Association of triglyceride-glucose index and its related parameters with atherosclerotic cardiovascular disease: evidence from a 15-year follow-up of Kailuan cohort

Association of triglyceride-glucose index and its related parameters with atherosclerotic cardiovascular disease: evidence from a 15-year follow-up of Kailuan cohort | 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 Association of triglyceride-glucose index and its related parameters with atherosclerotic cardiovascular disease: evidence from a 15-year follow-up of Kailuan cohort Xue Xia, Shuohua Chen, Xue Tian, Qin Xu, Yijun Zhang, Xiaoli Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4157386/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jun, 2024 Read the published version in Cardiovascular Diabetology → Version 1 posted 11 You are reading this latest preprint version Abstract Background Triglyceride glucose (TyG) index and its related parameters has been introduced as cost-effective surrogate indicators of insulin resistance, while prospective evidence on their effects on risk of atherosclerotic cardiovascular disease (ASCVD) remained scattered and inconsistent. We aimed to evaluate the association of TyG with new-onset ASCVD and further compare their predictive capacity. Method A total of 95,342 ASCVD-free participants were enrolled from the Kailuan study. TyG and its related parameters were defined by fasting blood glucose, triglyceride, BMI, WC and WHtR. The primary outcome was incident ASCVD, comprising myocardial infarction (MI) and ischemic stroke (IS). Cox proportional hazard models and restricted cubic spline analyses were adopted to investigate the association between each index and ASCVD. The C-index, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used for comparison of their predictive value for ASCVD. Results During a median follow-up of 15.0 years, 8,031 new cases of ASCVD were identified. The incidence rate of ASCVD increased along with elevated levels of each index, and the relationships were found to be nonlinear in the RCS analyses. The hazard ratio (HR) and 95% confidence interval (95% CI) for ASCVD was 1.39 (1.35, 1.43), 1.46 (1.41, 1.50), 1.50 (1.46, 1.55), and 1.52 (1.48, 1.57) per 1 IQR increase of baseline TyG, TyG-BMI, TyG-WC, and TyG-WHtR, respectively, and the association were more pronounced for females and younger individuals aged <60 years ( P for interaction <0.05). Using the updated mean or time-varying measurements instead of baseline indicators did not significantly alter the primary findings. Additionally, TyG-WC and TyG-WHtR showed better performance in predicting risk of ASCVD than TyG, with the IDI (95% CI) of (0.001, 0.004) and 0.004 (0.001, 0.004) and the category-free NRI (95% CI) of 0.120 (0.025, 0.138) and 0.143 (0.032, 0.166), respectively. Similar findings were observed for MI and IS. Conclusions Both the TyG index and its related parameters were significantly and positively associated with ASCVD. TyG-WC and TyG-WHtR had better performance in predicting incident ASCVD than TyG, which might be more suitable indices for risk stratification and enhance the primary prevention of ASCVD. Triglyceride glucose (TyG) Triglyceride glucose-body mass index (TyG-BMI) Triglyceride glucose-waist circumference (TyG-WC) Triglyceride glucose-waist-height ratio (TyG-WHtR) Atherosclerotic cardiovascular disease (ASCVD) Figures Figure 1 Figure 2 Background Despite remarkable advance in prevention strategies and therapeutic techniques, the burden of atherosclerotic cardiovascular disease (ASCVD) remains its decades-long rise for almost all non-high-income countries, and has becoming the leading cause of mortality and health impairment worldwide. [ 1 ] It was estimated that ASCVD accounted for 22% of total deaths and 245.5 million disability-adjusted life years (DALYs) in 2019, for which metabolic dysfunction was acknowledged as a predominant contributor. [ 2 , 3 ] Therefore, optimization of existing strategies to identify and control metabolic risk factors is essential for better prevention of ASCVD. Impaired insulin sensitivity, also known as insulin resistance (IR), contributes to the pathophysiology of diabetes and serves as a crucial element contributing to ASCVD.[ 4 ] The triglyceride glucose (TyG), defined merely based on fasting blood glucose (FBG) and triglyceride (TG), has gradually been used as a convenient and reliable alternative index for the consuming and expensive hyperinsulinemic-euglycemic clamp technique, which remains the gold standard for assessing insulin sensitivity.[ 5 , 6 ] Moreover, several studies revealed the association of TyG index with risks of hypertension, myocardial infarction (MI), and ischemic stroke (IS), some of which further confirmed its clinical value in predicting ASCVD. [ 7 – 9 ] Fat mass and central obesity also play an important role in the progress of atherogenic endothelial dysfunction partly through metabolic disorders, which ultimately lead to the occurrence of ASCVD. [ 10 , 11 ] Thus, TyG combined with obesity indices like body mass index (BMI), waist circumference (WC) and waist-to-height ratio (WHtR), were introduced as new surrogates for IR and has been proved to be associated with a series of metabolic diseases encompassing diabetes mellitus, metabolic syndrome, hypertension, and non-alcoholic fatty liver disease. [ 12 – 15 ] More recently, epidemiological evidence has suggested that those TyG related parameters are closely related to ASCVD and some even showed better prediction ability over TyG, while the evidence was scattered and most of them were drawn from cross-sectional designed studies, leaving the conclusion inconsistent. [ 16 – 19 ] With regards to the above, the current analysis was conducted based on the Kailuan cohort to investigate the association of TyG, TyG-BMI, TyG-WC, and TyG-WHtR with ASCVD and its subtypes, using the updated mean and time-varying indicators instead of baseline measurements in the sensitivity analyses for further verification. Additionally, we compared the predictive value of the TyG and its related parameters for ASCVD by calculating C-index, integrated discrimination improvement (IDI), and category-free net reclassification index (NRI). Methods Study design and participants The study population was derived from the Kailuan study, a community-based prospective cohort study conducted in Northern China, which has been fully described in previous publications. [ 20 – 22 ] In brief, it was established during 2006–2007, recruiting 101,510 participants aged 18–98 years from the Kailuan company. The subsequent visits were performed every two years until December 2021. The study complied with the Declaration of Helsinki and was approved by the Ethics Committees of both Kailuan General Hospital and Beijing Tiantan Hospital. Written informed consent was obtained before data collection. A total of 98,191 individuals free from ASCVD at baseline were initially included in the current analysis. We further excluded those with missing baseline records on triglyceride (N = 66), fasting blood glucose (N = 567), body weight (N = 35), body height (N = 45), or waist circumference (N = 2,136), reducing the final sample size to 95,342 ( Supplementary Fig. 1 ). Data collection and definitions Participants received face-to-face interview, laboratory tests and physical examination during both baseline survey and the following biennial visits. Standardized questionnaires were adopted to collect information on age, gender, sociodemographic characteristics (household income, education attainment and marriage status), health-related life styles (smoking, alcohol intake and physical activity), personal and family medical histories. Blood samples were collected in the morning after an overnight fasting and analyzed with the Hitachi 747 automated analyzer (Tokyo, Japan). Body weight, height, WC and blood pressure were measured by trained medical professionals when participants attended the physical examination. Current smoking or drinking were defined according to self-reported cigarette or alcohol consumption during the past 12 months. Individuals who exercised more than four times per week (≥ 20 minutes/time) were classified as active physical activity. Family history of ASCVD was defined as at least one of their parents diagnosed with MI or IS. Hypertension was identified based on systolic/diastolic blood pressure ≥ 140 /90 mmHg, self-reported history of hypertension or antihypertensive medication usage. Diabetes was defined using fasting blood glucose ≥ 7.0 mmol/L, self-reported history of diabetes, treatment with oral hypoglycemic agents or insulin injection. Elevated concentration of total (≥ 6.2 mmol/L) or low-density lipoprotein cholesterol (≥ 4.1 mmol/L), or use of lipid-lowering medication was considered as hyperlipidemia. Calculation of TyG and its related parameters The TyG index was calculated as ln (fasting triglyceride [mg/dL] × fasting blood glucose [mg/dL]/2). BMI was calculated as body mass (kg) divided by height squared (m 2 ), and WHtR was defined as the ratio of WC (cm) to height (cm). Accordingly, we calculated Tyg-BMI as TyG × BMI, TyG-WC as TyG × WC and TyG-WHtR as TyG ×WHtR.[ 18 ] In addition to baseline measurements, the updated mean of each indicator was further calculated with all available data from baseline to the latest survey before occurrence of ASCVD or to the end of follow-up. Assessment of outcomes Information on disease information and vital status was primarily obtained through face-to-face interviews, biennially updated till the date of death or the latest follow-up visit, whichever came first. Moreover, medical records from the municipal social insurance institution, discharge summaries from local hospitals, and death certificates from provincial vital statistics offices were also checked for additional validation of diagnosis. [ 23 ] All causes of events were recorded with the 10th version of the International Classification of Diseases (ICD-10). The primary outcome for this analysis was the first occurrence of ASCVD, a composite of MI and IS, and both of them were further analyzed separately as secondary outcomes. According to the World Health Organization criteria, MI (ICD-10 I21-I23 ) was identified according to clinical symptom, electrocardiographic manifestation, and concentration of myocardial enzymes; [ 24 ] and IS (ICD-10 I63 ) was diagnosed on the basis of clinical signs combined with neuroimages from brain computed tomography or magnetic resonance imaging. [ 25 ] Statistical analyses Baseline characteristics were described as median (inter-quartile range, IQR) for continuous variables and as frequency (%) for categorical variables, stratified by quartiles of baseline TyG index and its related parameters. With time-on-study as timescale, the person-years of follow-up was calculated from the completion of baseline survey to either the date of ASCVD, death, or the most recent visit, whichever came first. The Cox proportional hazards regression model was applied to estimate the hazard ratio (HR) and 95% confidence interval (95% CI) for ASCVD and its subtypes. The proportional hazard assumption was evaluated by the weighted Schoenfeld residuals, [ 26 ] and no violations were observed. Two sets of models were constructed to adjust for potential confounding factors. Model 1 was fitted with a primary adjustment for age and gender; in Model 2, we additionally controlled monthly income, education level, marital status, current smoking, current drinking, physical activity, and family history of ASCVD. To explore the potential nonlinear dose-response relationship of TyG index and its related parameters with ASCVD, restricted cubic splines (RCS) were fitted by adjusting for covariates in Model 2, with reference set at the 50th percentile and four knots at the 5th, 35th, 65th, and 95th percentiles of the distribution. Subgroup analyses by baseline characteristics were conducted for the primary outcome based on Model 2 to investigate possible effect modifications. The multiplicative interaction was assessed by introducing a cross-product term, and the P value was calculated using the likelihood ratio test. To further explore the potential impact of reverse causality, we repeated the primary analysis using a 2-year lag period by excluding ASCVD events that occurred during the initial two years of follow-up. Other sensitivity analyses involved 1) using the updated mean instead of baseline measurements of TyG index and its related parameters; 2) additionally adjusting for hypertension, diabetes and hyperlipidemia; 3) adopting the Cox regression model with time-varying variables updated in each survey before the occurrence of ASCVD events; 4) fitting the Fine & Gary sub-distribution hazard models to further consider the competing risk of non-ASCVD deaths. Moreover, C-index, IDI, and NRI were computed to quantify