and WHtR respectively, modified CHG indices were produced (CHG-BMI, CHG-WC and CHG-WHtR)[11,17]. CHG and modified CHG indices were all divided into four groups according to quartiles (Q). Ascertainment of covariates Based on clinical experience and previous literature[3,17], we considered the following variables potential confounders: (1) Demographic characteristics: age, gender (female/male), educational level (no formal education, primary school, secondary school, or high school and above), marital status (married or other [including separated, divorced, widowed, or never married]), and residence (urban/rural). (2) Behavioral factors: smoking and alcohol consumption. Smoking status was defined by lifetime cigarette use; participants who had smoked >100 cigarettes were considered ever-smokers and categorized as never, former, or current smokers. Alcohol intake was classified as never (drinking <1 time/month), former (prior regular drinkers who quit within the past year), or current drinker. (3) Health status: hypertension and diabetes mellitus (DM). Hypertension was defined per established clinical guidelines[18], with cases identified by systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, previous physician diagnosis, or current use of antihypertensive medications. DM was diagnosed based on the presence of any of the following: fasting blood glucose ≥7.0 mmol/L, random glucose ≥11.1 mmol/L, glycated hemoglobin (HbA1c) ≥6.5%, physician-confirmed diagnosis, or ongoing hypoglycemic drug treatment[19]. (4) Laboratory-assessed parameters: blood urea nitrogen (BUN), eDFR, and uric acid (UA). Statistical analysis Participants' baseline characteristics were described according to CKM stage (0–3) and separately by outcome occurrence. The normality of data distribution was visually inspected via histograms before statistical analysis. Continuous variables were presented as mean ± standard deviation (SD) or median (interquartile range). Intergroup comparisons of continuous variables were performed using the Student's t-test or one-way analysis of variance (ANOVA), followed by the SNK or LSD method for post hoc multiple comparisons. Categorical variables were expressed as numbers (percentages) and were compared using the chi-square (χ²) test or Fisher's exact test. All statistical tests were two-sided, and a P-value < 0.05 was considered statistically significant. To evaluate the association between the CHG index and its modified versions with incident stroke, we performed Cox proportional hazards regression analyses. Each index was analyzed both as a continuous variable and as a categorical variable (by dividing into quartiles, Q1–Q4). Three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age, gender, residence, marital status, education level, smoking and alcohol use; and Model 3 was adjusted for all variables in Model 2 plus hypertension, DM, BUN, UA and eGFR. The results are expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). Adjusted Kaplan–Meier analysis was performed to visualize the cumulative incidence of new-onset stroke across quartiles of CHG-related indices, and differences were assessed using the direct test[20]. Restricted cubic spline (RCS) regression was used to assess potential non-linear relationships between the CHG index (and its modified versions) and stroke incidence; four knots were placed at the 5th, 35th, 65th and 95th percentiles of the baseline distribution, with adjustment for all covariates. The discriminatory ability of the IR indices (CHG, CHG-WC, and CHG-WHtR) for incident stroke was compared by deriving their respective areas under the receiver operating characteristic curve (AUC). Furthermore, subgroup analyses were performed to evaluate the consistency of associations across clinically relevant categories, stratified by age (<60, ≥60 years), gender, and CKM stage (0–3). All analyses were performed using R Statistical Software (Version4.2.2, http://www.R-project.org, The R Foundation) and d Free Statistics software version (Version.2.2, Beijing, China. http://www.clinicalscientists.cn/freestatistics). Results Participant characteristics The final cohort comprised 6,836 participants, stratified by CKM stage (0–3) (Table 1) and stroke outcome (Table 2). Overall, 52.3% were female, and the mean age was 58.9 ± 9.2 years. CKM stage 3 was the largest subgroup (n = 4,320), characterized by male predominance (82.9%), high smoking prevalence (75.4%), and the highest values for systolic blood pressure (135.4 ± 21.8 mmHg), fasting blood glucose (110.6 ± 36.5 mg/dL), and 10-year cardiovascular risk (15.0 ± 3.4%). Stroke occurred in 575 participants (8.4% of the cohort). Compared with non-stroke participants, they were significantly older (60.9 ± 8.5 vs. 58.7 ± 9.2 years; p< 0.001) and exhibited elevated mean values of systolic and diastolic blood pressure, total cholesterol, triglycerides, LDL-C, uric acid, the CHG index, and all three novel CHG-related indices (CHG-BMI, CHG-WC, and CHG-WHtR; all p< 0.05). Conversely, HDL-C and eGFR were significantly lower in the stroke group (both p< 0.05). Additionally, CKM stage 3 was overrepresented among stroke cases (75.8% vs. 60.6%; p< 0.001). Table 1. The baseline characteristics according to CKM stages. Variables Total CKM Stage P -value Stage 0 Stage 1 Stage 2 Stage 3 N 6836 291 748 1567 4320 Age, years (mean (SD)) 58.9 ± 9.2 55.1 ± 8.0 54.3 ± 6.8 54.1 ± 6.6 61.8 ± 9.3 < 0.001 Gender, n (%) < 0.001 Female 3608 (52.8) 259 (89) 679 (90.8) 1446 (92.3) 1224 (28.9) Male 3228 (47.2) 32 (11) 69 (9.2) 121 (7.7) 3006 (71.1) Marital status, n (%) < 0.001 Married 783 (11.5) 28 (9.6) 65 (8.7) 117 (7.5) 573 (13.5) Others 6053 (88.5) 263 (90.4) 683 (91.3) 1450 (92.5) 3657 (86.5) Education level, n (%) < 0.001 No formal education 3290 (48.1) 172 (59.1) 404 (54) 787 (50.2) 1927 (45.6) Primary school 1479 (21.6) 38 (13.1) 137 (18.3) 266 (17) 1038 (24.5) Middle school 1387 (20.3) 46 (15.8) 149 (19.9) 340 (21.7) 852 (20.1) High school or above 680 (9.9) 35 (12) 58 (7.8) 174 (11.1) 413 (9.8) Residency registration, n (%) 0.008 Urban 2360 (34.5) 83 (28.5) 251 (33.6) 588 (37.5) 1438 (34) Rural 4476 (65.5) 208 (71.5) 497 (66.4) 979 (62.5) 2792 (66) Smoke, n (%) < 0.001 Never 4161 (60.9) 282 (96.9) 728 (97.3) 1526 (97.4) 1625 (38.4) Former 538 (7.9) 0 (0) 2 (0.3) 8 (0.5) 528 (12.5) Current 2137 (31.3) 9 (3.1) 18 (2.4) 33 (2.1) 2077 (49.1) Alcohol, n (%) < 0.001 Never 4107 (60.1) 242 (83.2) 605 (80.9) 1288 (82.2) 1972 (46.6) Former 535 (7.8) 6 (2.1) 33 (4.4) 62 (4) 434 (10.3) Current 2194 (32.1) 43 (14.8) 110 (14.7) 217 (13.8) 1824 (43.1) Hypertension (%), n (%) < 0.001 No 3757 (55.0) 291 (100.0) 748 (100.0) 975 (62.2) 1767 (41.8) Yes 3079 (45.0) 0 (0.0) 0 (0.0) 592 (37.8) 2463 (58.2) Diabetes, n (%) < 0.001 No 5847 (85.5) 291 (100) 748 (100) 1263 (80.6) 3545 (83.8) Yes 989 (14.5) 0 (0) 0 (0.0) 304 (19.4) 685 (16.2) SBP, mmHg (mean (SD)) 128.6 ± 21.0 108.9 ± 9.5 112.0 ± 8.9 121.9 ± 14.6 135.4 ± 21.8 < 0.001 DBP, mmHg (mean (SD)) 75.0 ± 12.0 64.3 ± 7.5 67.3 ± 7.2 74.2 ± 10.9 77.4 ± 12.3 < 0.001 Height, cm (mean (SD)) 1.6 ± 0.1 1.5 ± 0.1 1.5 ± 0.1 1.5 ± 0.1 1.6 ± 0.1 < 0.001 Weight, kg (mean (SD)) 58.6 ± 11.4 47.3 ± 7.0 55.9 ± 10.2 58.2 ± 9.9 59.9 ± 11.8 < 0.001 WC, cm (mean (SD)) 84.1 ± 11.9 72.3 ± 9.5 82.6 ± 10.2 84.6 ± 11.9 85.0 ± 11.9 < 0.001 TC, mg/dL (mean (SD)) 194.2 ± 38.3 184.6 ± 31.3 191.1 ± 35.0 198.2 ± 38.0 193.9 ± 39.1 < 0.001 HDL, mg/dL (mean (SD)) 51.7 ± 15.2 59.7 ± 13.9 58.0 ± 12.7 48.6 ± 13.6 51.2 ± 15.8 < 0.001 Tg, mg/dL (median (IQR)) 102.7 (73.5, 149.6) 77.0 (59.7, 101.8) 81.4 (63.7, 103.5) 136.3 (90.3, 185.0) 102.7 (72.6, 150.4) < 0.001 LDL, mg/dL (mean (SD)) 117.3 ± 34.7 112.3 ± 28.1 118.8 ± 30.6 119.1 ± 35.7 116.7 ± 35.4 0.006 FBG, mg/dL (mean (SD)) 108.9 ± 34.0 90.5 ± 7.1 99.5 ± 10.4 112.3 ± 35.4 110.6 ± 36.5 < 0.001 UA, mg/dL (mean (SD)) 4.4 ± 1.2 3.7 ± 1.0 3.8 ± 0.9 4.1 ± 1.1 4.7 ± 1.3 < 0.001 BUN, mg/dL (mean (SD)) 15.7 ± 4.5 15.0 ± 4.5 15.1 ± 4.0 14.7 ± 4.2 16.2 ± 4.6 < 0.001 Cr, mg/dL (mean (SD)) 0.8 ± 0.2 0.7 ± 0.1 0.7 ± 0.1 0.7 ± 0.1 0.8 ± 0.3 < 0.001 eGFR, ml/min per 1.73 m^2 (mean (SD)) 92.7 ± 14.4 97.6 ± 12.1 97.8 ± 11.5 96.4 ± 13.2 90.2 ± 14.8 < 0.001 Framingham, 10-year CVD risk% (mean (SD)) 12.2 ± 4.8 6.3 ± 2.6 6.6 ± 2.5 8.2 ± 2.7 15.0 ± 3.4 < 0.001 CHG (mean (SD)) 5.3 ± 0.4 5.0 ± 0.2 5.1 ± 0.2 5.4 ± 0.4 5.3 ± 0.5 < 0.001 CHG-BMI (mean (SD)) 125.9 ± 55.2 98.9 ± 12.5 119.5 ± 19.7 131.6 ± 22.7 126.8 ± 67.7 < 0.001 CHG-WC (mean (SD)) 448.4 ± 82.8 358.1 ± 51.4 421.4 ± 56.8 459.4 ± 79.4 455.3 ± 84.9 < 0.001 CHG-WHtR (mean (SD)) 2.8 ± 0.6 2.3 ± 0.3 2.7 ± 0.4 3.0 ± 0.5 2.9 ± 0.6 < 0.001 Data are presented as: Number (%) for categorical variables. Mean ± SD or median (IQR) for continuous variables. Abbreviation: UA: uric acid, BUN:blood urea nitrogen, Cr:serum creatinine, CKM: cardiovascular-kidney-metabolic syndrome, eGFR: estimated Glomerular Filtration Rate, FBG: fasting blood glucose, TC: total cholesterol, HDL: high density lipoprotein cholesterol, Tg: triglyceride, LDL: low density lipoprotein cholesterol, SBP: systolic blood pressure, DBP: diastolic pressure, WC: waist circumference, WHtR: waist-to-height ratio, BMI: body mass index, CHG: cholesterol-HDL-glucose index, CHG-BMI: CHG multiplied BMI, CHG-WC: CHG multiplied WC, CHG-WHtR: CHG multiplied WHtR. Table 2. The baseline characteristics according to stroke incidence. Variables Total (n = 6836) Non-stroke (n = 6261) New onset stroke (n = 575) p -value Age, years (mean (SD)) 58.9 ± 9.2 58.7 ± 9.2 60.9 ± 8.5 < 0.001 Gender, n (%) 0.05 Female 3608 (52.8) 3327 (53.1) 281 (48.9) Male 3228 (47.2) 2934 (46.9) 294 (51.1) Marital status, n (%) 0.265 Married 783 (11.5) 709 (11.3) 74 (12.9) Others 6053 (88.5) 5552 (88.7) 501 (87.1) Education level, n (%) 0.308 No formal education 3290 (48.1) 3005 (48) 285 (49.6) Primary school 1479 (21.6) 1344 (21.5) 135 (23.5) Middle school 1387 (20.3) 1285 (20.5) 102 (17.7) High school or above 680 (9.9) 627 (10) 53 (9.2) Residency registration, n (%) 0.748 Urban 2360 (34.5) 2165 (34.6) 195 (33.9) Rural 4476 (65.5) 4096 (65.4) 380 (66.1) Smoke, n (%) < 0.001 Never 4161 (60.9) 3845 (61.4) 316 (55) Former 538 (7.9) 468 (7.5) 70 (12.2) Current 2137 (31.3) 1948 (31.1) 189 (32.9) Alcohol, n (%) 0.002 Never 4107 (60.1) 3787 (60.5) 320 (55.7) Former 535 (7.8) 469 (7.5) 66 (11.5) Current 2194 (32.1) 2005 (32) 189 (32.9) Hypertension (%), n (%) < 0.001 No 3757 (55.0) 3541 (56.6) 216 (37.6) Yes 3079 (45.0) 2720 (43.4) 359 (62.4) Diabetes, n (%) 0.002 No 5847 (85.5) 5380 (85.9) 467 (81.2) Yes 989 (14.5) 881 (14.1) 108 (18.8) SBP, mmHg (mean (SD)) 128.6 ± 21.0 127.8 ± 20.6 137.0 ± 23.4 < 0.001 DBP, mmHg (mean (SD)) 75.0 ± 12.0 74.7 ± 11.9 78.4 ± 13.0 < 0.001 Height, cm (mean (SD)) 1.6 ± 0.1 1.6 ± 0.1 1.6 ± 0.1 0.679 Weight, kg (mean (SD)) 58.6 ± 11.4 58.4 ± 11.3 60.8 ± 11.6 < 0.001 WC, cm (mean (SD)) 84.1 ± 11.9 83.9 ± 11.8 86.9 ± 12.9 < 0.001 TC, mg/dL (mean (SD)) 194.2 ± 38.3 193.8 ± 38.2 198.7 ± 38.7 0.003 HDL, mg/dL (mean (SD)) 51.7 ± 15.2 52.0 ± 15.2 48.9 ± 14.9 < 0.001 Tg, mg/dL (median (IQR)) 102.7 (73.5, 149.6) 101.8 (72.6, 147.8) 114.2 (85.0, 160.2) < 0.001 LDL, mg/dL (mean (SD)) 117.3 ± 34.7 116.9 ± 34.6 121.5 ± 35.5 0.003 FBG, mg/dL (mean (SD)) 108.9 ± 34.0 108.4 ± 33.2 114.7 ± 40.8 < 0.001 UA, mg/dL (mean (SD)) 4.4 ± 1.2 4.4 ± 1.2 4.5 ± 1.3 0.017 BUN, mg/dL (mean (SD)) 15.7 ± 4.5 15.7 ± 4.5 15.7 ± 4.3 0.934 Cr, mg/dL (mean (SD)) 0.8 ± 0.2 0.8 ± 0.2 0.8 ± 0.2 0.014 eGFR, ml/min per 1.73 m^2 (mean (SD)) 92.7 ± 14.4 93.0 ± 14.4 90.2 ± 14.2 < 0.001 CHG (mean (SD)) 5.3 ± 0.4 5.3 ± 0.4 5.4 ± 0.5 < 0.001 CHG-BMI (mean (SD)) 125.9 ± 55.2 125.3 ± 57.1 132.5 ± 27.5 0.003 CHG-WC (mean (SD)) 448.4 ± 82.8 446.1 ± 81.8 473.7 ± 88.8 < 0.001 CHG-WHtR (mean (SD)) 2.8 ± 0.6 2.8 ± 0.6 3.0 ± 0.6 < 0.001 Framingham, 10-year CVD risk% (mean (SD)) 12.2 ± 4.8 12.0 ± 4.8 14.2 ± 4.6 < 0.001 CKM stage, n (%) < 0.001 0 291 (4.3) 281 (4.5) 10 (1.7) 1 748 (10.9) 717 (11.5) 31 (5.4) 2 1567 (22.9) 1469 (23.5) 98 (17) 3 4230 (61.9) 3794 (60.6) 436 (75.8) Data are presented as: Number (%) for categorical variables. Mean ± SD or median (IQR) for continuous variables. Relationship between CHG and modified CHG indices with stroke incidence among people with CKM stage 0 – 3 Table 3 presents the associations between the CHG index, its modified variants, and incident stroke. In continuous analysis, the baseline CHG index significantly predicted stroke risk per 1-SD increase, with effect estimates remaining consistent across progressively adjusted models (fully adjusted Model 3: HR 1.18, 95% CI: 1.07–1.29; p = 0.001), indicating robustness to demographic, lifestyle, and metabolic confounders. Categorically, participants in the highest quartile (Q4) had a 41% higher risk than those in Q1 (HR 1.41, 95% CI: 1.08–1.85; p = 0.011), demonstrating a dose-response relationship. Among the modified indices, CHG-WC showed a robust association both as a continuous variable (per 1-SD increase: HR 1.15, 95% CI: 1.05–1.26; p = 0.003) and as a categorical variable (Q4 vs Q1: HR 1.52, 95% CI: 1.15–2.02; p = 0.003). Furthermore, CHG-WHtR was significantly associated with stroke risk in continuous analysis (per 1-SD: HR 1.12, 95% CI: 1.03–1.22; p = 0.008), with the highest