and compare the predictive powers of TyG, TyG-BMI, TyG-WC, and TyG-WHtR. The above assessments were performed with SAS version 9.4 (SAS Institute Inc, Cary, NC, USA) and R version 4.1.2 (R Core Team, Vienna, Austria). A two-sided P value < 0.05 was considered to be statistically significant. Results Baseline characteristics of the participants Of 95,342 eligible participants included, the median age at recruitment was 59.0 years and 75,880 (79.6%) were males. The baseline characteristics stratified by TyG index were shown in Table 1 . In comparison with individuals at the lowest quartile of TyG, individuals with higher TyG levels had greater probabilities to be male and less well-educated. Moreover, they were more likely to be diagnosed with hypertension, diabetes and hyperlipidemia, while no significant difference was found in their family history of ASCVD. Similar tendency was also observed across quartiles of TyG-BMI, TyG-WC, and TyG-WHtR ( Supplementary Table 1–3 ). Table 1 Baseline characteristics of participants according to TyG quartiles. Overall Quartiles of TyG Quartile 1 Quartile 2 Quartile 3 Quartile 4 Range of TyG < 8.2 8.2–8.6 8.6–9.1 ≥ 9.1 No. of participants 95342 23835 23835 23832 23840 Age, years, Median (IQR) 59.0 (51.0,66.2) 57.9 (49.0,66.0) 59.0 (51.0,66.3) 59.2 (52.0,67.0) 59.0 (52.0,66.0) Male, N (%) 75880 (79.6) 17556 (73.7) 18889 (79.2) 19355 (81.2) 20080 (84.2) High school or above, N (%) 18674 (19.6) 5642 (23.7) 4469 (18.7) 4430 (18.6) 4133 (17.3) Monthly income ≥ 600 CNY, N (%) 6151 (6.6) 1676 (7.3) 1461 (6.3) 1499 (6.5) 1515 (6.5) Married, N (%) 87453 (94.4) 21339 (92.6) 22061 (94.9) 21956 (94.8) 22097 (95.4) Current smoking, N (%) 32037 (34.3) 7733 (33.4) 7490 (32.0) 8039 (34.4) 8775 (37.6) Current drinking, N (%) 35028 (37.5) 8692 (37.5) 8184 (35.0) 8710 (37.3) 9442 (40.4) Active physical activity, N (%) 14022 (15.2) 3673 (16.0) 3381 (14.6) 3555 (15.4) 3413 (14.8) Family history of ASCVD, N (%) 10728 (11.3) 2636 (11.1) 2556 (10.7) 2782 (11.7) 2754 (11.6) Hypertension, N (%) 32287 (34.0) 5369 (22.6) 7327 (30.9) 8902 (37.5) 10689 (45.0) Diabetes, N (%) 8629 (9.1) 258 (1.1) 606 (2.5) 1774 (7.4) 5991 (25.1) Hyperlipidemia, N (%) 12113 (12.7) 1422 (6.0) 2346 (9.8) 3421 (14.4) 4924 (20.7) TyG-BMI, Median (IQR) 214.4 (189.0,242.2) 181.6 (164.8,200.3) 204.7 (187.4,223.6) 223.6 (205.1,243.6) 251.3 (230.2,274.4) TyG-WC, Median (IQR) 747.1 (671.6,830.2) 646.7 (593.1,703.5) 715.3 (666.2,767.5) 773.3 (720.8,830.1) 863.1 (802.6,929.7) TyG-WHtR, Median (IQR) 4.5 (4.0,5.0) 3.9 (3.6,4.2) 4.3 (4.0,4.6) 4.6 (4.3,5.0) 5.1 (4.8,5.5) Values were represented by median (IQR) for continuous variables and frequency (percentage) for categorical variables. Abbreviations: ASCVD, atherosclerotic cardiovascular diseases; BMI, body mass index; IQR, interquartile range; TyG, triglyceride-glucose; WC, waist circumference; WHtR, Waist-to-Height ratio. Association of TyG and its related parameters with ASCVD Over a median (IQR) follow-up of 15.0 (14.7, 15.2) years, 8,031 (8.4%) cases of ASCVD were identified, including 1,909 (2.0%) MI and 6,353 (6.7%) IS. The crude incidence rate of ASCVD increased substantially with the level of TyG and its related parameters. For instance, it ranged from 407.04 to 847.46 per 100,000 person-years across quartiles of TyG and from 328.77 to 945.00 per 100,000 person-years across quartiles of TyG-WHtR (Table 2 ). After adjustment for potential confounding factors in Model 2, higher levels of TyG and its related parameters were still related to elevated risks of ASCVD. More intuitively, RCS curves showed significant positive associations of those indicators with ASCVD and its subtypes, most of which exhibited a nonlinear monotone increase ( Supplementary Fig. 2 ). Per 1 IQR increase in TyG was associated with a 39% higher risk (HR:1.39; 95%CI: 1.35, 1.43) of ASCVD, and the corresponding estimates were even greater for those TyG-related parameters, reaching 1.46 (1.41, 1.50) for TyG-BMI, 1.50 (1.46, 1.55) for TyG-WC, and 1.52 (1.48, 1.57) for TyG-WHtR. In the subtype analyses, similar results were yielded for MI and IS. TyG-WC and TyG-WHtR showed the highest association with MI and IS, respectively. Compared with the lowest quartile, the HR (95%CI) of MI was 1.66 (1.39, 1.97), 2.15 (1.82, 2.54), and 3.05 (2.60, 3.58) for the second, third and fourth quartile of TyG-WC; the HR (95%CI) of IS was 1.35 (1.24, 1.47), 1.64 (1.51, 1.78), and 2.20 (2.04, 2.38) for the second, third and fourth quartile of TyG-WHtR. Table 2 Associations of TyG and its related parameters with atherosclerotic cardiovascular disease and its subtypes. Quartiles Continuous Quartile 1 Quartile 2 Quartile 3 Quartile 4 Per 1 IQR Increase ASCVD TyG Cases/Total 1356/23835 1798/23835 2157/23832 2720/23840 Incidence Rate a 407.04 546.31 661.23 847.46 Model 1, HR (95%CI) b Reference 1.28 (1.20, 1.38) 1.53 (1.43, 1.64) 2.00 (1.87, 2.13) 1.39 (1.36, 1.43) Model 2, HR (95%CI) c Reference 1.29 (1.20, 1.39) 1.54 (1.44, 1.65) 2.01 (1.88, 2.15) 1.39 (1.35, 1.43) TyG-BMI Cases/Total 1292/23835 1822/23836 2186/23835 2731/23836 Incidence Rate a 390.23 553.78 670.15 845.16 Model 1, HR (95%CI) b Reference 1.33 (1.24, 1.43) 1.59 (1.48, 1.70) 2.09 (1.96, 2.24) 1.45 (1.41, 1.49) Model 2, HR (95%CI) c Reference 1.34 (1.25, 1.44) 1.60 (1.49, 1.72) 2.12 (1.98, 2.27) 1.46 (1.41, 1.50) TyG-WC Cases/Total 1086/23835 1783/23836 2194/23835 2968/23836 Incidence Rate a 322.2 539.22 674.35 938.18 Model 1, HR (95%CI) b Reference 1.43 (1.33, 1.54) 1.68 (1.56, 1.81) 2.30 (2.14, 2.47) 1.51 (1.46, 1.55) Model 2, HR (95%CI) c Reference 1.44 (1.33, 1.55) 1.69 (1.57, 1.83) 2.30 (2.14, 2.47) 1.50 (1.46, 1.55) TyG-WHtR Cases/Total 1110/23835 1725/23836 2216/23835 2980/23836 Incidence Rate a 328.77 521.11 680.94 945 Model 1, HR (95%CI) b Reference 1.38 (1.28, 1.49) 1.72 (1.60, 1.84) 2.33 (2.17, 2.49) 1.53 (1.48, 1.57) Model 2, HR (95%CI) c Reference 1.39 (1.29, 1.51) 1.74 (1.61, 1.87) 2.33 (2.17, 2.50) 1.52 (1.48, 1.57) Myocardial Infarction TyG Cases/Total 276/23835 381/23835 532/23832 720/23840 Incidence Rate a 81.55 113.32 158.89 217.38 Model 1, HR (95%CI) b Reference 1.33 (1.14, 1.55) 1.83 (1.59, 2.12) 2.56 (2.22, 2.94) 1.51 (1.43, 1.59) Model 2, HR (95%CI) c Reference 1.35 (1.15, 1.59) 1.85 (1.59, 2.15) 2.56 (2.22, 2.96) 1.51 (1.43, 1.60) TyG-BMI Cases/Total 284/23835 393/23836 522/23835 710/23836 Incidence Rate a 84.54 116.93 155.85 212.76 Model 1, HR (95%CI) b Reference 1.29 (1.11, 1.51) 1.70 (1.47, 1.97) 2.43 (2.12, 2.79) 1.55 (1.46, 1.65) Model 2, HR (95%CI) c Reference 1.30 (1.11, 1.52) 1.67 (1.44, 1.94) 2.42 (2.10, 2.79) 1.55 (1.46, 1.65) TyG-WC Cases/Total 206/23835 391/23836 534/23835 778/23836 Incidence Rate a 60.37 115.75 160.05 237.16 Model 1, HR (95%CI) b Reference 1.62 (1.37, 1.92) 2.09 (1.78, 2.46) 3.01 (2.58, 3.52) 1.65 (1.55, 1.75) Model 2, HR (95%CI) c Reference 1.66 (1.39, 1.97) 2.15 (1.82, 2.54) 3.05 (2.60, 3.58) 1.65 (1.55, 1.75) TyG-WHtR Cases/Total 224/23835 380/23836 551/23835 754/23836 Incidence Rate a 65.53 112.51 164.94 230.54 Model 1, HR (95%CI) b Reference 1.48 (1.26, 1.75) 2.05 (1.76, 2.40) 2.79 (2.40, 3.24) 1.64 (1.54, 1.74) Model 2, HR (95%CI) c Reference 1.52 (1.28, 1.80) 2.10 (1.79, 2.47) 2.83 (2.42, 3.30) 1.64 (1.54, 1.74) Ischemic Stroke TyG Cases/Total 1114/23835 1464/23835 1678/23832 2097/23840 Incidence Rate a 332.89 442.09 509.88 645.6 Model 1, HR (95%CI) b Reference 1.27 (1.18, 1.37) 1.45 (1.34, 1.56) 1.86 (1.73, 2.00) 1.36 (1.32, 1.40) Model 2, HR (95%CI) c Reference 1.27 (1.18, 1.38) 1.45 (1.34, 1.57) 1.87 (1.73, 2.01) 1.35 (1.31, 1.40) TyG-BMI Cases/Total 1038/23835 1473/23836 1724/23835 2118/23836 Incidence Rate a 312.15 444.66 523.97 647.93 Model 1, HR (95%CI) b Reference 1.33 (1.23, 1.44) 1.55 (1.43, 1.67) 2.00 (1.86, 2.15) 1.42 (1.38, 1.47) Model 2, HR (95%CI) c Reference 1.35 (1.25, 1.47) 1.58 (1.46, 1.71) 2.03 (1.88, 2.19) 1.43 (1.38, 1.48) TyG-WC Cases/Total 902/23835 1433/23836 1708/23835 2310/23836 Incidence Rate a 266.69 430.66 520.18 721 Model 1, HR (95%CI) b Reference 1.39 (1.27, 1.51) 1.57 (1.45, 1.71) 2.14 (1.98, 2.32) 1.47 (1.42, 1.52) Model 2, HR (95%CI) c Reference 1.38 (1.27, 1.51) 1.58 (1.45, 1.71) 2.14 (1.98, 2.32) 1.46 (1.42, 1.52) TyG-WHtR Cases/Total 912/23835 1379/23836 1730/23835 2332/23836 Incidence Rate a 269.12 413.98 526.89 730.17 Model 1, HR (95%CI) b Reference 1.34 (1.24, 1.46) 1.63 (1.50, 1.76) 2.20 (2.04, 2.38) 1.49 (1.45, 1.54) Model 2, HR (95%CI) c Reference 1.35 (1.24, 1.47) 1.64 (1.51, 1.78) 2.20 (2.04, 2.38) 1.49 (1.44, 1.54) a Incidence rate per 1,000 person-years. b Cox proportional hazard regression model, adjusted for age and gender. c Further adjusted for monthly income, education level, marital status, current smoking, current drinking, physical activity, and family history of atherosclerotic cardiovascular diseases. Abbreviations: ASCVD, atherosclerotic cardiovascular diseases; BMI, body mass index; HR (95%CI), hazard ratio (95% confidence interval); IQR, interquartile range; TyG, triglyceride-glucose; WC, waist circumference; WHtR, Waist-to-Height ratio. Subgroup and sensitivity analyses The results of subgroup analyses were presented in Fig. 1 and Supplementary Table 4 . In general, age and gender significantly interacted with both TyG and its related parameters in relation to ASCVD risk, within which stronger associations were observed among those younger (< 60 years) and female individuals ( P for interaction <0.001). Other significant interactions were also observed between specific subgroups and indicators. For instance, the adverse effect of high TyG-WC and TyG-WHtR on ASCVD seemed to be stronger among those free from hypertension and diabetes. Results were consistent when using the updated mean instead of baseline measurements of TyG index and its related parameters; other sensitivity analyses also had no substantial influence on the primary findings (Fig. 2 , Supplementary Table 5 ). Predictive values of TyG and its related parameters for ASCVD The C-index (95%CI) of TyG, TyG-BMI, TyG-WC, and TyG-WHtR was 0.583 (0.577, 0.590), 0.584 (0.578, 0.591), 0.612 (0.606, 0.618), 0.613 (0.607, 0.619) for ASCVD, respectively. In addition, the discriminatory power and risk reclassification of TyG-WC and TyG-WHtR appeared to be slightly better than TyG, with the IDI (95%CI) of 0.004 (0.001, 0.004) and 0.004 (0.001, 0.004), and the category-free NRI (95%CI) of 0.120 (0.025, 0.138) and 0.143 (0.032, 0.166), respectively. However, no significant improvement was observed in the predictive value of TyG-BMI for ASCVD compared with TyG. Similar results were found for MI and IS, and more detailed information was provided in Table 3 . Table 3 Reclassification and discrimination statistics for TyG and its related parameters. C-index (95%CI) IDI (95%CI) Category-free NRI (95%CI) ASCVD TyG 0.583 (0.577, 0.590) Reference Reference TyG-BMI 0.584 (0.578, 0.591) 0.000 (-0.001, 0.000) 0.005 (-0.024, 0.016) TyG-WC 0.612 (0.606, 0.618) 0.004 (0.001, 0.004) 0.120 (0.025, 0.138) TyG-WHtR 0.613 (0.607, 0.619) 0.004 (0.001, 0.004) 0.143 (0.032, 0.166) Myocardial Infarction TyG 0.608 (0.595, 0.621) Reference Reference TyG-BMI 0.596 (0.583, 0.609) 0.000 (-0.001, 0.000) -0.038 (-0.064, 0.017) TyG-WC 0.635 (0.623, 0.647) 0.001 (0.000, 0.001) 0.122 (0.020, 0.157) TyG-WHtR 0.631 (0.619, 0.643) 0.001 (0.000, 0.001) 0.113 (0.019, 0.145) Ischemic Stroke TyG 0.576 (0.568, 0.583) Reference Reference TyG-BMI 0.581 (0.574, 0.588) 0.000 (0.000, 0.000) 0.020 (0.001, 0.030) TyG-WC 0.605 (0.598, 0.612) 0.003 (0.000, 0.003) 0.115 (0.025, 0.137) TyG-WHtR 0.608 (0.601, 0.614) 0.003 (0.001, 0.003) 0.145 (0.034, 0.155) Abbreviations: ASCVD, atherosclerotic cardiovascular diseases; BMI, body mass index; 95%CI, 95% confidence interval); IDI, integrated discrimination improvement; IQR, interquartile range; NRI, net reclassification index; TyG, triglyceride-glucose; WC, waist circumference; WHtR, Waist-to-Height ratio. Discussion Based on data from the Kailuan cohort, this large-scale, prospective study demonstrated significant positive associations of TyG, TyG-BMI, TyG-WC and TyG-WHtR with the occurrence of ASCVD and its subtypes, which were validated by multiple sensitivity analyses like using the updated mean or time-varying measurements instead of baseline indicators. Although higher TyG and its related parameters were found to be consistently associated with increased risk of ASCVD regardless of age and gender, the effect seemed to be stronger among females and individuals who were < 60 years of age. In addition, TyG-WC and TyG-WHtR have showed better capacity for predicting incident ASCVD compared with single TyG. The TyG index and its combination with obesity indicators were proposed as simple yet powerful surrogate indicators for IR and metabolic health in clinical applications. Furthermore, emerging epidemiologic evidence has suggested that those indicators were closely related to systematic arterial stiffness, carotid atherosclerosis, coronary artery stenosis, and ASCVD both in the general population and among patients with cardiometabolic diseases like hypertension and diabetes. [ 7 , 19 , 27 – 31 ] A cross-sectional study conducted in Taiwan, for instance, suggested that individuals with higher TyG, TyG-BMI, and TyG-WC were more likely to have elevated risk of ASCVD. [ 17 ] In accordance, two community-based studies in China indicated that the three above-mentioned indicators were positively associated with increased risk of stroke. [ 16 , 32 ] As for MI, the third National Health and Nutrition Examination Survey (NHANES III) suggested that elevated TyG increased the risk of subclinical myocardial injury. [ 33 ] More directly, a previous study based on Kailuan cohort has demonstrated the association between baseline and long-term TyG and occurrence of MI. [ 8 ] Nevertheless, it was noteworthy that most of them focused on the influence of the current hottest TyG, while researches on those TyG related parameters remained relatively scattered. Furthermore, few existing studies evaluated those parameters at the same time, thus limiting further comparisons between different indicators. Recently, a cross-sectional study based on an American population has investigated the relationship of TyG and its related indicators with CVD incidence and mortality and evaluated their prediction values comprehensively. However, only a single baseline blood sample was collected in that study, and the diagnosis of CVD was established by self-reported diagnoses without exact time of onset. [ 18 ] More importantly, a significant heterogeneity of the association of TyG with CVD between low/middle-income and high-income countries was found in the Prospective Urban Rural Epidemiology (PURE) study, emphasizing the necessity of more evidence from population at diverse levels of economic development.