quartile conferring a 59% increased risk (HR 1.59, 95% CI: 1.19–2.11; p = 0.002). While CHG-BMI showed no significant association with stroke in either continuous (HR 1.04, 95% CI 0.96-1.13; p=0.382) or categorical analyses. Table 3. Associations of CHG index and modified indices with stroke onset. Model 1 HR (95%CI) P -value Model 2 HR (95%CI) P -value Model 3 HR (95%CI) P -value CHG (per 1 SD) 1.21 (1.13~1.32) <0.001 1.20 (1.11~1.30) <0.001 1.18 (1.07~1.29) 0.001 CHG quartile Q1 1.00 1.00 1.00 Q2 1.20 (0.91~1.57) 0.196 1.10 (0.84~1.45) 0.486 1.04 (0.79~1.38) 0.773 Q3 1.28 (0.99~1.64) 0.056 1.20 (0.93~1.55) 0.159 1.13 (0.87~1.46) 0.351 Q4 1.60 (1.26~2.04) < 0.001 1.54 (1.20~1.97) 0.001 1.41 (1.08~1.85) 0.011 CHG-BMI (per 1 SD) 1.06 (1.00~1.12) 0.048 1.06 (1.00~1.13) 0.058 1.04 (0.96~1.13) 0.382 CHG-BMI quartile Q1 1.00 1.00 1.00 Q2 1.01 (0.77~1.32) 0.903 1.03 (0.79~1.36) 0.808 0.97 (0.73~1.28) 0.824 Q3 1.36 (1.05~1.75) 0.019 1.43 (1.10~1.86) 0.007 1.26 (0.97~1.65) 0.087 Q4 1.25 (0.98~1.60) 0.073 1.31 (1.01~1.70) 0.039 1.08 (0.82~1.41) 0.594 CHG-WC (per 1 SD) 1.22 (1.13~1.32) <0.001 1.21 (1.11~1.31) <0.001 1.15 (1.05~1.26) 0.003 CHG-WC quartile Q1 1.00 1.00 1.00 Q2 1.46 (1.10~1.94) 0.008 1.44 (1.08~1.91) 0.012 1.38 (1.03~1.83) 0.026 Q3 1.73 (1.33~2.26) <0.001 1.73 (1.32~2.26) <0.001 1.61 (1.23~2.11) 0.001 Q4 1.86 (1.44~2.40) <0.001 1.79 (1.38~2.33) <0.001 1.52 (1.15~2.02) 0.003 CHG-WHtR (per 1 SD) 1.18 (1.11~1.26) <0.001 1.17 (1.09~1.26) <0.001 1.12 (1.03~1.22) 0.008 Q1 1.00 1.00 1.00 Q2 1.54 (1.16~2.05) 0.003 1.55 (1.17~2.06) 0.002 1.47 (1.11~1.96) 0.008 Q3 2.00 (1.53~2.60) <0.001 2.00 (1.52~2.62) <0.001 1.81 (1.37~2.38) <0.001 Q4 1.87 (1.45~2.43) <0.001 1.85 (1.41~2.42) <0.001 1.59 (1.19~2.11) 0.002 Model 1: non-adjusted; Model 2: adjusted for age, gender, residence, education levels, marital status, smoking and drinking status; Model 3: adjusted for factors in model 2 and hypertension, diabetes, BUN, UA, and eGFR. Figure 2 further depicts RCS analyses of CHG, CHG-WC, and CHG-WHtR in relation to incident stroke risk across the CKM stage 0-3 population. All indices demonstrated linear associations with stroke risk (P for non-linearity: CHG = 0.595, CHG-WC = 0.887, CHG-WHtR = 0.513). Kaplan–Meier analyses in Figure 3 revealed dose-dependent gradients in cumulative stroke incidence across quartiles of CHG, CHG-WC, and CHG-WHtR. For these indices, participants in Q4 consistently demonstrated the highest event rates, whereas Q1 exhibited the lowest risk trajectories. Statistically significant separation between quartile curves was confirmed by adjusted direct tests (all P< 0.005). ROC curve analysis (Figure 4) revealed that CHG-WC had the strongest discrimination for new-onset stroke (AUC = 0.6528), followed by CHG (AUC = 0.6504) and CHG-WHtR(AUC = 0.6493). Subgroup analyses Subgroup analyses were conducted to explore the relationships between the CHG, CHG-WC, and CHG-WHtR indices and stroke incidence across populations stratified by age, gender, and CKM stage (Table 4). The analyses revealed significant age-dependent associations, with all CHG indices demonstrating robust stroke risk predictions in participants aged < 60 years (CHG: HR 1.27, 95% CI 1.13–1.42; CHG-WC: HR 1.32, 95% CI 1.16–1.50; CHG-WHtR: HR 1.22, 95% CI 1.12–1.33; all P 0.05), the CHG, CHG-WC, and CHG-WHtR indices showed effect estimates consistent with those in younger cohorts ( 0.05), with consistent positive associations in both males (HRs 1.16–1.18, all P< 0.05) and females (HRs 1.14–1.18, all P< 0.05). When stratified by CKM stage, only stage 3 participants showed significant associations: CHG (HR 1.18, P=0.001), CHG-WC (HR 1.14, P=0.011), and CHG-WHtR (HR 1.11, P=0.027). The absence of significant findings in stages 0–2 may reflect limited statistical power. Table 4. Subgroup analyses of the relationship between CHG index and modified indices with stroke incidence in a population with CKM syndrome stages 0–3. Characteristics Number of participants CHG HR (95%CI) P -value P for interaction CHG-WC HR (95%CI) P -value P for interaction CHG-WHtR HR (95%CI) P -value P for interaction Age(years) 0.050 0.040 0.032 < 60 3895 1.27 (1.13~1.42) < 0.001 1.32 (1.16~1.50) < 0.001 1.22 (1.12~1.33) < 0.001 ≥ 60 2941 1.09 (0.97~1.23) 0.139 1.06 (0.95~1.19) 0.288 1.06 (0.94~1.19) 0.339 Gender 0.477 0.605 0.534 Male 3228 1.18 (1.06~1.32) 0.002 1.17 (1.04~131) 0.011 1.16 (1.02~1.31) 0.020 Female 3608 1.14 (1.01~1.28) 0.038 1.18 (1.04~1.34) 0.012 1.16 (1.04~1.29) 0.007 CKM stages 0.074 0.447 0.246 Srage 0 291 4.28 (0.63~29.31) 0.139 1.04 (0.30~3.61) 0.946 1.18 (0.33~4.25) 0.802 Stage 1 748 2.13 (0.97~4.64) 0.058 1.04 (0.30~3.61) 0.218 1.18 (0.33~4.25) 0.128 Stage 2 1567 1.03 (0.83~1.28) 0.784 1.15 (0.94~1.41) 0.180 1.16 (0.94~1.44) 0.174 Stage 3 4230 1.18 (1.07~1.29) 0.001 1.14 (1.03~1.26) 0.010 1.11 (1.01~1.21) 0.027 Note: CHG and modified CHG indices (CHG-WC and CHG-WHtR) were analyzed as continuous variable (per 1 SD). The models were adjusted for age, gender, residence, education, marital status, smoking, drinking, hypertension, diabetes, BUN, UA, and eGFR. Discussion In 6,836 adults with CKM stages 0–3, the CHG index and its variants (CHG-WC and CHG-WHtR) were independently associated with incident stroke, whereas CHG-BMI was not. Each 1-SD increase in the CHG index was associated with an 18% higher risk of stroke (HR 1.18, 95% CI 1.07–1.29). CHG-WHtR demonstrated the strongest association (quantile Q4 vs. Q1: HR 1.59, 95% CI 1.19–2.11). RCS analyses indicated linear relationships of CHG, CHG-WC, and CHG-WHtR with stroke risk, and Kaplan-Meier curves showed progressively increasing cumulative incidence across increasing quartiles of these indices. Associations were most pronounced in participants aged <60 years and in CKM stage 3, underscoring the utility of these indices in metabolically active populations. IR drives cardiovascular pathogenesis through increased arterial stiffness[21]. Notably, it accelerates atherosclerosis and plaque destabilisation independent of hyperglycaemia[22]. Constituents of the CKM syndrome are significantly associated with atherosclerotic risk. Accelerated atherosclerosis driven by IR may represent a mechanism for stroke development in early-stage CKM populations[2]. Emerging evidence suggests that systematic IR quantification and targeted interventions in CKM stages 0- 3, including individuals from risk-free states to subclinical cardiovascular pathology, may disrupt the pathophysiological cascade underlying cardiovascular events[23]. CHG and its obesity-integrated derivatives serve as significant markers for predicting IR[11,17,24]. Originally introduced by Mansoori et al., the CHG index exhibits enhanced discriminative power for type 2 diabetes relative to the TyG index[11]; subsequent studies indicate it may also outperform the TyG index in predicting CVD risk[17]. Consistent with previous studies, we found a positive, linear dose-response relationship between both the CHG index and its derivative indices (CHG-WC and CHG-WHtR) with stroke incidence in the CKM stage 0–3 population. Previously, several indices—such as the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio[25,26], total cholesterol to HDL-C ratio (TC/HDL-C)[27], triglyceride-glucose (TyG) index[3,28], and homeostatic model assessment of insulin resistance (HOMA-IR)[29,30]—have been employed to estimate the risk of IR and stroke. Despite their proven value, these markers exhibit drawbacks such as variable accuracy across demographics, elevated expense, methodological intricacy, and unreliable replication in areas with limited resources[12]. By combining the routinely assayed parameters of total cholesterol, HDL-C, and fasting blood glucose, the CHG index forms a composite biomarker that captures derangements in both lipid and glucose metabolism. This unification potentially improves its predictive power and clinical utility[12]. Notably, while CHG, CHG-WC, and CHG-WHtR were significantly associated with stroke events, CHG-BMI showed no statistically significant relationship. Although the precise mechanisms remain unclear, this discrepancy may arise from the distinct pathophysiological correlates of abdominal obesity indicators (e.g., WC, WHtR) compared to general adiposity measures (e.g., BMI). The pathogenesis of CKM syndrome frequently involves adipose tissue excess, dysfunction, or a combination thereof[2]. Dysfunctional visceral fat triggers the hypersecretion of pro-inflammatory and pro-oxidant mediators, resulting in vascular, myocardial, and renal damage that ultimately raises CVD risk[31,32]. Although widely used, BMI and waist circumference are poor proxies for abdominal fat distribution. A Lancet review underscored the critical importance of synthesizing lipid profile data with abdominal adiposity metrics, such as WC[33]. Indeed, WC and WHtR are well-validated parameters for assessing abdominal obesity[34,35], and demonstrate superior predictive value over BMI for CVD incidence[36,37]. Therefore, the integration of the CHG index with abdominal obesity measures (CHG-WC and CHG-WHtR) reveals a synergistic risk phenotype that is more directly linked to the pathogenesis of stroke in early CKM stages. This finding reinforces current guidelines which emphasize that waist-centered anthropometric measures hold greater prognostic value than BMI in cardiometabolic risk stratification. Strengths and limitations The robustness of this study derives from three key attributes: (i) a nationally representative cohort of high CKM risk middle-aged and older Chinese adults, enhancing generalisability; (ii) concurrent evaluation of CHG-derived indices incorporating visceral adiposity surrogates (WC/WHtR), enabling integrated profiling of metabolic and anatomical stroke determinants; and (iii) a prospective design featuring rigorous covariate adjustment and subgroup validation, collectively supporting the robustness and consistency of our findings. However, several limitations merit acknowledgment. First, the single-baseline assessment of CHG-related indices precludes tracking longitudinal changes in metabolic profiles or adiposity redistribution, potentially underestimating cumulative exposure impact. Second, while prior CHARLS validation supports reasonable accuracy, stroke ascertainment predominantly through self-reports raises potential misclassification concerns. Third, generalizability beyond middle-aged and older Chinese individuals necessitates caution regarding younger or non-Chinese demographics. Finally, residual confounding from unmeasured variables remains plausible despite rigorous covariate adjustments characteristic of observational research. Conclusion Our findings indicate that the CHG index and its derivatives incorporating abdominal obesity indices (WC and WHtR) serve as valuable tools for predicting stroke incidence in individuals with early CKM stages (0–3). Consequently, these indices could be useful for early identification and intervention in CKM syndrome. Declarations Data availability statement Data can be found in a publicly accessible repository. The datasets produced and analyzed in this study are available on the CHARLS homepage at http://charls.pku.edu.cn/en. CHARLS data have been de - identified, with participants being recognized by a distinct ID number. Ethics statement This research includes individuals as participants. Approval for the CHARLS survey initiative was granted by the Biomedical Ethics Committee at Peking University (IRB00001052-11015). All participants provided their informed consent in writing at the time of their involvement. Competing interests None declared. Author contributions Study concept and design: Li Ke, Ying Li, Sili Jiang, Wenli Xing and Lei Zhao. Acquisition of data: Li Ke, Ying Li. Analysis and interpretation of data: Li Ke, Ying Li, Sili Jiang, Wenli Xing and Lei Zhao. Li Ke, Ying Li and Wenli Xing wrote of the manuscript. All authors have revised the manuscript. Funding The author(s) declare that there was no financial assistance obtained for the research. Acknowledgments We extend our gratitude to the National Institute on Aging's Behavioral and Social Research Division, the Natural Science Foundation of China, the World Bank, and Peking University for their financial support. We also express our appreciation to the CHARLS research and field teams, as well as all study participants, for their invaluable contributions. References Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation. 2023;148:1606–35. https://doi.org/10.1161/cir.0000000000001184 Ndumele CE, Neeland IJ, Tuttle KR, Chow SL, Mathew RO, Khan SS, et al. A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome: A Scientific Statement From the American Heart Association. Circulation. 2023;148:1636–64. https://doi.org/10.1161/cir.0000000000001186 Hong J, Zhang R, Tang H, Wu S, Chen Y, Tan X. Comparison of triglyceride glucose index and modified triglyceride glucose indices in predicting cardiovascular diseases incidence among populations with cardiovascular-kidney-metabolic syndrome stages 0–3: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24. https://doi.org/10.1186/s12933-025-02662-3 Bryazka D, Reitsma MB, Abate YH, Al Magied AHAA, Abdelkader A, Abdollahi A, et al. Forecasting the effects of smoking prevalence scenarios on years of life lost and life expectancy from 2022 to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Public Health [Internet]. 