[ 34 ] To the best of our knowledge, few researches have been conducted in developing countries to compare the correlation between TyG related parameters with ASCVD, of which prospective studies were even rarer. Benefiting from repeated measurements over the long-term follow-ups and accurate information on disease incidence, we evaluated and compared the association of TyG, TyG-BMI, TyG-WC, and TyG-WHtR with ASCVD based on a large-scale cohort in China; sensitivity analyses based on long-term average and time-varying levels further confirmed these findings. Consistent with previous publications, we observed significant and positive correlations between all four indicators and ASCVD as well as its subtypes. The HRs of per 1 IQR increase were found to be higher for TyG-WC and TyG-WHtR, which also had significantly better performance in predicting risk of ASCVD in comparison with TyG, while no obvious difference was observed between TyG-BMI and TyG. Our findings were generally in line with previous research conducted in high-income countries. For instance, a study from Korea suggested that TyG-WC had a better diagnostic efficacy for coronary artery calcification than TyG-BMI and TyG. [ 35 ] Similarly, findings based on the NHANES study also indicated that combining the TyG index with WC and WHtR further enhance its ability to predict CVD mortality. [ 18 ] Mechanisms behind the different predictive performance haven’t been fully explained yet, while the combined effects between IR and excess abdominal fat on the development of ASCVD might be a possible explanation. Both imparted insulin sensitivity, reflecting by TyG, and excessive accumulation of visceral adiposity, indicated by WC and WHtR, were closely related to chronic inflammation, endothelium dysfunction and atherosclerosis, which all contributed to the development of ASCVD. Therefore, novel indicators integrating TyG with central obesity indices might present a more accurate prediction of ASCVD than TyG index alone. Moreover, our subgroup analyses revealed that the association of TyG and its related parameters with ASCVD was more pronounced in relatively younger and female individuals. The age-specific influence was consistent with previous studies. [ 18 , 36 ] One potential reason might be that the elderly were more likely to take multiple medications and be exposed to more cardiovascular risk factors apart from IR or obesity, which to some extent undermined the predictive capability of TyG and other indicators for ASCVD. On the contrary, we found that the interaction of those indices with genders were heterogeneous across studies. For instance, a research conducted in the U.S showed that the relationship between TyG and 10-year risk of ASCVD was stronger in men than women. [ 37 ] while the correlation was found to be higher in women than men in another study, in line with our current findings. [ 18 ] Therefore, further exploration is essential for deeper explanation of the gender difference. The advantage of our study merits consideration. The analysis was conducted on the basis of a large-scale, community-based cohort with repeated measurements during 15-year follow-ups, which made it possible to simultaneously compare roles of each indicator in a prospective design, thus helping explore the most suitable risk marker for predicting future risk of ASCVD. Moreover, findings of our study were relatively reliable and robust since no obvious violation was observed with series of sensitivity analyses performed. Nonetheless, our study also has some limitations to address. First, IR indicators including insulin and HbA1c were not measured in the Kailuan study, which limited further comparisons with those conventional biomarkers in the current analysis. Secondly, in spite of adequate confounding adjustments in the analyses, residual or unmeasured confounding could not be completely avoided given the observational study design. Moreover, generalizing of our findings to other populations with diverse time and social background should be cautious since only individuals living in Northern China were included in the current study. Conclusion The TyG index and TyG related parameters, namely TyG-BMI, TyG-WC and TyG-WHtR, were significantly associated with the incidence of ASCVD and its major subtypes MI and IS, and the effect of those indicators on the risk of ASCVD was more pronounced in female and individuals aged less than 60 years. In addition, we demonstrated that innovative indices combining the TyG index with central obesity parameters WC, and WHtR outperformed TyG alone in prediction for incidence of ASCVD. In consideration of the increasingly severe disease burden and limited medical resource, our findings of convenient, low-cost surrogate indices of IR with good predictive capacity were of great importance for ASCVD prevention in low- and middle-income counties including China. Abbreviations ASCVD atherosclerotic cardiovascular diseases BMI body mass index CI confidence interval HR hazard ratio IDI integrated discrimination improvement IQR inter-quartile range IR insulin resistance IS ischemic stroke MI myocardial infarction NRI net reclassification index RCS restricted cubic splines TyG triglyceride-glucose WC waist circumference WHtR waist-to-height ratio Declarations Ethics approval and consent to participate The whole study complies with the Declaration of Helsinki and has been approved by the Ethics Committees of both Kailuan General Hospital and Beijing Tiantan Hospital. Written informed consent was obtained from all participants before data collection. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The author declare that they have no competing interests. Funding This work was supported by Beijing Natural Science Foundation Haidian original innovation joint fund (L222123), National Key Research and Development Program of China (2022YFC3600600), Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (CCMU2022ZKYXZ009), Fund for Young Talents of Beijing Medical Management Center (QML20230505), and the high-level public health talents (xuekegugan-02-47). The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Authors' contributions SW and AW had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: SW, AW Acquisition, analysis, or interpretation of data: XX, SC, XT, QX Drafting of the manuscript: XX Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: XX, SC, YZ, XZ, JL Obtained funding: AW Administrative, technical, or material support: SW, AW Supervision: SW, AW Acknowledgements The authors acknowledge all staff and participants of the Kailuan study for their important participation and contribution. References GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982–3021. Wang W, Hu M, Liu H, Zhang X, Li H, Zhou F, Liu YM, Lei F, Qin JJ, Zhao YC, et al. Global Burden of Disease Study 2019 suggests that metabolic risk factors are the leading drivers of the burden of ischemic heart disease. Cell Metab. 2021;33(10):1943–56. e1942. Adeva-Andany MM, Martínez-Rodríguez J, González-Lucán M, Fernández-Fernández C, Castro-Quintela E. Insulin resistance is a cardiovascular risk factor in humans. Diabetes Metab Syndr. 2019;13(2):1449–55. Muniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab. 2008;294(1):E15–26. Toro-Huamanchumo CJ, Urrunaga-Pastor D, Guarnizo-Poma M, Lazaro-Alcantara H, Paico-Palacios S, Pantoja-Torres B, Ranilla-Seguin VDC, Benites-Zapata VA. Triglycerides and glucose index as an insulin resistance marker in a sample of healthy adults. Diabetes Metab Syndr. 2019;13(1):272–7. Wang A, Wang G, Liu Q, Zuo Y, Chen S, Tao B, Tian X, Wang P, Meng X, Wu S, et al. Triglyceride-glucose index and the risk of stroke and its subtypes in the general population: an 11-year follow-up. Cardiovasc Diabetol. 2021;20(1):46. Tian X, Zuo Y, Chen S, Liu Q, Tao B, Wu S, Wang A. Triglyceride-glucose index is associated with the risk of myocardial infarction: an 11-year prospective study in the Kailuan cohort. Cardiovasc Diabetol. 2021;20(1):19. Miao H, Zhou Z, Yang S, Zhang Y. The association of triglyceride-glucose index and related parameters with hypertension and cardiovascular risk: a cross-sectional study. Hypertens Res 2023. Reho JJ, Rahmouni K. Oxidative and inflammatory signals in obesity-associated vascular abnormalities. Clin Sci (Lond). 2017;131(14):1689–700. Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444(7121):875–80. Zhang X, Zhang T, He S, Jia S, Zhang Z, Ye R, Yang X, Chen X. Association of metabolic syndrome with TyG index and TyG-related parameters in an urban Chinese population: a 15-year prospective study. Diabetol Metab Syndr. 2022;14(1):84. Xue Y, Xu J, Li M, Gao Y. Potential screening indicators for early diagnosis of NAFLD/MAFLD and liver fibrosis: Triglyceride glucose index-related parameters. Front Endocrinol (Lausanne). 2022;13:951689. Cheng W, Kong F, Chen S. Comparison of the predictive value of four insulin resistance surrogates for the prevalence of hypertension: a population-based study. Diabetol Metab Syndr. 2022;14(1):137. Wang X, Liu J, Cheng Z, Zhong Y, Chen X, Song W. Triglyceride glucose-body mass index and the risk of diabetes: a general population-based cohort study. Lipids Health Dis. 2021;20(1):99. Zhao Y, Zhang J, Chen C, Qin P, Zhang M, Shi X, Yang Y, Lu J, Sun L, Hu D. Comparison of six surrogate insulin resistance indexes for predicting the risk of incident stroke: The Rural Chinese Cohort Study. Diabetes Metab Res Rev. 2022;38(7):e3567. Huang YC, Huang JC, Lin CI, Chien HH, Lin YY, Wang CL, Liang FW, Dai CY, Chuang HY. Comparison of Innovative and Traditional Cardiometabolic Indices in Estimating Atherosclerotic Cardiovascular Disease Risk in Adults. Diagnostics (Basel) 2021, 11(4). Dang K, Wang X, Hu J, Zhang Y, Cheng L, Qi X, Liu L, Ming Z, Tao X, Li Y. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003–2018. Cardiovasc Diabetol. 2024;23(1):8. Liu Z, Deng B, Huang Q, Tu R, Yu F, Xia J, Feng J. Comparison of seven surrogate insulin resistance indexes for predicting the prevalence of carotid atherosclerosis in normal-weight individuals. Front Public Health. 2023;11:1241523. Wu S, An S, Li W, Lichtenstein AH, Gao J, Kris-Etherton PM, Wu Y, Jin C, Huang S, Hu FB, et al. Association of Trajectory of Cardiovascular Health Score and Incident Cardiovascular Disease. JAMA Netw Open. 2019;2(5):e194758. Wang C, Yuan Y, Zheng M, Pan A, Wang M, Zhao M, Li Y, Yao S, Chen S, Wu S, et al. Association of Age of Onset of Hypertension With Cardiovascular Diseases and Mortality. J Am Coll Cardiol. 2020;75(23):2921–30. Zhou YF, Chen S, Wang G, Chen S, Zhang YB, Chen JX, Tu ZZ, Liu G, Wu S, Pan A. Effectiveness of a Workplace-Based, Multicomponent Hypertension Management Program in Real-World Practice: A Propensity-Matched Analysis. Hypertension. 2022;79(1):230–40. Wang YH, Wang J, Chen SH, Li JQ, Lu QD, Vitiello MV, Wang F, Tang XD, Shi J, Lu L, et al. Association of Longitudinal Patterns of Habitual Sleep Duration With Risk of Cardiovascular Events and All-Cause Mortality. JAMA Netw Open. 2020;3(5):e205246. Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas AM, Pajak A. Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. Circulation. 1994;90(1):583–612. Special Report from the World Health Organization. Stroke-1989. Recommendations on stroke prevention, diagnosis, and therapy. Report of the WHO Task Force on Stroke and other Cerebrovascular Disorders. Stroke, 20(10):1407–31. PM G, TM T. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81:515–26. Li F, Wang Y, Shi B, Sun S, Wang S, Pang S, Wu X. Association between the cumulative average triglyceride glucose-body mass index and cardiovascular disease incidence among the middle-aged and older population: a prospective nationwide cohort study in China. Cardiovasc Diabetol. 2024;23(1):16. Xu W, Zhao H, Han X, Liu J, Li H, Sun J, Xing A, Chen S, Wu S, Wu Y. Relationship between early-onset stroke and triglyceride-glucose index among young Chinese adults. Lipids Health Dis. 2023;22(1):3. Liang S, Wang C, Zhang J, Liu Z, Bai Y, Chen Z, Huang H, He Y. Triglyceride-glucose index and coronary artery disease: a systematic review and meta-analysis of risk, severity, and prognosis. Cardiovasc Diabetol. 2023;22(1):170. Wu S, Xu L, Wu M, Chen S, Wang Y, Tian Y. Association between triglyceride-glucose index and risk of arterial stiffness: a cohort study. Cardiovasc Diabetol. 2021;20(1):146. Lee EY, Yang HK, Lee J, Kang B, Yang Y, Lee SH, Ko SH, Ahn YB, Cha BY, Yoon KH, et al. Triglyceride glucose index, a marker of insulin resistance, is associated with coronary artery stenosis in asymptomatic subjects with type 2 diabetes. Lipids Health Dis. 2016;15(1):155. Huang Q, Yin L, Liu Z, Wei M, Feng J, Huang Q, Liu Y, Liu Z, Xia J. Association of novel lipid indicators with the risk of stroke among participants in Central China: a population-based prospective study. Front Endocrinol (Lausanne). 2023;14:1266552. Liu Y, Wu M, Xu J, Sha D, Xu B, Kang L. Association between Triglyceride and glycose (TyG) index and subclinical myocardial injury. Nutr Metab Cardiovasc Dis. 2020;30(11):2072–6. Lopez-Jaramillo P, Gomez-Arbelaez D, Martinez-Bello D, Abat MEM, Alhabib KF, Avezum Á, Barbarash O, Chifamba J, Diaz ML, Gulec S, et al. Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study. Lancet Healthy Longev. 2023;4(1):e23–33. Cho YK, Lee J, Kim HS, Kim EH, Lee MJ, Yang DH, Kang JW, Jung CH, Park JY, Kim HK et al. Triglyceride Glucose-Waist Circumference Better Predicts Coronary Calcium Progression Compared with Other Indices of Insulin Resistance: A Longitudinal Observational Study. J Clin Med 2020, 10(1). Barzegar N, Tohidi M, Hasheminia M, Azizi F, Hadaegh F. The impact of triglyceride-glucose index on incident cardiovascular events during 16 years of follow-up: Tehran Lipid and Glucose Study. Cardiovasc Diabetol. 2020;19(1):155. Qu H, Long LZ, Chen L, Wu HT, Fu CG, Zhang SS. Triglyceride-glucose index and estimated 10-year risk of a first hard cardiovascular event. Front Cardiovasc Med. 2022;9:994329. Additional Declarations No competing interests reported. Supplementary Files Additionalfiles.pdf Additional files File format • Pdf. Title and Description of data • Supplementary Table 1. Baseline characteristics of participants according to TyG-BMI quartiles. • Supplementary Table 2. Baseline characteristics of participants according to TyG-WC quartiles. • Supplementary Table 3. Baseline characteristics of participants according to TyG-WHtR quartiles. • Supplementary Table 4. Subgroup analyses of TyG and its related parameters with atherosclerotic cardiovascular disease. • Supplementary Table 5. Sensitivity analyses for association of TyG and its related parameters with atherosclerotic cardiovascular disease and its subtypes. • Supplementary Figure 1. Flow chart of participants included and excluded in the analyses. • Supplementary Figure 2. Restricted cubic spline curves of the association of triglyceride-glucose and its related parameters with atherosclerotic cardiovascular disease and its subtypes. Cite Share Download PDF Status: Published Journal Publication published 18 Jun, 2024 Read the published version in Cardiovascular Diabetology → Version 1 posted Editorial decision: Revision requested 14 Apr, 2024 Reviews received at journal 12 Apr, 2024 Reviews received at journal 04 Apr, 2024 Reviews received at journal 28 Mar, 2024 Reviewers agreed at journal 28 Mar, 2024 Reviewers agreed at journal 27 Mar, 2024 Reviewers agreed at journal 27 Mar, 2024 Reviewers invited by journal 26 Mar, 2024 Editor assigned by journal 25 Mar, 2024 Submission checks completed at journal 25 Mar, 2024 First submitted to journal 24 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4157386","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284671671,"identity":"f855b9ef-5dd4-45d4-806d-176a931c2137","order_by":0,"name":"Xue Xia","email":"","orcid":"","institution":"Beijing Neurosurgical Institute, Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Xia","suffix":""},{"id":284671672,"identity":"4d48c521-5e0e-4ac7-b8ff-0d4bc1aa4607","order_by":1,"name":"Shuohua Chen","email":"","orcid":"","institution":"Kailuan General Hospital, North China University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuohua","middleName":"","lastName":"Chen","suffix":""},{"id":284671673,"identity":"9abf09fd-671f-4097-b94c-a2f6e19784ad","order_by":2,"name":"Xue Tian","email":"","orcid":"","institution":"Beijing Neurosurgical Institute, Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Tian","suffix":""},{"id":284671674,"identity":"626ec68c-f5de-470d-b9d7-1465798be711","order_by":3,"name":"Qin Xu","email":"","orcid":"","institution":"Beijing Neurosurgical Institute, Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Xu","suffix":""},{"id":284671675,"identity":"82c77671-6e7f-415a-80ae-9d673bc65f7c","order_by":4,"name":"Yijun Zhang","email":"","orcid":"","institution":"Beijing Neurosurgical Institute, Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Zhang","suffix":""},{"id":284671676,"identity":"1f939563-ff10-4590-9f06-e2840ae3544c","order_by":5,"name":"Xiaoli Zhang","email":"","orcid":"","institution":"Beijing Neurosurgical Institute, Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Zhang","suffix":""},{"id":284671677,"identity":"56abb1d8-8290-479a-83ee-7f130630b227","order_by":6,"name":"Jing Li","email":"","orcid":"","institution":"Beijing Neurosurgical Institute, Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":284671678,"identity":"18f70c1b-8165-4b6e-89b1-e8cfbf5293e4","order_by":7,"name":"Shouling Wu","email":"","orcid":"","institution":"Kailuan General Hospital, North China University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shouling","middleName":"","lastName":"Wu","suffix":""},{"id":284671679,"identity":"beaebb4d-f54f-44c6-bd17-1c3baf390754","order_by":8,"name":"Anxin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3RuwrCMBSA4QMFuxxxbfHyDEcKFaHQV+nUKRTBxSfo5OUBfBHHQECXiI4VHQRBHBR000EwiF1Tuwnmn7J8ObkAmEw/HII9BALgZQhKIB6VIAAOUzO+IbRYitNjJhod98L7t/sOavXFWk9kEndHMsbuNInUwY7gjllPS3zOfKqmAdKWkSJCbYKRnqzOfvuZOkgb+S3JmHd4T8kwJzbXkjA7+1YzjVFdKiIZC3SHqBXgTph3u6QiVE83p0EgWjW093oDUHHyhfp99aeAVESsa7747F48xWQymf6rF1rwTClKsvcOAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Neurosurgical Institute, Capital Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Anxin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-03-24 09:44:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4157386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4157386/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12933-024-02290-3","type":"published","date":"2024-06-19T01:02:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53749521,"identity":"5292c39f-6eb4-4ceb-973f-d40fdc497c38","added_by":"auto","created_at":"2024-03-29 18:33:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots of subgroup\u003c/strong\u003e \u003cstrong\u003eanalyses for the association of TyG and its related parameters with atherosclerotic cardiovascular diseases.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: ASCVD, atherosclerotic cardiovascular diseases; BMI, body mass index; HR (95%CI), hazard ratio (95% confidence interval); IQR, interquartile range; TyG, triglyceride-glucose; WC, waist circumference; WHtR, Waist-to-Height ratio.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4157386/v1/5a33623193f7f0054679a26d.png"},{"id":53749523,"identity":"fd52cfbd-dc05-4d14-98e2-4f6cee0d29e9","added_by":"auto","created_at":"2024-03-29 18:33:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51706,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensitivity analyses for association of TyG and its related parameters with atherosclerotic cardiovascular diseases. \u003c/strong\u003e(A) Excluding ASCVD events that occurred during the initial two years of follow-up; (B) Using the updated mean instead of baseline measurements of TyG index and its related parameters;(C) Further adjusting for hypertension, diabetes and hyperlipidemia; (D) Adopting the Cox regression model with time-varying variables updated in each survey before the occurrence of ASCVD events; (E) Adopting the Fine \u0026amp; Gary sub-distribution hazard models to further consider the competing risk of non-ASCVD deaths.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ASCVD, atherosclerotic cardiovascular diseases; BMI, body mass index; HR (95%CI), hazard ratio (95% confidence interval); IQR, interquartile range; Q1-Q4, Quartile1-4; TyG, triglyceride-glucose; WC, waist circumference; WHtR, Waist-to-Height ratio.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4157386/v1/8f1872e4a0e304b0d295d635.png"},{"id":58692708,"identity":"f7873d51-ee29-4a09-aefb-60113e302002","added_by":"auto","created_at":"2024-06-20 01:02:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1250345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4157386/v1/d92add1c-e47c-47a0-8560-865d2fdebcea.pdf"},{"id":53749522,"identity":"5d925340-2c8a-4b93-9854-caaed74b629b","added_by":"auto","created_at":"2024-03-29 18:33:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":613660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFile format\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;• Pdf.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTitle and Description of data\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e• Supplementary Table 1. Baseline characteristics of participants according to TyG-BMI quartiles.\u003c/p\u003e\n\u003cp\u003e• Supplementary Table 2. Baseline characteristics of participants according to TyG-WC quartiles.\u003c/p\u003e\n\u003cp\u003e• Supplementary Table 3. Baseline characteristics of participants according to TyG-WHtR quartiles.\u003c/p\u003e\n\u003cp\u003e• Supplementary Table 4. Subgroup analyses of TyG and its related parameters with atherosclerotic cardiovascular disease.\u003c/p\u003e\n\u003cp\u003e• Supplementary Table 5. Sensitivity analyses for association of TyG and its related parameters with atherosclerotic cardiovascular disease and its subtypes.\u003c/p\u003e\n\u003cp\u003e• Supplementary Figure 1. Flow chart of participants included and excluded in the analyses.\u003c/p\u003e\n\u003cp\u003e• Supplementary Figure 2. Restricted cubic spline curves of the association of triglyceride-glucose and its related parameters with atherosclerotic cardiovascular disease and its subtypes.\u003c/p\u003e","description":"","filename":"Additionalfiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4157386/v1/7175c2e5d89a61a957a1f982.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of triglyceride-glucose index and its related parameters with atherosclerotic cardiovascular disease: evidence from a 15-year follow-up of Kailuan cohort","fulltext":[{"header":"Background","content":"\u003cp\u003eDespite remarkable advance in prevention strategies and therapeutic techniques, the burden of atherosclerotic cardiovascular disease (ASCVD) remains its decades-long rise for almost all non-high-income countries, and has becoming the leading cause of mortality and health impairment worldwide. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] It was estimated that ASCVD accounted for 22% of total deaths and 245.5\u0026nbsp;million disability-adjusted life years (DALYs) in 2019, for which metabolic dysfunction was acknowledged as a predominant contributor. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Therefore, optimization of existing strategies to identify and control metabolic risk factors is essential for better prevention of ASCVD.\u003c/p\u003e \u003cp\u003eImpaired insulin sensitivity, also known as insulin resistance (IR), contributes to the pathophysiology of diabetes and serves as a crucial element contributing to ASCVD.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] The triglyceride glucose (TyG), defined merely based on fasting blood glucose (FBG) and triglyceride (TG), has gradually been used as a convenient and reliable alternative index for the consuming and expensive hyperinsulinemic-euglycemic clamp technique, which remains the gold standard for assessing insulin sensitivity.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Moreover, several studies revealed the association of TyG index with risks of hypertension, myocardial infarction (MI), and ischemic stroke (IS), some of which further confirmed its clinical value in predicting ASCVD. [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFat mass and central obesity also play an important role in the progress of atherogenic endothelial dysfunction partly through metabolic disorders, which ultimately lead to the occurrence of ASCVD. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Thus, TyG combined with obesity indices like body mass index (BMI), waist circumference (WC) and waist-to-height ratio (WHtR), were introduced as new surrogates for IR and has been proved to be associated with a series of metabolic diseases encompassing diabetes mellitus, metabolic syndrome, hypertension, and non-alcoholic fatty liver disease. [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] More recently, epidemiological evidence has suggested that those TyG related parameters are closely related to ASCVD and some even showed better prediction ability over TyG, while the evidence was scattered and most of them were drawn from cross-sectional designed studies, leaving the conclusion inconsistent. [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWith regards to the above, the current analysis was conducted based on the Kailuan cohort to investigate the association of TyG, TyG-BMI, TyG-WC, and TyG-WHtR with ASCVD and its subtypes, using the updated mean and time-varying indicators instead of baseline measurements in the sensitivity analyses for further verification. Additionally, we compared the predictive value of the TyG and its related parameters for ASCVD by calculating C-index, integrated discrimination improvement (IDI), and category-free net reclassification index (NRI).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThe study population was derived from the Kailuan study, a community-based prospective cohort study conducted in Northern China, which has been fully described in previous publications. [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] In brief, it was established during 2006\u0026ndash;2007, recruiting 101,510 participants aged 18\u0026ndash;98 years from the Kailuan company. The subsequent visits were performed every two years until December 2021. The study complied with the Declaration of Helsinki and was approved by the Ethics Committees of both Kailuan General Hospital and Beijing Tiantan Hospital. Written informed consent was obtained before data collection.\u003c/p\u003e \u003cp\u003eA total of 98,191 individuals free from ASCVD at baseline were initially included in the current analysis. We further excluded those with missing baseline records on triglyceride (N\u0026thinsp;=\u0026thinsp;66), fasting blood glucose (N\u0026thinsp;=\u0026thinsp;567), body weight (N\u0026thinsp;=\u0026thinsp;35), body height (N\u0026thinsp;=\u0026thinsp;45), or waist circumference (N\u0026thinsp;=\u0026thinsp;2,136), reducing the final sample size to 95,342 (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection and definitions\u003c/h2\u003e \u003cp\u003eParticipants received face-to-face interview, laboratory tests and physical examination during both baseline survey and the following biennial visits. Standardized questionnaires were adopted to collect information on age, gender, sociodemographic characteristics (household income, education attainment and marriage status), health-related life styles (smoking, alcohol intake and physical activity), personal and family medical histories. Blood samples were collected in the morning after an overnight fasting and analyzed with the Hitachi 747 automated analyzer (Tokyo, Japan). Body weight, height, WC and blood pressure were measured by trained medical professionals when participants attended the physical examination.\u003c/p\u003e \u003cp\u003eCurrent smoking or drinking were defined according to self-reported cigarette or alcohol consumption during the past 12 months. Individuals who exercised more than four times per week (\u0026ge;\u0026thinsp;20 minutes/time) were classified as active physical activity. Family history of ASCVD was defined as at least one of their parents diagnosed with MI or IS. Hypertension was identified based on systolic/diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 /90 mmHg, self-reported history of hypertension or antihypertensive medication usage. Diabetes was defined using fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, self-reported history of diabetes, treatment with oral hypoglycemic agents or insulin injection. Elevated concentration of total (\u0026ge;\u0026thinsp;6.2 mmol/L) or low-density lipoprotein cholesterol (\u0026ge;\u0026thinsp;4.1 mmol/L), or use of lipid-lowering medication was considered as hyperlipidemia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of TyG and its related parameters\u003c/h2\u003e \u003cp\u003eThe TyG index was calculated as ln (fasting triglyceride [mg/dL] \u0026times; fasting blood glucose [mg/dL]/2). BMI was calculated as body mass (kg) divided by height squared (m\u003csup\u003e2\u003c/sup\u003e), and WHtR was defined as the ratio of WC (cm) to height (cm). Accordingly, we calculated Tyg-BMI as TyG \u0026times; BMI, TyG-WC as TyG \u0026times; WC and TyG-WHtR as TyG \u0026times;WHtR.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] In addition to baseline measurements, the updated mean of each indicator was further calculated with all available data from baseline to the latest survey before occurrence of ASCVD or to the end of follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of outcomes\u003c/h2\u003e \u003cp\u003eInformation on disease information and vital status was primarily obtained through face-to-face interviews, biennially updated till the date of death or the latest follow-up visit, whichever came first. Moreover, medical records from the municipal social insurance institution, discharge summaries from local hospitals, and death certificates from provincial vital statistics offices were also checked for additional validation of diagnosis. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] All causes of events were recorded with the 10th version of the International Classification of Diseases (ICD-10).\u003c/p\u003e \u003cp\u003eThe primary outcome for this analysis was the first occurrence of ASCVD, a composite of MI and IS, and both of them were further analyzed separately as secondary outcomes. According to the World Health Organization criteria, MI (ICD-10 \u003cem\u003eI21-I23\u003c/em\u003e) was identified according to clinical symptom, electrocardiographic manifestation, and concentration of myocardial enzymes; [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and IS (ICD-10 \u003cem\u003eI63\u003c/em\u003e) was diagnosed on the basis of clinical signs combined with neuroimages from brain computed tomography or magnetic resonance imaging. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eBaseline characteristics were described as median (inter-quartile range, IQR) for continuous variables and as frequency (%) for categorical variables, stratified by quartiles of baseline TyG index and its related parameters. With time-on-study as timescale, the person-years of follow-up was calculated from the completion of baseline survey to either the date of ASCVD, death, or the most recent visit, whichever came first.\u003c/p\u003e \u003cp\u003eThe Cox proportional hazards regression model was applied to estimate the hazard ratio (HR) and 95% confidence interval (95% CI) for ASCVD and its subtypes. The proportional hazard assumption was evaluated by the weighted Schoenfeld residuals, [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and no violations were observed. Two sets of models were constructed to adjust for potential confounding factors. Model 1 was fitted with a primary adjustment for age and gender; in Model 2, we additionally controlled monthly income, education level, marital status, current smoking, current drinking, physical activity, and family history of ASCVD. To explore the potential nonlinear dose-response relationship of TyG index and its related parameters with ASCVD, restricted cubic splines (RCS) were fitted by adjusting for covariates in Model 2, with reference set at the 50th percentile and four knots at the 5th, 35th, 65th, and 95th percentiles of the distribution.\u003c/p\u003e \u003cp\u003eSubgroup analyses by baseline characteristics were conducted for the primary outcome based on Model 2 to investigate possible effect modifications. The multiplicative interaction was assessed by introducing a cross-product term, and the \u003cem\u003eP\u003c/em\u003e value was calculated using the likelihood ratio test. To further explore the potential impact of reverse causality, we repeated the primary analysis using a 2-year lag period by excluding ASCVD events that occurred during the initial two years of follow-up. Other sensitivity analyses involved 1) using the updated mean instead of baseline measurements of TyG index and its related parameters; 2) additionally adjusting for hypertension, diabetes and hyperlipidemia; 3) adopting the Cox regression model with time-varying variables updated in each survey before the occurrence of ASCVD events; 4) fitting the Fine \u0026amp; Gary sub-distribution hazard models to further consider the competing risk of non-ASCVD deaths. Moreover, C-index, IDI, and NRI were computed to quantify and compare the predictive powers of TyG, TyG-BMI, TyG-WC, and TyG-WHtR.\u003c/p\u003e \u003cp\u003eThe above assessments were performed with SAS version 9.4 (SAS Institute Inc, Cary, NC, USA) and R version 4.1.2 (R Core Team, Vienna, Austria). A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the participants\u003c/h2\u003e \u003cp\u003eOf 95,342 eligible participants included, the median age at recruitment was 59.0 years and 75,880 (79.6%) were males. The baseline characteristics stratified by TyG index were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In comparison with individuals at the lowest quartile of TyG, individuals with higher TyG levels had greater probabilities to be male and less well-educated. Moreover, they were more likely to be diagnosed with hypertension, diabetes and hyperlipidemia, while no significant difference was found in their family history of ASCVD. Similar tendency was also observed across quartiles of TyG-BMI, TyG-WC, and TyG-WHtR (\u003cb\u003eSupplementary Table\u0026nbsp;1\u0026ndash;3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants according to TyG quartiles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eQuartiles of TyG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange of TyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u0026ndash;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.6\u0026ndash;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.0 (51.0,66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.9 (49.0,66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.0 (51.0,66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.2 (52.0,67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.0 (52.0,66.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75880 (79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17556 (73.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18889 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19355 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20080 (84.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or above, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18674 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5642 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4469 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4430 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4133 (17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income\u0026thinsp;\u0026ge;\u0026thinsp;600 CNY, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6151 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1676 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1461 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1499 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1515 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87453 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21339 (92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22061 (94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21956 (94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22097 (95.