2024;9:e729–44. https://doi.org/10.1016/s2468-2667(24)00166-x Yue Y, Li P, Sun Z, Murayama R, Li Z, Hashimoto K, et al. Association of novel triglyceride-glucose-related indices with incident stroke in early-stage cardiovascular-kidney-metabolic syndrome. Cardiovasc Diabetol. 2025;24. https://doi.org/10.1186/s12933-025-02854-x Bornfeldt KE, Tabas I. Insulin Resistance, Hyperglycemia, and Atherosclerosis. Cell Metab. 2011;14:575–85. https://doi.org/10.1016/j.cmet.2011.07.015 Duan M, Zhao X, Li S, Miao G, Bai L, Zhang Q, et al. Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001–2018. Cardiovasc Diabetol. 2024;23. https://doi.org/10.1186/s12933-024-02334-8 Lu Z, Xiong Y, Feng X, Yang K, Gu H, Zhao X, et al. Insulin resistance estimated by estimated glucose disposal rate predicts outcomes in acute ischemic stroke patients. Cardiovasc Diabetol. 2023;22. https://doi.org/10.1186/s12933-023-01925-1 Zhang Q, Xiao S, Jiao X, Shen Y. The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001–2018. Cardiovasc Diabetol. 2023;22. https://doi.org/10.1186/s12933-023-02030-z Wu Y, Yang Y, Zhang J, Liu S, Zhuang W. The change of triglyceride-glucose index may predict incidence of stroke in the general population over 45 years old. Cardiovasc Diabetol. 2023;22. https://doi.org/10.1186/s12933-023-01870-z Mansoori A, Nosrati M, Dorchin M, Mohammadyari F, Derakhshan‐Nezhad E, Ferns G, et al. A novel index for diagnosis of type 2 diabetes mellitus: Cholesterol, High density lipoprotein, and Glucose (CHG) index. J Diabetes Investig. 2024; https://doi.org/10.1111/jdi.14343 Zhang Z, Chen M, Cai D, Fan M, Wang Y. Association between the cholesterol, high-density lipoprotein, and glucose index and type 2 diabetes mellitus prevalence in US adults: A cross-sectional study based on National Health and Nutrition Examination Survey 2009-2018. . 2025;53:3000605251375557–3000605251375557. https://doi.org/10.1177/03000605251375557 Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2012;43:61–8. https://doi.org/10.1093/ije/dys203 D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General Cardiovascular Risk Profile for Use in Primary Care. Circulation. 2008;117:743–53. https://doi.org/10.1161/circulationaha.107.699579 Chen TK, Knicely DH, Grams ME. Chronic Kidney Disease Diagnosis and Management. JAMA. 2019;322:1294. https://doi.org/10.1001/jama.2019.14745 Ma Y-C, Zuo L, Chen J-H, Luo Q, Yu X-Q, Li Y, et al. Modified Glomerular Filtration Rate Estimating Equation for Chinese Patients with Chronic Kidney Disease. JASN. 2006;17:2937–44. https://doi.org/10.1681/asn.2006040368 Mo D, Zhang P, Zhang M, Dai H, Guan J. Cholesterol, high-density lipoprotein, and glucose index versus triglyceride–glucose index in predicting cardiovascular disease risk: a cohort study. Cardiovasc Diabetol. 2025;24. https://doi.org/10.1186/s12933-025-02675-y Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 International Society of Hypertension Global Hypertension Practice Guidelines. Hypertension [Internet]. 2020;75:1334–57. https://doi.org/10.1161/hypertensionaha.120.15026 Huang C, You H, Zhang Y, Fan L, Feng X, Shao N. Association between the hemoglobin A1c/High-density lipoprotein cholesterol ratio and stroke incidence: a prospective nationwide cohort study in China. Lipids Health Dis. 2025;24. https://doi.org/10.1186/s12944-025-02438-4 Zhang X, Loberiza FR, Klein JP, Zhang M-J. A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model. Comput Methods Programs Biomed. 2007;88:95–101. https://doi.org/10.1016/j.cmpb.2007.07.010 Gast KB, Tjeerdema N, Stijnen T, Smit JWA, Dekkers OM. Insulin Resistance and Risk of Incident Cardiovascular Events in Adults without Diabetes: Meta-Analysis. PLoS One. 2012;7:e52036. https://doi.org/10.1371/journal.pone.0052036 DeFronzo RA. Insulin resistance, lipotoxicity, type 2 diabetes and atherosclerosis: the missing links. The Claude Bernard Lecture 2009. Diabetologia. 2010;53:1270–87. https://doi.org/10.1007/s00125-010-1684-1 Zhang H, Huang S, Fang Y, Zhang H, Yang W, Yu M. Insulin resistance assessed by estimated glucose disposal rate predicts cardiovascular disease in stages 0–3 of cardiovascular-kidney-metabolic syndrome: a UK biobank cohort study. Cardiovasc Diabetol. 2025;24. https://doi.org/10.1186/s12933-025-02860-z Çatak M, Konuk ŞG, Hepsen S. The cholesterol‐HDL‐glucose (CHG) index and traditional adiposity markers in predicting diabetic retinopathy and nephropathy. J Diabetes Investig. 2025; https://doi.org/10.1111/jdi.70086 Che B, Zhong C, Zhang R, Pu L, Zhao T, Zhang Y, et al. Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc Diabetol. 2023;22. https://doi.org/10.1186/s12933-023-01762-2 Zhang S, Cao C, Han Y, Hu H, Zheng X. A nonlinear relationship between the triglycerides to high-density lipoprotein cholesterol ratio and stroke risk: an analysis based on data from the China Health and Retirement Longitudinal Study. Diabetol Metab Syndr. 2024;16. https://doi.org/10.1186/s13098-024-01339-3 Xiao B, Cao C, Han Y, Yang F, Hu H, Luo J. A non-linear connection between the total cholesterol to high-density lipoprotein cholesterol ratio and stroke risk: a retrospective cohort study from the China Health and Retirement Longitudinal Study. Eur J Med Res [Internet]. 2024;29. https://doi.org/10.1186/s40001-024-01769-9 Wu M, Li C, Yu Y, Zeng L, Qiu Y, Liu J, et al. Association between the triglyceride-glucose (TyG) index and stroke risk in Chinese normal-weight adults: a population-based study. Diabetol Metab Syndr. 2024;16. https://doi.org/10.1186/s13098-024-01421-w Yang X, Wang G, Jing J, Wang A, Zhang X, Jia Q, et al. Association of triglyceride-glucose index and stroke recurrence among nondiabetic patients with acute ischemic stroke. BMC Neurol. 2022;22. https://doi.org/10.1186/s12883-022-02588-3 Jing J, Pan Y, Zhao X, Zheng H, Jia Q, Mi D, et al. Insulin Resistance and Prognosis of Nondiabetic Patients With Ischemic Stroke. Stroke [Internet]. 2017;48:887–93. https://doi.org/10.1161/strokeaha.116.015613 Bhupathiraju SN, Hu FB. Epidemiology of Obesity and Diabetes and Their Cardiovascular Complications. Circ Res. 2016;118:1723–35. https://doi.org/10.1161/circresaha.115.306825 Rana MN, Neeland IJ. Adipose Tissue Inflammation and Cardiovascular Disease: An Update. Curr Diab Rep [Internet]. 2022;22:27–37. https://doi.org/10.1007/s11892-021-01446-9 Neeland IJ, Ross R, Després J-P, Matsuzawa Y, Yamashita S, Shai I, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 2019;7:715–25. https://doi.org/10.1016/s2213-8587(19)30084-1 Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 2005;56:303–7. https://doi.org/10.1080/09637480500195066 Kahn BB, Flier JS. Obesity and insulin resistance. J Clin Invest. 2000;106:473–81. https://doi.org/10.1172/jci10842 Ashwell M, Gunn P, Gibson S. Waist‐to‐height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta‐analysis. Obes Rev. 2011;13:275–86. https://doi.org/10.1111/j.1467-789x.2011.00952.x Wang L, Lee Y, Wu Y, Zhang X, Jin C, Huang Z, et al. A prospective study of waist circumference trajectories and incident cardiovascular disease in China: the Kailuan Cohort Study. Am J Clin Nutr. 2020;113:338–47. https://doi.org/10.1093/ajcn/nqaa331 Additional Declarations No competing interests reported. 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23:41:53","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181648,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7746070/v1/02beb8d878172802565f80c4.html"},{"id":94049813,"identity":"28f683a4-82c3-4298-80df-d482a643b2c5","added_by":"auto","created_at":"2025-10-21 23:41:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55976,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart.​\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7746070/v1/0007b2d458d8707da0c056a0.png"},{"id":94049814,"identity":"7a6dfdf2-59ea-4211-b8d8-4728599e9108","added_by":"auto","created_at":"2025-10-21 23:41:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24676,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationships of the CHG, CHG-WC, and CHG-WHtR indices with stroke risk, analyzed by RCS and adjusted for age, gender, residence, education, marital status, smoking, alcohol use, hypertension, diabetes, BUN, UA, and eGFR.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7746070/v1/ddf67e94d5d1d92c0fe42068.png"},{"id":94049821,"identity":"7edf80eb-2081-456a-84dd-a428a7cd7610","added_by":"auto","created_at":"2025-10-21 23:41:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23591,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted Kaplan–Meier curves show the incidence of stroke according to quartiles of the (A) CHG, (B) CHG-WC, and (C) CHG-WHtR indices, after adjustment for age, gender, residence, education, marital status, smoking, drinking, hypertension, diabetes, BUN, UA, and eGFR.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7746070/v1/778c9cbdb1404f9aadefac95.png"},{"id":94049816,"identity":"6acdb02a-6e73-42db-a84f-9fd2af9dff4b","added_by":"auto","created_at":"2025-10-21 23:41:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66239,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminative accuracy of insulin-resistance indices for incident stroke.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7746070/v1/f9361520f6d7ed89107819dd.png"},{"id":100069312,"identity":"898741c0-d83c-4a61-82ab-09e3571fa494","added_by":"auto","created_at":"2026-01-12 16:12:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1239765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7746070/v1/5d5193a4-b3cb-44d8-b01f-ef11e46a70d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations Between the CHG Index, Its Modified Versions, and Incident Stroke in Early-Stage CKM Syndrome: A Nationwide Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003e​​Cardiovascular-Kidney-Metabolic (CKM) syndrome​​ refers to a systemic disease caused by the ​​pathophysiological interplay​​ among metabolic abnormalities, chronic kidney disease, and cardiovascular disease, which significantly increases the global disease burden and mortality risk[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The American Heart Association(AHA) defines five CKM stages (0\u0026ndash;4)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]; notably, early CKM (stages 0\u0026ndash;3) encompasses a large population with metabolic disorders or subclinical disease, for which preventive measures are most effective[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the 2021 Global Burden of Disease study, stroke ​​continues to be​​ one of the leading causes of disability and death worldwide[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Since stroke is a clinical event associated with advanced CKM (Stage 4), the AHA consequently emphasizes identifying at-risk individuals in preclinical stages (0\u0026ndash;3) and calls for targeted research to advance stroke prevention[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInsulin resistance (IR) denotes diminished peripheral responsiveness to insulin, drives metabolic dysfunction, and markedly increases cardiovascular risk[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The triglyceride-glucose (TyG) index, a well-established biomarker of IR, along with its derived variants, has been extensively investigated in the context of cardiovascular disease (CVD)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Recently proposed as a novel biomarker for type 2 diabetes mellitus, the cholesterol\u0026ndash;high-density lipoprotein\u0026ndash;glucose (CHG) index shows superior diagnostic performance to the triglyceride-glucose index and correlates closely with IR[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The CHG index is an integrated metabolic marker that reflects glucose and lipid dysregulation and represents a measure of overall metabolic health[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, research on the association between the CHG index and stroke incidence among individuals in the early stages of CKM syndrome remains limited. ​​To ​​assess​​ obesity-related IR, we ​​developed​​ CHG-body mass index (BMI), CHG-waist-circumference (CHG-WC), and CHG-waist-to-height ​​ratio​​ (CHG-WHtR). Using the nationally representative, prospective China Health and Retirement Longitudinal Study (CHARLS), we examined associations of the CHG index and these derivatives with stroke risk among Chinese adults at CKM stages 0\u0026ndash;3.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHARLS, a nationally representative cohort study conducted by the National School of Development at Peking University, monitors longitudinal changes in health, socioeconomic status, and demographics among Chinese adults aged ≥45 years[13]. The study, which encompasses 450 villages across 28 provinces, consisted of a baseline survey (2011-2012) and four follow-up waves (2013, 2015, 2018, and 2020). All participants in the CHARLS study provided written informed consent. The consent process was approved by the Peking University Institutional Review Board (IRB00001052-11015). As this study uses de-identified public data from CHARLS, additional consent was not required. This study was conducted in accordance with the STROBE guidelines. CHARLS data are accessible online (https://charls.pku.edu.cn/) upon registration and application.