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32037 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7733 (33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7490 (32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8039 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8775 (37.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinking, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35028 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8692 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8184 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8710 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9442 (40.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive physical activity, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14022 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3673 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3381 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3555 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3413 (14.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of ASCVD, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10728 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2636 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2556 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2782 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2754 (11.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32287 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5369 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7327 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8902 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10689 (45.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8629 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e606 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1774 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5991 (25.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12113 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1422 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2346 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3421 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4924 (20.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214.4 (189.0,242.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181.6 (164.8,200.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204.7 (187.4,223.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e223.6 (205.1,243.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e251.3 (230.2,274.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747.1 (671.6,830.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e646.7 (593.1,703.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e715.3 (666.2,767.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e773.3 (720.8,830.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e863.1 (802.6,929.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHtR, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5 (4.0,5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9 (3.6,4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3 (4.0,4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6 (4.3,5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1 (4.8,5.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues were represented by median (IQR) for continuous variables and frequency (percentage) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: ASCVD, atherosclerotic cardiovascular diseases; BMI, body mass index; IQR, interquartile range; TyG, triglyceride-glucose; WC, waist circumference; WHtR, Waist-to-Height ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of TyG and its related parameters with ASCVD\u003c/h2\u003e \u003cp\u003eOver a median (IQR) follow-up of 15.0 (14.7, 15.2) years, 8,031 (8.4%) cases of ASCVD were identified, including 1,909 (2.0%) MI and 6,353 (6.7%) IS. The crude incidence rate of ASCVD increased substantially with the level of TyG and its related parameters. For instance, it ranged from 407.04 to 847.46 per 100,000 person-years across quartiles of TyG and from 328.77 to 945.00 per 100,000 person-years across quartiles of TyG-WHtR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjustment for potential confounding factors in Model 2, higher levels of TyG and its related parameters were still related to elevated risks of ASCVD. More intuitively, RCS curves showed significant positive associations of those indicators with ASCVD and its subtypes, most of which exhibited a nonlinear monotone increase (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). Per 1 IQR increase in TyG was associated with a 39% higher risk (HR:1.39; 95%CI: 1.35, 1.43) of ASCVD, and the corresponding estimates were even greater for those TyG-related parameters, reaching 1.46 (1.41, 1.50) for TyG-BMI, 1.50 (1.46, 1.55) for TyG-WC, and 1.52 (1.48, 1.57) for TyG-WHtR. In the subtype analyses, similar results were yielded for MI and IS. TyG-WC and TyG-WHtR showed the highest association with MI and IS, respectively. Compared with the lowest quartile, the HR (95%CI) of MI was 1.66 (1.39, 1.97), 2.15 (1.82, 2.54), and 3.05 (2.60, 3.58) for the second, third and fourth quartile of TyG-WC; the HR (95%CI) of IS was 1.35 (1.24, 1.47), 1.64 (1.51, 1.78), and 2.20 (2.04, 2.38) for the second, third and fourth quartile of TyG-WHtR.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of TyG and its related parameters with atherosclerotic cardiovascular disease and its subtypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eQuartiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePer 1 IQR Increase\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASCVD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1356/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1798/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2157/23832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2720/23840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e407.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e546.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e661.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e847.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (1.20, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.53 (1.43, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.00 (1.87, 2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39 (1.36, 1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (1.20, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.54 (1.44, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.01 (1.88, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39 (1.35, 1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1292/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1822/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2186/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2731/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e390.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e553.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e670.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e845.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 (1.24, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.59 (1.48, 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.09 (1.96, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.45 (1.41, 1.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.25, 1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.60 (1.49, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.12 (1.98, 2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.46 (1.41, 1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-WC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1086/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1783/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2194/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2968/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e322.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e539.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e674.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e938.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43 (1.33, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.68 (1.56, 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.30 (2.14, 2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.51 (1.46, 1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44 (1.33, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.69 (1.57, 1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.30 (2.14, 2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.50 (1.46, 1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-WHtR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1110/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1725/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2216/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2980/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e521.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e680.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.28, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.72 (1.60, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.33 (2.17, 2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.53 (1.48, 1.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.29, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.74 (1.61, 1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.33 (2.17, 2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.52 (1.48, 1.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMyocardial Infarction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e532/23832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e720/23840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e158.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e217.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 (1.14, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.83 (1.59, 2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.56 (2.22, 2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.51 (1.43, 1.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35 (1.15, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.85 (1.59, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.56 (2.22, 2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.51 (1.43, 1.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e393/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e522/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e710/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e155.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e212.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (1.11, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.70 (1.47, 1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.43 (2.12, 2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.55 (1.46, 1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30 (1.11, 1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.67 (1.44, 1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.42 (2.10, 2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.55 (1.46, 1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-WC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e391/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e534/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e778/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e160.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e237.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62 (1.37, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.09 (1.78, 2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.01 (2.58, 3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.65 (1.55, 1.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66 (1.39, 1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.15 (1.82, 2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.05 (2.60, 3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.65 (1.55, 1.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-WHtR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e551/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e754/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e164.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e230.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48 (1.26, 1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.05 (1.76, 2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.79 (2.40, 3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.64 (1.54, 1.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52 (1.28, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.10 (1.79, 2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.83 (2.42, 3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.64 (1.54, 1.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIschemic Stroke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1114/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1464/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1678/23832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2097/23840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e509.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e645.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (1.18, 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.45 (1.34, 1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.86 (1.73, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.36 (1.32, 1.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (1.18, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.45 (1.34, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.87 (1.73, 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35 (1.31, 1.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1038/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1473/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1724/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2118/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e444.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e523.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e647.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 (1.23, 1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.55 (1.43, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.00 (1.86, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.42 (1.38, 1.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35 (1.25, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.58 (1.46, 1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.03 (1.88, 2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.43 (1.38, 1.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-WC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e902/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1433/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1708/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2310/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e266.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e430.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e520.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.27, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.57 (1.45, 1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.14 (1.98, 2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.47 (1.42, 1.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.27, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.58 (1.45, 1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.14 (1.98, 2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.46 (1.42, 1.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-WHtR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e912/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1379/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1730/23835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2332/23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence Rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e413.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e526.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e730.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95%CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.24, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.63 (1.50, 1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.20 (2.04, 2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49 (1.45, 1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35 (1.24, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.64 (1.51, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.20 (2.04, 2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49 (1.44, 1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Incidence rate per 1,000 person-years.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e Cox proportional hazard regression model, adjusted for age and gender.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003e Further adjusted for monthly income, education level, marital status, current smoking, current drinking, physical activity, and family history of atherosclerotic cardiovascular diseases.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: ASCVD, atherosclerotic cardiovascular diseases; BMI, body mass index; HR (95%CI), hazard ratio (95% confidence interval); IQR, interquartile range; TyG, triglyceride-glucose; WC, waist circumference; WHtR, Waist-to-Height ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and sensitivity analyses\u003c/h2\u003e \u003cp\u003eThe results of subgroup analyses were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e. In general, age and gender significantly interacted with both TyG and its related parameters in relation to ASCVD risk, within which stronger associations were observed among those younger (\u0026lt;\u0026thinsp;60 years) and female individuals (\u003cem\u003eP\u003c/em\u003e \u003csub\u003efor interaction\u003c/sub\u003e\u0026lt;0.001). Other significant interactions were also observed between specific subgroups and indicators. For instance, the adverse effect of high TyG-WC and TyG-WHtR on ASCVD seemed to be stronger among those free from hypertension and diabetes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResults were consistent when using the updated mean instead of baseline measurements of TyG index and its related parameters; other sensitivity analyses also had no substantial influence on the primary findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePredictive values of TyG and its related parameters for ASCVD\u003c/h2\u003e \u003cp\u003eThe C-index (95%CI) of TyG, TyG-BMI, TyG-WC, and TyG-WHtR was 0.583 (0.577, 0.590), 0.584 (0.578, 0.591), 0.612 (0.606, 0.618), 0.613 (0.607, 0.619) for ASCVD, respectively. In addition, the discriminatory power and risk reclassification of TyG-WC and TyG-WHtR appeared to be slightly better than TyG, with the IDI (95%CI) of 0.004 (0.001, 0.004) and 0.004 (0.001, 0.004), and the category-free NRI (95%CI) of 0.120 (0.025, 0.138) and 0.143 (0.032, 0.166), respectively. However, no significant improvement was observed in the predictive value of TyG-BMI for ASCVD compared with TyG. Similar results were found for MI and IS, and more detailed information was provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReclassification and discrimination statistics for TyG and its related parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-index (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIDI (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategory-free NRI (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASCVD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.583 (0.577, 0.590)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.584 (0.578, 0.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (-0.001, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005 (-0.024, 0.016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.612 (0.606, 0.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004 (0.001, 0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120 (0.025, 0.138)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.613 (0.607, 0.619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004 (0.001, 0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.143 (0.032, 0.166)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMyocardial Infarction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.608 (0.595, 0.621)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.596 (0.583, 0.609)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (-0.001, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.038 (-0.064, 0.017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.635 (0.623, 0.647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001 (0.000, 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.122 (0.020, 0.157)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.631 (0.619, 0.643)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001 (0.000, 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.113 (0.019, 0.145)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIschemic Stroke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.576 (0.568, 0.583)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.581 (0.574, 0.588)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020 (0.001, 0.030)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.605 (0.598, 0.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003 (0.000, 0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.115 (0.025, 0.137)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.608 (0.601, 0.614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003 (0.001, 0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145 (0.034, 0.155)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: ASCVD, atherosclerotic cardiovascular diseases; BMI, body mass index; 95%CI, 95% confidence interval); IDI, integrated discrimination improvement; IQR, interquartile range; NRI, net reclassification index; TyG, triglyceride-glucose; WC, waist circumference; WHtR, Waist-to-Height ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on data from the Kailuan cohort, this large-scale, prospective study demonstrated significant positive associations of TyG, TyG-BMI, TyG-WC and TyG-WHtR with the occurrence of ASCVD and its subtypes, which were validated by multiple sensitivity analyses like using the updated mean or time-varying measurements instead of baseline indicators. Although higher TyG and its related parameters were found to be consistently associated with increased risk of ASCVD regardless of age and gender, the effect seemed to be stronger among females and individuals who were \u0026lt;\u0026thinsp;60 years of age. In addition, TyG-WC and TyG-WHtR have showed better capacity for predicting incident ASCVD compared with single TyG.\u003c/p\u003e \u003cp\u003eThe TyG index and its combination with obesity indicators were proposed as simple yet powerful surrogate indicators for IR and metabolic health in clinical applications. Furthermore, emerging epidemiologic evidence has suggested that those indicators were closely related to systematic arterial stiffness, carotid atherosclerosis, coronary artery stenosis, and ASCVD both in the general population and among patients with cardiometabolic diseases like hypertension and diabetes. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] A cross-sectional study conducted in Taiwan, for instance, suggested that individuals with higher TyG, TyG-BMI, and TyG-WC were more likely to have elevated risk of ASCVD. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] In accordance, two community-based studies in China indicated that the three above-mentioned indicators were positively associated with increased risk of stroke. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] As for MI, the third National Health and Nutrition Examination Survey (NHANES III) suggested that elevated TyG increased the risk of subclinical myocardial injury. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] More directly, a previous study based on Kailuan cohort has demonstrated the association between baseline and long-term TyG and occurrence of MI. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Nevertheless, it was noteworthy that most of them focused on the influence of the current hottest TyG, while researches on those TyG related parameters remained relatively scattered. Furthermore, few existing studies evaluated those parameters at the same time, thus limiting further comparisons between different indicators. Recently, a cross-sectional study based on an American population has investigated the relationship of TyG and its related indicators with CVD incidence and mortality and evaluated their prediction values comprehensively. However, only a single baseline blood sample was collected in that study, and the diagnosis of CVD was established by self-reported diagnoses without exact time of onset. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] More importantly, a significant heterogeneity of the association of TyG with CVD between low/middle-income and high-income countries was found in the Prospective Urban Rural Epidemiology (PURE) study, emphasizing the necessity of more evidence from population at diverse levels of economic development.