\u003c/p\u003e\n\u003cp\u003eIn this study, we initially included 17,708 participants from Wave 1. The following exclusion criteria were applied: (1) missing key variables necessary for calculating the CHG and related indices (weight, height, WC, age, gender, TC, FBG, and HDL-C); (2) baseline age less than 45 years; (3) classified as CKM stage 4 or missing CKM data at baseline; (4) missing other required covariates; (5) absence of follow-up data on incident stroke in subsequent waves. After these exclusions, a total of 6,836 participants were included in the final analytical cohort. The detailed flow of participant selection is illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of CKM syndrome stages 0 to 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the AHA advisory[1], CKM syndrome is classified into five stages (0 to 4). Stage 0 denotes the absence of all CKM risk factors. Stage 1 is characterized by early metabolic disturbances, including overweight/obesity, abdominal obesity, and/or impaired glucose tolerance. Stage 2 encompasses individuals with metabolic risk factors—such as hypertriglyceridemia, hypertension, metabolic syndrome, or type 2 diabetes—as well as those with moderate- to high-risk chronic kidney disease (CKD), or both. Stage 3 refers to individuals at high risk or with subclinical CVD. Stage 4 corresponds to overt clinical cardiovascular disease attributable to underlying CKM-related pathologies, including diagnosed coronary artery disease, heart failure, stroke, peripheral artery disease, or atrial fibrillation[3,5].\u003c/p\u003e\n\u003cp\u003eSubclinical CVD risk equivalents were defined as either a high predicted 10-year CVD risk, assessed using the Framingham risk score with a threshold of \u0026gt;20%, or the presence of very high-risk CKD[14]. We classified CKD stages according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria[1]. According to a 2019 JAMA review, high-risk CKD was characterized by an estimated glomerular filtration rate (eGFR) of less than 30 mL/min per 1.73 m[15]². We defined the eGFR according to the Chinese Modification of Diet in Renal Disease (C-MDRD) formula: 175 × Scr−1.234 × age−0.179 × 0.79 (for females), reported in mL/min/1.73 m²[16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome ascertainment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary endpoint of this study was the incidence of stroke. During the follow-up period (Waves 2–5), stroke cases were identified based on participant self-report. Specifically, participants who responded \"yes\" to either of the following questions were identified as having an incident stroke event: “Since the last interview, have you been diagnosed with a stroke by a doctor?” or “Are you currently undergoing any treatment (Traditional Chinese Medicine/Western medicine/physical therapy/acupuncture/occupational therapy) to manage your stroke?”. Participants who experienced a stroke during any follow-up wave prior to 2020 were not followed further. Otherwise, participants were followed until 2020. Time to stroke onset was defined as the time interval between the follow-up wave at which stroke was reported and the baseline survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of CHG and modified CHG indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLaboratory analyses were carried out at the Youanmen Clinical Laboratory of Capital Medical University. Sample material consisted of frozen plasma or whole blood. Levels of fasting blood glucose (FBG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) were quantified via enzymatic colorimetry after an overnight fast. Anthropometric data—including height, body weight, and waist circumference (WC)—were collected three times per individual, and the average of these measurements was used in the analysis. The formula for calculating the CHG index is: CHG index = Ln [TC (mg/dL) × FBG (mg/dL) / 2 × HDL (mg/dL)][11]. BMI was calculated as“Weight (kg) /Height (m)\u003csup\u003e2\u003c/sup\u003e” and WHtR was defined as“Waist (cm) /Height (cm)”. Through multiplying CHG with BMI, WC and WHtR respectively, modified CHG indices were produced (CHG-BMI, CHG-WC and CHG-WHtR)[11,17]. CHG and modified CHG indices were all divided into four groups according to quartiles (Q).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAscertainment of covariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on clinical experience and previous literature[3,17], we considered the following variables potential confounders: (1) Demographic characteristics: age, gender (female/male), educational level (no formal education, primary school, secondary school, or high school and above), marital status (married or other [including separated, divorced, widowed, or never married]), and residence (urban/rural). (2) Behavioral factors: smoking and alcohol consumption. Smoking status was defined by lifetime cigarette use; participants who had smoked \u0026gt;100 cigarettes were considered ever-smokers and categorized as never, former, or current smokers. Alcohol intake was classified as never (drinking \u0026lt;1 time/month), former (prior regular drinkers who quit within the past year), or current drinker. (3) Health status: hypertension and diabetes mellitus (DM). Hypertension was defined per established clinical guidelines[18], with cases identified by systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, previous physician diagnosis, or current use of antihypertensive medications. DM was diagnosed based on the presence of any of the following: fasting blood glucose ≥7.0 mmol/L, random glucose ≥11.1 mmol/L, glycated hemoglobin (HbA1c) ≥6.5%, physician-confirmed diagnosis, or ongoing hypoglycemic drug treatment[19]. (4) Laboratory-assessed parameters: blood urea nitrogen (BUN), eDFR, and uric acid (UA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants' baseline characteristics were described according to CKM stage (0–3) and separately by outcome occurrence. The normality of data distribution was visually inspected via histograms before statistical analysis. Continuous variables were presented as mean ± standard deviation (SD) or median (interquartile range). Intergroup comparisons of continuous variables were performed using the Student's t-test or one-way analysis of variance (ANOVA), followed by the SNK or LSD method for post hoc multiple comparisons. Categorical variables were expressed as numbers (percentages) and were compared using the chi-square (χ²) test or Fisher's exact test. All statistical tests were two-sided, and a P-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003eTo evaluate the association between the CHG index and its modified versions with incident stroke, we performed Cox proportional hazards regression analyses. Each index was analyzed both as a continuous variable and as a categorical variable (by dividing into quartiles, Q1–Q4). Three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age, gender, residence, marital status, education level, smoking and alcohol use; and Model 3 was adjusted for all variables in Model 2 plus hypertension, DM, BUN, UA and eGFR. The results are expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). Adjusted Kaplan–Meier analysis was performed to visualize the cumulative incidence of new-onset stroke across quartiles of CHG-related indices, and differences were assessed using the direct test[20]. Restricted cubic spline (RCS) regression was used to assess potential non-linear relationships between the CHG index (and its modified versions) and stroke incidence; four knots were placed at the 5th, 35th, 65th and 95th percentiles of the baseline distribution, with adjustment for all covariates. The discriminatory ability of the IR indices (CHG, CHG-WC, and CHG-WHtR) for incident stroke was compared by deriving their respective areas under the receiver operating characteristic curve (AUC).\u003c/p\u003e\n\u003cp\u003eFurthermore, subgroup analyses were performed to evaluate the consistency of associations across clinically relevant categories, stratified by age (\u0026lt;60, ≥60 years), gender, and CKM stage (0–3). All analyses were performed using R Statistical Software (Version4.2.2, http://www.R-project.org, The R Foundation) and d Free Statistics software version (Version.2.2, Beijing, China. http://www.clinicalscientists.cn/freestatistics).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final cohort comprised 6,836 participants, stratified by CKM stage (0\u0026ndash;3) (Table 1) and stroke outcome (Table 2). Overall, 52.3% were female, and the mean age was 58.9 \u0026plusmn; 9.2 years. CKM stage 3 was the largest subgroup (n = 4,320), characterized by male predominance (82.9%), high smoking prevalence (75.4%), and the highest values for systolic blood pressure (135.4 \u0026plusmn; 21.8 mmHg), fasting blood glucose (110.6 \u0026plusmn; 36.5 mg/dL), and 10-year cardiovascular risk (15.0 \u0026plusmn; 3.4%).\u003c/p\u003e\n\u003cp\u003eStroke occurred in 575 participants (8.4% of the cohort). Compared with non-stroke participants, they were significantly older (60.9\u0026nbsp;\u0026plusmn;\u0026nbsp;8.5 vs. 58.7\u0026nbsp;\u0026plusmn;\u0026nbsp;9.2 years; p\u0026lt; 0.001) and exhibited elevated mean values of systolic and diastolic blood pressure, total cholesterol, triglycerides, LDL-C, uric acid, the CHG index, and all three novel CHG-related indices (CHG-BMI, CHG-WC, and CHG-WHtR; all p\u0026lt; 0.05). Conversely, HDL-C and eGFR were significantly lower in the stroke group (both p\u0026lt; 0.05). Additionally, CKM stage 3 was overrepresented among stroke cases (75.8% vs. 60.6%; p\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eTable 1. The baseline characteristics according to CKM stages.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCKM Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStage 0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStage 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, years (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.9 \u0026plusmn; 9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.1 \u0026plusmn; 8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54.3 \u0026plusmn; 6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54.1 \u0026plusmn; 6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.8 \u0026plusmn; 9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3608 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e259 (89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e679 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1446 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1224 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3228 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3006 (71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e783 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e117 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e573 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6053 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e263 (90.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e683 (91.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1450 (92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3657 (86.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation level, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3290 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e172 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e404 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e787 (50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1927 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1479 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e137 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e266 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1038 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1387 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e149 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e340 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e852 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e680 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e174 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e413 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eResidency registration, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2360 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e251 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e588 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1438 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4476 (65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e208 (71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e497 (66.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e979 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2792 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSmoke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4161 (60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e282 (96.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e728 (97.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1526 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1625 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e538 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e528 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2137 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2077 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4107 (60.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e242 (83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e605 (80.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1288 (82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1972 (46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e535 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e434 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2194 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e217 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1824 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\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\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3757 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e291 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e748 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e975 (62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1767 (41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3079 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e592 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2463 (58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5847 (85.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e291 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e748 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1263 (80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3545 (83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e989 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e304 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e685 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP, mmHg (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e128.6 \u0026plusmn; 21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108.9 \u0026plusmn; 9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112.0 \u0026plusmn; 8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121.9 \u0026plusmn; 14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e135.4 \u0026plusmn; 21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDBP, mmHg (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.0 \u0026plusmn; 12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.3 \u0026plusmn; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.3 \u0026plusmn; 7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.2 \u0026plusmn; 10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.4 \u0026plusmn; 12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeight, cm (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight, kg (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.6 \u0026plusmn; 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.3 \u0026plusmn; 7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.9 \u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.2 \u0026plusmn; 9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.9 \u0026plusmn; 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWC, cm (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.1 \u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.3 \u0026plusmn; 9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.6 \u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.6 \u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.0 \u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTC, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e194.2 \u0026plusmn; 38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e184.6 \u0026plusmn; 31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e191.1 \u0026plusmn; 35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e198.2 \u0026plusmn; 38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e193.9 \u0026plusmn; 39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDL, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.7 \u0026plusmn; 15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.7 \u0026plusmn; 13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.0 \u0026plusmn; 12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.6 \u0026plusmn; 13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.2 \u0026plusmn; 15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTg, mg/dL (median (IQR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.7 (73.5, 149.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.0 (59.7, 101.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.4 (63.7, 103.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e136.3 (90.3, 185.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.7 (72.6, 150.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDL, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e117.3 \u0026plusmn; 34.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112.3 \u0026plusmn; 28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e118.8 \u0026plusmn; 30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e119.1 \u0026plusmn; 35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116.7 \u0026plusmn; 35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFBG, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108.9 \u0026plusmn; 34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.5 \u0026plusmn; 7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99.5 \u0026plusmn; 10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112.3 \u0026plusmn; 35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110.6 \u0026plusmn; 36.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUA, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.4 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.7 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.8 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.1 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.7 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBUN, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.7 \u0026plusmn; 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.0 \u0026plusmn; 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.1 \u0026plusmn; 4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.7 \u0026plusmn; 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.2 \u0026plusmn; 4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCr, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eeGFR, ml/min per\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.73\u0026nbsp;m^2 (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92.7 \u0026plusmn; 14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97.6 \u0026plusmn; 12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97.8 \u0026plusmn; 11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96.4 \u0026plusmn; 13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.2 \u0026plusmn; 14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFramingham, 10-year CVD risk% (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.2 \u0026plusmn; 4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.3 \u0026plusmn; 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.6 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.2 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.0 \u0026plusmn; 3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHG (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.3 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.1 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.4 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.3 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHG-BMI (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125.9 \u0026plusmn; 55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.9 \u0026plusmn; 12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e119.5 \u0026plusmn; 19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e131.6 \u0026plusmn; 22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126.8 \u0026plusmn; 67.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHG-WC (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e448.4 \u0026plusmn; 82.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e358.1 \u0026plusmn; 51.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e421.4 \u0026plusmn; 56.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e459.4 \u0026plusmn; 79.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e455.3 \u0026plusmn; 84.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHG-WHtR (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.8 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.3 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.0 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.9 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\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\u003eData are presented as: Number (%) for categorical variables. Mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD or median (IQR) for continuous variables.\u003c/p\u003e\n\u003cp\u003eAbbreviation: UA: uric acid, BUN:blood urea nitrogen, Cr:serum creatinine, CKM: cardiovascular-kidney-metabolic syndrome, eGFR: estimated Glomerular Filtration Rate, FBG: fasting blood glucose, TC: total cholesterol, HDL: high density lipoprotein cholesterol, Tg: triglyceride, LDL: low density lipoprotein cholesterol, SBP: systolic blood pressure, DBP: diastolic pressure, WC: waist circumference, WHtR: waist-to-height ratio, BMI: body mass index, CHG: cholesterol-HDL-glucose index, CHG-BMI: CHG multiplied BMI, CHG-WC: CHG multiplied WC, CHG-WHtR: CHG multiplied WHtR.\u003c/p\u003e\n\u003cp\u003eTable 2. The baseline characteristics according to stroke incidence.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal (n = 6836)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-stroke (n = 6261)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNew onset stroke (n = 575)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, years (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.9 \u0026plusmn; 9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.7 \u0026plusmn; 9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.9 \u0026plusmn; 8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3608 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3327 (53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e281 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3228 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2934 (46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e294 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e783 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e709 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6053 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5552 (88.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e501 (87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation level, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3290 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3005 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e285 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1479 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1344 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e135 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1387 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1285 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e680 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e627 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eResidency registration, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2360 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2165 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e195 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4476 (65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4096 (65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e380 (66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSmoke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4161 (60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3845 (61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e316 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e538 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e468 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2137 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1948 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e189 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4107 (60.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3787 (60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e320 (55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e535 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e469 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2194 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2005 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e189 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\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\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3757 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3541 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216 (37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3079 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2720 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e359 (62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5847 (85.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5380 (85.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e467 (81.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e989 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e881 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP, mmHg (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e128.6 \u0026plusmn; 21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127.8 \u0026plusmn; 20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e137.0 \u0026plusmn; 23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDBP, mmHg (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.0 \u0026plusmn; 12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.7 \u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.4 \u0026plusmn; 13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeight, cm (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight, kg (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.6 \u0026plusmn; 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.4 \u0026plusmn; 11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.8 \u0026plusmn; 11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWC, cm (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.1 \u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.9 \u0026plusmn; 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.9 \u0026plusmn; 12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTC, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e194.2 \u0026plusmn; 38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e193.8 \u0026plusmn; 38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e198.7 \u0026plusmn; 38.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDL, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.7 \u0026plusmn; 15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.0 \u0026plusmn; 15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.9 \u0026plusmn; 14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTg, mg/dL (median (IQR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.7 (73.5, 149.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.8 (72.6, 147.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e114.2 (85.0, 160.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDL, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e117.3 \u0026plusmn; 34.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116.9 \u0026plusmn; 34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121.5 \u0026plusmn; 35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFBG, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108.9 \u0026plusmn; 34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108.4 \u0026plusmn; 33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e114.7 \u0026plusmn; 40.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUA, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.4 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.4 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.5 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBUN, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.7 \u0026plusmn; 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.7 \u0026plusmn; 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.7 \u0026plusmn; 4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCr, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eeGFR, ml/min per 1.73\u0026nbsp;m^2 (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92.7 \u0026plusmn; 14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.0 \u0026plusmn; 14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.2 \u0026plusmn; 14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHG (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.3 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.3 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.4 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHG-BMI (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125.9 \u0026plusmn; 55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125.3 \u0026plusmn; 57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e132.5 \u0026plusmn; 27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHG-WC (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e448.4 \u0026plusmn; 82.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e446.1 \u0026plusmn; 81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e473.7 \u0026plusmn; 88.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHG-WHtR (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.8 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.8 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.0 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFramingham, 10-year CVD risk% (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.2 \u0026plusmn; 4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.0 \u0026plusmn; 4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.2 \u0026plusmn; 4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCKM stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e291 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e281 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e748 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e717 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1567 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1469 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4230 (61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3794 (60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e436 (75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as: Number (%) for categorical variables. Mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD or median (IQR) for continuous variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between CHG and modified CHG indices with stroke incidence among people with CKM stage 0\u003c/strong\u003e\u003cstrong\u003e\u0026ndash;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3\u0026nbsp;presents the associations between the CHG index, its modified variants, and incident stroke. In continuous analysis, the baseline CHG index significantly predicted stroke risk per 1-SD increase, with effect estimates remaining consistent across progressively adjusted models (fully adjusted Model 3: HR 1.18, 95% CI: 1.07\u0026ndash;1.29; p = 0.001), indicating robustness to demographic, lifestyle, and metabolic confounders. Categorically, participants in the highest quartile (Q4) had a 41% higher risk than those in Q1 (HR 1.41, 95% CI: 1.08\u0026ndash;1.85; p = 0.011), demonstrating a dose-response relationship.\u003c/p\u003e\n\u003cp\u003eAmong the modified indices, CHG-WC showed a robust association both as a continuous variable (per 1-SD increase: HR 1.15, 95% CI: 1.05\u0026ndash;1.26; p = 0.003) and as a categorical variable (Q4 vs Q1: HR 1.52, 95% CI: 1.15\u0026ndash;2.02; p = 0.003). Furthermore, CHG-WHtR was significantly associated with stroke risk in continuous analysis (per 1-SD: HR 1.12, 95% CI: 1.03\u0026ndash;1.22; p = 0.008), with the highest quartile conferring a 59% increased risk (HR 1.59, 95% CI: 1.19\u0026ndash;2.11; p = 0.002). While CHG-BMI showed no significant association with stroke in either continuous (HR 1.04, 95% CI 0.96-1.13; p=0.382) or categorical analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Associations of CHG index and modified indices with stroke onset.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eHR (95%CI) \u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eHR (95%CI) \u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003cp\u003eHR (95%CI) \u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHG (per 1 SD) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21 (1.13~1.32) \u0026lt;0.001 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20 (1.11~1.30) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18 (1.07~1.29) 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHG quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1.00 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20 (0.91~1.57) 0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.10 (0.84~1.45) 0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04 (0.79~1.38) 0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.28 (0.99~1.64) 0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20 (0.93~1.55) 0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.13 (0.87~1.46) 0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.60 (1.26~2.04) \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.54 (1.20~1.97) 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41 (1.08~1.85) 0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHG-BMI (per 1 SD) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.06 (1.00~1.12) 0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.06 (1.00~1.13) 0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04 (0.96~1.13) 0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHG-BMI quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1.00 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.77~1.32) 0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03 (0.79~1.36) 0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.73~1.28) 0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.36 (1.05~1.75) 0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.43 (1.10~1.86) 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.26 (0.97~1.65) 0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.25 (0.98~1.60) 0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.31 (1.01~1.70) 0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08 (0.82~1.41) 0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHG-WC (per 1 SD) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.22 (1.13~1.32) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21 (1.11~1.31) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15 (1.05~1.26) 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHG-WC quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1.00 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.46 (1.10~1.94) 0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.44 (1.08~1.91) 0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.38 (1.03~1.83) 0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.73 (1.33~2.26) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.73 (1.32~2.26) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.61 (1.23~2.11) 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.86 (1.44~2.40) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.79 (1.38~2.33) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.52 (1.15~2.02) 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHG-WHtR (per 1 SD) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18 (1.11~1.26) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.17 (1.09~1.26) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12 (1.03~1.22) 0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1.00 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.54 (1.16~2.05) 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.55 (1.17~2.06) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.47 (1.11~1.96) 0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.00 (1.53~2.60) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.00 (1.52~2.62) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.81 (1.37~2.38) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.87 (1.45~2.43) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.85 (1.41~2.42) \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.59 (1.19~2.11) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: non-adjusted; Model 2: adjusted for age, gender, residence, education levels, marital status, smoking and drinking status; Model 3: adjusted for factors in model 2 and hypertension, diabetes, BUN, UA, and eGFR.\u003c/p\u003e\n\u003cp\u003eFigure 2 further depicts RCS analyses of CHG, CHG-WC, and CHG-WHtR in relation to incident stroke risk across the CKM stage 0-3 population. All indices demonstrated linear associations with stroke risk (P for non-linearity: CHG = 0.595, CHG-WC = 0.887, CHG-WHtR = 0.513). Kaplan\u0026ndash;Meier analyses in Figure 3 revealed dose-dependent gradients in cumulative stroke incidence across quartiles of CHG, CHG-WC, and CHG-WHtR. For these indices, participants in Q4 consistently demonstrated the highest event rates, whereas Q1 exhibited the lowest risk trajectories. Statistically significant separation between quartile curves was confirmed by adjusted direct tests (all P\u0026lt; 0.005). ROC curve analysis (Figure 4) revealed that CHG-WC had the strongest discrimination for new-onset stroke (AUC = 0.6528), followed by CHG (AUC = 0.6504) and CHG-WHtR(AUC = 0.6493).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analyses were conducted to explore the relationships between the CHG, CHG-WC, and CHG-WHtR indices and stroke incidence across populations stratified by age, gender, and CKM stage (Table 4). The analyses revealed significant age-dependent associations, with all CHG indices demonstrating robust stroke risk predictions in participants aged \u0026lt; 60 years (CHG: HR 1.27, 95% CI 1.13\u0026ndash;1.42; CHG-WC: HR 1.32, 95% CI 1.16\u0026ndash;1.50; CHG-WHtR: HR 1.22, 95% CI 1.12\u0026ndash;1.33; all P\u0026lt; 0.001). Although non-significant in participants \u0026ge;60 years (p \u0026gt; 0.05), the CHG, CHG-WC, and CHG-WHtR indices showed effect estimates consistent with those in younger cohorts (\u0026lt;60 years), suggesting that the underlying biological associations may persist across the lifespan. No evidence of effect modification by gender was observed (P for interaction \u0026gt; 0.05), with consistent positive associations in both males (HRs 1.16\u0026ndash;1.18, all P\u0026lt; 0.05) and females (HRs 1.14\u0026ndash;1.18, all P\u0026lt; 0.05). When stratified by CKM stage, only stage 3 participants showed significant associations: CHG (HR 1.18, P=0.001), CHG-WC (HR 1.14, P=0.011), and CHG-WHtR (HR 1.11, P=0.027). The absence of significant findings in stages 0\u0026ndash;2 may reflect limited statistical power.\u003c/p\u003e\n\u003cp\u003eTable 4. Subgroup analyses of the relationship between CHG index and modified indices with stroke incidence in a population with CKM syndrome stages 0\u0026ndash;3.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eNumber of participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eCHG\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCHG-WC\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCHG-WHtR\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.27 (1.13~1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.32 (1.16~1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.22 (1.12~1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\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: 86px;\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.09 (0.97~1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.06 (0.95~1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.06 (0.94~1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\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: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.18 (1.06~1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.17 (1.04~131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.16 (1.02~1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\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: 86px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.14 (1.01~1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.18 (1.04~1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.16 (1.04~1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\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: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKM stages\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSrage 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e4.28 (0.63~29.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.04 (0.30~3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.18 (0.33~4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\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: 86px;\"\u003e\n \u003cp\u003eStage 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.13 (0.97~4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.04 (0.30~3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.18 (0.33~4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\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: 86px;\"\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.03 (0.83~1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.15 (0.94~1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.16 (0.94~1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\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: 86px;\"\u003e\n \u003cp\u003eStage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.18 (1.07~1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.14 (1.03~1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.11 (1.01~1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: CHG and modified CHG indices (CHG-WC and CHG-WHtR) were analyzed as continuous variable (per 1 SD).\u003c/p\u003e\n\u003cp\u003eThe models were adjusted for age, gender, residence, education, marital status, smoking, drinking, hypertension, diabetes, BUN, UA, and eGFR.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn 6,836 adults with CKM stages 0–3, the CHG index and its variants (CHG-WC and CHG-WHtR) were independently associated with incident stroke, whereas CHG-BMI was not. Each 1-SD increase in the CHG index was associated with an 18% higher risk of stroke (HR 1.18, 95% CI 1.07–1.29). CHG-WHtR demonstrated the strongest association (quantile Q4 vs. Q1: HR 1.59, 95% CI 1.19–2.11). RCS analyses indicated linear relationships of CHG, CHG-WC, and CHG-WHtR with stroke risk, and Kaplan-Meier curves showed progressively increasing cumulative incidence across increasing quartiles of these indices. Associations were most pronounced in participants aged \u0026lt;60 years and in CKM stage 3, underscoring the utility of these indices in metabolically active populations.\u003c/p\u003e\n\u003cp\u003eIR drives cardiovascular pathogenesis through increased arterial stiffness[21]. Notably, it accelerates atherosclerosis and plaque destabilisation independent of hyperglycaemia[22]. Constituents of the CKM syndrome are significantly associated with atherosclerotic risk. Accelerated atherosclerosis driven by IR may represent a mechanism for stroke development in early-stage CKM populations[2]. Emerging evidence suggests that systematic IR quantification and targeted interventions in CKM stages 0- 3, including individuals from risk-free states to subclinical cardiovascular pathology, may disrupt the pathophysiological cascade underlying cardiovascular events[23]. CHG and its obesity-integrated derivatives serve as significant markers for predicting IR[11,17,24].\u003c/p\u003e\n\u003cp\u003eOriginally introduced by Mansoori et al., the CHG index exhibits enhanced discriminative power for type 2 diabetes relative to the TyG index[11]; subsequent studies indicate it may also outperform the TyG index in predicting CVD risk[17]. Consistent with previous studies, we found a positive, linear dose-response relationship between both the CHG index and its derivative indices (CHG-WC and CHG-WHtR) with stroke incidence in the CKM stage 0–3 population. Previously, several indices—such as the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio[25,26], total cholesterol to HDL-C ratio (TC/HDL-C)[27], triglyceride-glucose (TyG) index[3,28], and homeostatic model assessment of insulin resistance (HOMA-IR)[29,30]—have been employed to estimate the risk of IR and stroke. Despite their proven value, these markers exhibit drawbacks such as variable accuracy across demographics, elevated expense, methodological intricacy, and unreliable replication in areas with limited resources[12]. By combining the routinely assayed parameters of total cholesterol, HDL-C, and fasting blood glucose, the CHG index forms a composite biomarker that captures derangements in both lipid and glucose metabolism. This unification potentially improves its predictive power and clinical utility[12].\u003c/p\u003e\n\u003cp\u003eNotably, while CHG, CHG-WC, and CHG-WHtR were significantly associated with stroke events, CHG-BMI showed no statistically significant relationship. Although the precise mechanisms remain unclear, this discrepancy may arise from the distinct pathophysiological correlates of abdominal obesity indicators (e.g., WC, WHtR) compared to general adiposity measures (e.g., BMI). The pathogenesis of CKM syndrome frequently involves adipose tissue excess, dysfunction, or a combination thereof[2]. Dysfunctional visceral fat triggers the hypersecretion of pro-inflammatory and pro-oxidant mediators, resulting in vascular, myocardial, and renal damage that ultimately raises CVD risk[31,32]. Although widely used, BMI and waist circumference are poor proxies for abdominal fat distribution. A Lancet review underscored the critical importance of synthesizing lipid profile data with abdominal adiposity metrics, such as WC[33]. Indeed, WC and WHtR are well-validated parameters for assessing abdominal obesity[34,35], and demonstrate superior predictive value over BMI for CVD incidence[36,37]. Therefore, the integration of the CHG index with abdominal obesity measures (CHG-WC and CHG-WHtR) reveals a synergistic risk phenotype that is more directly linked to the pathogenesis of stroke in early CKM stages. This finding reinforces current guidelines which emphasize that waist-centered anthropometric measures hold greater prognostic value than BMI in cardiometabolic risk stratification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe robustness of this study derives from three key attributes: (i) a nationally representative cohort of high CKM risk middle-aged and older Chinese adults, enhancing generalisability; (ii) concurrent evaluation of CHG-derived indices incorporating visceral adiposity surrogates (WC/WHtR), enabling integrated profiling of metabolic and anatomical stroke determinants; and (iii) a prospective design featuring rigorous covariate adjustment and subgroup validation, collectively supporting the robustness and consistency of our findings. However, several limitations merit acknowledgment. First, the single-baseline assessment of CHG-related indices precludes tracking longitudinal changes in metabolic profiles or adiposity redistribution, potentially underestimating cumulative exposure impact. Second, while prior CHARLS validation supports reasonable accuracy, stroke ascertainment predominantly through self-reports raises potential misclassification concerns. Third, generalizability beyond middle-aged and older Chinese individuals necessitates caution regarding younger or non-Chinese demographics. Finally, residual confounding from unmeasured variables remains plausible despite rigorous covariate adjustments characteristic of observational research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings indicate that the CHG index and its derivatives incorporating abdominal obesity indices (WC and WHtR) serve as valuable tools for predicting stroke incidence in individuals with early CKM stages (0\u0026ndash;3). Consequently, these indices could be useful for early identification and intervention in CKM syndrome.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData can be found in a publicly accessible repository. The datasets produced and analyzed in this study are available on the CHARLS homepage at http://charls.pku.edu.cn/en. CHARLS data have been de - identified, with participants being recognized by a distinct ID number.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research includes individuals as participants. Approval for the CHARLS survey initiative was granted by the Biomedical Ethics Committee at Peking University (IRB00001052-11015). All participants provided their informed consent in writing at the time of their involvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: Li Ke, Ying Li, Sili Jiang, Wenli Xing and Lei Zhao. Acquisition of data: Li Ke, Ying Li. Analysis and interpretation of data: Li Ke, Ying Li, Sili Jiang, Wenli Xing and Lei Zhao. Li Ke, Ying Li and Wenli Xing wrote of the manuscript. All authors have revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that there was no financial assistance obtained for the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to the National Institute on Aging's Behavioral and Social Research Division, the Natural Science Foundation of China, the World Bank, and Peking University for their financial support. We also express our appreciation to the CHARLS research and field teams, as well as all study participants, for their invaluable contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNdumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation. 2023;148:1606\u0026ndash;35. https://doi.org/10.1161/cir.0000000000001184\u003c/li\u003e\n \u003cli\u003eNdumele CE, Neeland IJ, Tuttle KR, Chow SL, Mathew RO, Khan SS, et al. A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome: A Scientific Statement From the American Heart Association. Circulation. 2023;148:1636\u0026ndash;64. https://doi.org/10.1161/cir.0000000000001186\u003c/li\u003e\n \u003cli\u003eHong J, Zhang R, Tang H, Wu S, Chen Y, Tan X. Comparison of triglyceride glucose index and modified triglyceride glucose indices in predicting cardiovascular diseases incidence among populations with cardiovascular-kidney-metabolic syndrome stages 0\u0026ndash;3: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24. https://doi.org/10.1186/s12933-025-02662-3\u003c/li\u003e\n \u003cli\u003eBryazka D, Reitsma MB, Abate YH, Al Magied AHAA, Abdelkader A, Abdollahi A, et al. Forecasting the effects of smoking prevalence scenarios on years of life lost and life expectancy from 2022 to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Public Health [Internet]. 2024;9:e729\u0026ndash;44. https://doi.org/10.1016/s2468-2667(24)00166-x\u003c/li\u003e\n \u003cli\u003eYue Y, Li P, Sun Z, Murayama R, Li Z, Hashimoto K, et al. Association of novel triglyceride-glucose-related indices with incident stroke in early-stage cardiovascular-kidney-metabolic syndrome. Cardiovasc Diabetol. 2025;24. https://doi.org/10.1186/s12933-025-02854-x\u003c/li\u003e\n \u003cli\u003eBornfeldt KE, Tabas I. Insulin Resistance, Hyperglycemia, and Atherosclerosis. Cell Metab. 2011;14:575\u0026ndash;85. https://doi.org/10.1016/j.cmet.2011.07.015\u003c/li\u003e\n \u003cli\u003eDuan M, Zhao X, Li S, Miao G, Bai L, Zhang Q, et al. Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001\u0026ndash;2018. Cardiovasc Diabetol. 2024;23. https://doi.org/10.1186/s12933-024-02334-8\u003c/li\u003e\n \u003cli\u003eLu Z, Xiong Y, Feng X, Yang K, Gu H, Zhao X, et al. Insulin resistance estimated by estimated glucose disposal rate predicts outcomes in acute ischemic stroke patients. Cardiovasc Diabetol. 2023;22. https://doi.org/10.1186/s12933-023-01925-1\u003c/li\u003e\n \u003cli\u003eZhang Q, Xiao S, Jiao X, Shen Y. The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001\u0026ndash;2018. Cardiovasc Diabetol. 2023;22. https://doi.org/10.1186/s12933-023-02030-z\u003c/li\u003e\n \u003cli\u003eWu Y, Yang Y, Zhang J, Liu S, Zhuang W. The change of triglyceride-glucose index may predict incidence of stroke in the general population over 45 years old. Cardiovasc Diabetol. 2023;22. https://doi.org/10.1186/s12933-023-01870-z\u003c/li\u003e\n \u003cli\u003eMansoori A, Nosrati M, Dorchin M, Mohammadyari F, Derakhshan‐Nezhad E, Ferns G, et al. A novel index for diagnosis of type 2 diabetes mellitus: Cholesterol, High density lipoprotein, and Glucose (CHG) index. J Diabetes Investig. 2024; https://doi.org/10.1111/jdi.14343\u003c/li\u003e\n \u003cli\u003eZhang Z, Chen M, Cai D, Fan M, Wang Y. Association between the cholesterol, high-density lipoprotein, and glucose index and type 2 diabetes mellitus prevalence in US adults: A cross-sectional study based on National Health and Nutrition Examination Survey 2009-2018. . 2025;53:3000605251375557\u0026ndash;3000605251375557. https://doi.org/10.1177/03000605251375557\u003c/li\u003e\n \u003cli\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2012;43:61\u0026ndash;8. https://doi.org/10.1093/ije/dys203\u003c/li\u003e\n \u003cli\u003eD\u0026rsquo;Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General Cardiovascular Risk Profile for Use in Primary Care. Circulation. 2008;117:743\u0026ndash;53. https://doi.org/10.1161/circulationaha.107.699579\u003c/li\u003e\n \u003cli\u003eChen TK, Knicely DH, Grams ME. Chronic Kidney Disease Diagnosis and Management. JAMA. 2019;322:1294. https://doi.org/10.1001/jama.2019.14745\u003c/li\u003e\n \u003cli\u003eMa Y-C, Zuo L, Chen J-H, Luo Q, Yu X-Q, Li Y, et al. Modified Glomerular Filtration Rate Estimating Equation for Chinese Patients with Chronic Kidney Disease. JASN. 2006;17:2937\u0026ndash;44. https://doi.org/10.1681/asn.2006040368\u003c/li\u003e\n \u003cli\u003eMo D, Zhang P, Zhang M, Dai H, Guan J. Cholesterol, high-density lipoprotein, and glucose index versus triglyceride\u0026ndash;glucose index in predicting cardiovascular disease risk: a cohort study. Cardiovasc Diabetol. 2025;24. https://doi.org/10.1186/s12933-025-02675-y\u003c/li\u003e\n \u003cli\u003eUnger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 International Society of Hypertension Global Hypertension Practice Guidelines. Hypertension [Internet]. 2020;75:1334\u0026ndash;57. https://doi.org/10.1161/hypertensionaha.120.15026\u003c/li\u003e\n \u003cli\u003eHuang C, You H, Zhang Y, Fan L, Feng X, Shao N. Association between the hemoglobin A1c/High-density lipoprotein cholesterol ratio and stroke incidence: a prospective nationwide cohort study in China. Lipids Health Dis. 2025;24. https://doi.org/10.1186/s12944-025-02438-4\u003c/li\u003e\n \u003cli\u003eZhang X, Loberiza FR, Klein JP, Zhang M-J. A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model. Comput Methods Programs Biomed. 2007;88:95\u0026ndash;101. https://doi.org/10.1016/j.cmpb.2007.07.010\u003c/li\u003e\n \u003cli\u003eGast KB, Tjeerdema N, Stijnen T, Smit JWA, Dekkers OM. Insulin Resistance and Risk of Incident Cardiovascular Events in Adults without Diabetes: Meta-Analysis. PLoS One. 2012;7:e52036. https://doi.org/10.1371/journal.pone.0052036\u003c/li\u003e\n \u003cli\u003eDeFronzo RA. Insulin resistance, lipotoxicity, type 2 diabetes and atherosclerosis: the missing links. The Claude Bernard Lecture 2009. Diabetologia. 2010;53:1270\u0026ndash;87. https://doi.org/10.1007/s00125-010-1684-1\u003c/li\u003e\n \u003cli\u003eZhang H, Huang S, Fang Y, Zhang H, Yang W, Yu M. Insulin resistance assessed by estimated glucose disposal rate predicts cardiovascular disease in stages 0\u0026ndash;3 of cardiovascular-kidney-metabolic syndrome: a UK biobank cohort study. Cardiovasc Diabetol. 2025;24. https://doi.org/10.1186/s12933-025-02860-z\u003c/li\u003e\n \u003cli\u003e\u0026Ccedil;atak M, Konuk ŞG, Hepsen S. The cholesterol‐HDL‐glucose (CHG) index and traditional adiposity markers in predicting diabetic retinopathy and nephropathy. J Diabetes Investig. 2025; https://doi.org/10.1111/jdi.70086\u003c/li\u003e\n \u003cli\u003eChe B, Zhong C, Zhang R, Pu L, Zhao T, Zhang Y, et al. Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc Diabetol. 2023;22. https://doi.org/10.1186/s12933-023-01762-2\u003c/li\u003e\n \u003cli\u003eZhang S, Cao C, Han Y, Hu H, Zheng X. A nonlinear relationship between the triglycerides to high-density lipoprotein cholesterol ratio and stroke risk: an analysis based on data from the China Health and Retirement Longitudinal Study. Diabetol Metab Syndr. 2024;16. https://doi.org/10.1186/s13098-024-01339-3\u003c/li\u003e\n \u003cli\u003eXiao B, Cao C, Han Y, Yang F, Hu H, Luo J. A non-linear connection between the total cholesterol to high-density lipoprotein cholesterol ratio and stroke risk: a retrospective cohort study from the China Health and Retirement Longitudinal Study. Eur J Med Res [Internet]. 2024;29. https://doi.org/10.1186/s40001-024-01769-9\u003c/li\u003e\n \u003cli\u003eWu M, Li C, Yu Y, Zeng L, Qiu Y, Liu J, et al. Association between the triglyceride-glucose (TyG) index and stroke risk in Chinese normal-weight adults: a population-based study. Diabetol Metab Syndr. 2024;16. https://doi.org/10.1186/s13098-024-01421-w\u003c/li\u003e\n \u003cli\u003eYang X, Wang G, Jing J, Wang A, Zhang X, Jia Q, et al. Association of triglyceride-glucose index and stroke recurrence among nondiabetic patients with acute ischemic stroke. BMC Neurol. 2022;22. https://doi.org/10.1186/s12883-022-02588-3\u003c/li\u003e\n \u003cli\u003eJing J, Pan Y, Zhao X, Zheng H, Jia Q, Mi D, et al. Insulin Resistance and Prognosis of Nondiabetic Patients With Ischemic Stroke. Stroke [Internet]. 2017;48:887\u0026ndash;93. https://doi.org/10.1161/strokeaha.116.015613\u003c/li\u003e\n \u003cli\u003eBhupathiraju SN, Hu FB. Epidemiology of Obesity and Diabetes and Their Cardiovascular Complications. Circ Res. 2016;118:1723\u0026ndash;35. https://doi.org/10.1161/circresaha.115.306825\u003c/li\u003e\n \u003cli\u003eRana MN, Neeland IJ. Adipose Tissue Inflammation and Cardiovascular Disease: An Update. Curr Diab Rep [Internet]. 2022;22:27\u0026ndash;37. https://doi.org/10.1007/s11892-021-01446-9\u003c/li\u003e\n \u003cli\u003eNeeland IJ, Ross R, Despr\u0026eacute;s J-P, Matsuzawa Y, Yamashita S, Shai I, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 2019;7:715\u0026ndash;25. https://doi.org/10.1016/s2213-8587(19)30084-1\u003c/li\u003e\n \u003cli\u003eAshwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 2005;56:303\u0026ndash;7. https://doi.org/10.1080/09637480500195066\u003c/li\u003e\n \u003cli\u003eKahn BB, Flier JS. Obesity and insulin resistance. J Clin Invest. 2000;106:473\u0026ndash;81. https://doi.org/10.1172/jci10842\u003c/li\u003e\n \u003cli\u003eAshwell M, Gunn P, Gibson S. Waist‐to‐height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta‐analysis. Obes Rev. 2011;13:275\u0026ndash;86. https://doi.org/10.1111/j.1467-789x.2011.00952.x\u003c/li\u003e\n \u003cli\u003eWang L, Lee Y, Wu Y, Zhang X, Jin C, Huang Z, et al. A prospective study of waist circumference trajectories and incident cardiovascular disease in China: the Kailuan Cohort Study. Am J Clin Nutr. 2020;113:338\u0026ndash;47. https://doi.org/10.1093/ajcn/nqaa331\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CKM syndrome, Stroke, CHG, Modified CHG indices, China Health and Retirement Longitudinal Study","lastPublishedDoi":"10.21203/rs.3.rs-7746070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7746070/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCardiovascular-kidney-metabolic (CKM) syndrome is a major health burden. Stroke, the third leading cause of death globally, is strongly linked to insulin resistance (IR). The novel cholesterol, high-density lipoprotein (HDL), and glucose (CHG) index has been shown to have superior diagnostic accuracy for diabetes, but its association with stroke in early CKM syndrome (stages 0\u0026ndash;3) is unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis nationwide prospective cohort study included 6,836 adults with CKM syndrome stages 0\u0026ndash;3 from the CHARLS (2011\u0026ndash;2020). Multivariable Cox models assessed associations between baseline CHG indices (and modified variants) and incident stroke. Dose-response relationships were evaluated using restricted cubic splines (RCS) and Kaplan-Meier analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOver a 9-year follow-up period, 575 incident stroke cases were documented. Per 1-SD increase, the CHG index (HR\u0026thinsp;=\u0026thinsp;1.18, 95% CI:1.07\u0026ndash;1.29), CHG-WC (HR\u0026thinsp;=\u0026thinsp;1.15, 1.05\u0026ndash;1.26), and CHG-WHtR (HR\u0026thinsp;=\u0026thinsp;1.12, 1.03\u0026ndash;1.22) were independently associated with stroke. Quartile analysis revealed the strongest association for CHG-WHtR (Q4 vs. Q1: HR\u0026thinsp;=\u0026thinsp;1.59, 95% CI: 1.19\u0026ndash;2.11). Dose-response relationships were linear. Subgroup analyses indicated enhanced predictive utility in participants aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years and those with CKM stage 3. No significant association was observed between CHG-BMI and stroke incidence.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe​​ CHG index and its derivatives incorporating abdominal obesity indices (WC and WHtR) ​​robustly predict​​ incident stroke in early-stage CKM syndrome; their clinical adoption may enhance early detection and prevention of stroke events in ​​populations with vulnerable metabolism​.\u003c/p\u003e","manuscriptTitle":"Associations Between the CHG Index, Its Modified Versions, and Incident Stroke in Early-Stage CKM Syndrome: A Nationwide Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 23:41:47","doi":"10.21203/rs.3.rs-7746070/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-04T00:36:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T13:29:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291148724780978778058991007076259182172","date":"2025-10-30T03:41:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T08:56:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258560028158671193082382416303839119801","date":"2025-10-25T17:04:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274275085804854360368063495660962406746","date":"2025-10-23T08:11:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230801306669160313060220265534381002374","date":"2025-10-19T05:36:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T23:53:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T17:32:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-06T14:49:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-09-30T01:37:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e78f0ddb-0ef7-4c18-9923-b83ce0edd049","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:03:44+00:00","versionOfRecord":{"articleIdentity":"rs-7746070","link":"https://doi.org/10.1186/s40001-025-03808-5","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2026-01-06 15:58:14","publishedOnDateReadable":"January 6th, 2026"},"versionCreatedAt":"2025-10-21 23:41:47","video":"","vorDoi":"10.1186/s40001-025-03808-5","vorDoiUrl":"https://doi.org/10.1186/s40001-025-03808-5","workflowStages":[]},"version":"v1","identity":"rs-7746070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7746070","identity":"rs-7746070","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}