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, few researches have been conducted in developing countries to compare the correlation between TyG related parameters with ASCVD, of which prospective studies were even rarer. Benefiting from repeated measurements over the long-term follow-ups and accurate information on disease incidence, we evaluated and compared the association of TyG, TyG-BMI, TyG-WC, and TyG-WHtR with ASCVD based on a large-scale cohort in China; sensitivity analyses based on long-term average and time-varying levels further confirmed these findings. Consistent with previous publications, we observed significant and positive correlations between all four indicators and ASCVD as well as its subtypes. The HRs of per 1 IQR increase were found to be higher for TyG-WC and TyG-WHtR, which also had significantly better performance in predicting risk of ASCVD in comparison with TyG, while no obvious difference was observed between TyG-BMI and TyG. Our findings were generally in line with previous research conducted in high-income countries. For instance, a study from Korea suggested that TyG-WC had a better diagnostic efficacy for coronary artery calcification than TyG-BMI and TyG. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Similarly, findings based on the NHANES study also indicated that combining the TyG index with WC and WHtR further enhance its ability to predict CVD mortality. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Mechanisms behind the different predictive performance haven\u0026rsquo;t been fully explained yet, while the combined effects between IR and excess abdominal fat on the development of ASCVD might be a possible explanation. Both imparted insulin sensitivity, reflecting by TyG, and excessive accumulation of visceral adiposity, indicated by WC and WHtR, were closely related to chronic inflammation, endothelium dysfunction and atherosclerosis, which all contributed to the development of ASCVD. Therefore, novel indicators integrating TyG with central obesity indices might present a more accurate prediction of ASCVD than TyG index alone.\u003c/p\u003e \u003cp\u003eMoreover, our subgroup analyses revealed that the association of TyG and its related parameters with ASCVD was more pronounced in relatively younger and female individuals. The age-specific influence was consistent with previous studies. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] One potential reason might be that the elderly were more likely to take multiple medications and be exposed to more cardiovascular risk factors apart from IR or obesity, which to some extent undermined the predictive capability of TyG and other indicators for ASCVD. On the contrary, we found that the interaction of those indices with genders were heterogeneous across studies. For instance, a research conducted in the U.S showed that the relationship between TyG and 10-year risk of ASCVD was stronger in men than women. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] while the correlation was found to be higher in women than men in another study, in line with our current findings. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Therefore, further exploration is essential for deeper explanation of the gender difference.\u003c/p\u003e \u003cp\u003eThe advantage of our study merits consideration. The analysis was conducted on the basis of a large-scale, community-based cohort with repeated measurements during 15-year follow-ups, which made it possible to simultaneously compare roles of each indicator in a prospective design, thus helping explore the most suitable risk marker for predicting future risk of ASCVD. Moreover, findings of our study were relatively reliable and robust since no obvious violation was observed with series of sensitivity analyses performed. Nonetheless, our study also has some limitations to address. First, IR indicators including insulin and HbA1c were not measured in the Kailuan study, which limited further comparisons with those conventional biomarkers in the current analysis. Secondly, in spite of adequate confounding adjustments in the analyses, residual or unmeasured confounding could not be completely avoided given the observational study design. Moreover, generalizing of our findings to other populations with diverse time and social background should be cautious since only individuals living in Northern China were included in the current study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe TyG index and TyG related parameters, namely TyG-BMI, TyG-WC and TyG-WHtR, were significantly associated with the incidence of ASCVD and its major subtypes MI and IS, and the effect of those indicators on the risk of ASCVD was more pronounced in female and individuals aged less than 60 years. In addition, we demonstrated that innovative indices combining the TyG index with central obesity parameters WC, and WHtR outperformed TyG alone in prediction for incidence of ASCVD. In consideration of the increasingly severe disease burden and limited medical resource, our findings of convenient, low-cost surrogate indices of IR with good predictive capacity were of great importance for ASCVD prevention in low- and middle-income counties including China.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eASCVD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003eatherosclerotic cardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBMI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003ebody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003econfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003ehazard ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIDI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003eintegrated discrimination improvement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIQR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003einter-quartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003einsulin resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003eischemic stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003emyocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eNRI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003enet reclassification index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRCS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003erestricted cubic splines\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTyG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003etriglyceride-glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eWC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003ewaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.97463002114165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eWHtR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.02536997885835%\" valign=\"top\"\u003e\n \u003cp\u003ewaist-to-height ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole study complies with the Declaration of Helsinki and has been approved by the Ethics Committees of both Kailuan General Hospital and Beijing Tiantan Hospital. Written informed consent was obtained from all participants before data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Beijing Natural Science Foundation Haidian original innovation joint fund (L222123), National Key Research and Development Program of China (2022YFC3600600), Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (CCMU2022ZKYXZ009), Fund for Young Talents of Beijing Medical Management Center (QML20230505), and the high-level public health talents (xuekegugan-02-47).\u003c/p\u003e\n\u003cp\u003eThe sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSW and AW had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eConcept and design: SW, AW\u003c/li\u003e\n \u003cli\u003eAcquisition, analysis, or interpretation of data: XX, SC, XT, QX\u003c/li\u003e\n \u003cli\u003eDrafting of the manuscript: XX\u003c/li\u003e\n \u003cli\u003eCritical revision of the manuscript for important intellectual content: All authors.\u003c/li\u003e\n \u003cli\u003eStatistical analysis: XX, SC, YZ, XZ, JL\u003c/li\u003e\n \u003cli\u003eObtained funding: AW\u003c/li\u003e\n \u003cli\u003eAdministrative, technical, or material support: SW, AW\u003c/li\u003e\n \u003cli\u003eSupervision: SW, AW\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge all staff and participants of the Kailuan study for their important participation and contribution.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u0026ndash;2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982\u0026ndash;3021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Hu M, Liu H, Zhang X, Li H, Zhou F, Liu YM, Lei F, Qin JJ, Zhao YC, et al. Global Burden of Disease Study 2019 suggests that metabolic risk factors are the leading drivers of the burden of ischemic heart disease. Cell Metab. 2021;33(10):1943\u0026ndash;56. e1942.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdeva-Andany MM, Mart\u0026iacute;nez-Rodr\u0026iacute;guez J, Gonz\u0026aacute;lez-Luc\u0026aacute;n M, Fern\u0026aacute;ndez-Fern\u0026aacute;ndez C, Castro-Quintela E. Insulin resistance is a cardiovascular risk factor in humans. Diabetes Metab Syndr. 2019;13(2):1449\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab. 2008;294(1):E15\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToro-Huamanchumo CJ, Urrunaga-Pastor D, Guarnizo-Poma M, Lazaro-Alcantara H, Paico-Palacios S, Pantoja-Torres B, Ranilla-Seguin VDC, Benites-Zapata VA. Triglycerides and glucose index as an insulin resistance marker in a sample of healthy adults. Diabetes Metab Syndr. 2019;13(1):272\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang A, Wang G, Liu Q, Zuo Y, Chen S, Tao B, Tian X, Wang P, Meng X, Wu S, et al. Triglyceride-glucose index and the risk of stroke and its subtypes in the general population: an 11-year follow-up. Cardiovasc Diabetol. 2021;20(1):46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian X, Zuo Y, Chen S, Liu Q, Tao B, Wu S, Wang A. Triglyceride-glucose index is associated with the risk of myocardial infarction: an 11-year prospective study in the Kailuan cohort. Cardiovasc Diabetol. 2021;20(1):19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao H, Zhou Z, Yang S, Zhang Y. The association of triglyceride-glucose index and related parameters with hypertension and cardiovascular risk: a cross-sectional study. Hypertens Res 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReho JJ, Rahmouni K. Oxidative and inflammatory signals in obesity-associated vascular abnormalities. Clin Sci (Lond). 2017;131(14):1689\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444(7121):875\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Zhang T, He S, Jia S, Zhang Z, Ye R, Yang X, Chen X. Association of metabolic syndrome with TyG index and TyG-related parameters in an urban Chinese population: a 15-year prospective study. Diabetol Metab Syndr. 2022;14(1):84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue Y, Xu J, Li M, Gao Y. Potential screening indicators for early diagnosis of NAFLD/MAFLD and liver fibrosis: Triglyceride glucose index-related parameters. Front Endocrinol (Lausanne). 2022;13:951689.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng W, Kong F, Chen S. Comparison of the predictive value of four insulin resistance surrogates for the prevalence of hypertension: a population-based study. Diabetol Metab Syndr. 2022;14(1):137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Liu J, Cheng Z, Zhong Y, Chen X, Song W. Triglyceride glucose-body mass index and the risk of diabetes: a general population-based cohort study. Lipids Health Dis. 2021;20(1):99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Zhang J, Chen C, Qin P, Zhang M, Shi X, Yang Y, Lu J, Sun L, Hu D. Comparison of six surrogate insulin resistance indexes for predicting the risk of incident stroke: The Rural Chinese Cohort Study. Diabetes Metab Res Rev. 2022;38(7):e3567.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang YC, Huang JC, Lin CI, Chien HH, Lin YY, Wang CL, Liang FW, Dai CY, Chuang HY. Comparison of Innovative and Traditional Cardiometabolic Indices in Estimating Atherosclerotic Cardiovascular Disease Risk in Adults. Diagnostics (Basel) 2021, 11(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDang K, Wang X, Hu J, Zhang Y, Cheng L, Qi X, Liu L, Ming Z, Tao X, Li Y. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003\u0026ndash;2018. Cardiovasc Diabetol. 2024;23(1):8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Deng B, Huang Q, Tu R, Yu F, Xia J, Feng J. Comparison of seven surrogate insulin resistance indexes for predicting the prevalence of carotid atherosclerosis in normal-weight individuals. Front Public Health. 2023;11:1241523.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu S, An S, Li W, Lichtenstein AH, Gao J, Kris-Etherton PM, Wu Y, Jin C, Huang S, Hu FB, et al. Association of Trajectory of Cardiovascular Health Score and Incident Cardiovascular Disease. JAMA Netw Open. 2019;2(5):e194758.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Yuan Y, Zheng M, Pan A, Wang M, Zhao M, Li Y, Yao S, Chen S, Wu S, et al. Association of Age of Onset of Hypertension With Cardiovascular Diseases and Mortality. J Am Coll Cardiol. 2020;75(23):2921\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou YF, Chen S, Wang G, Chen S, Zhang YB, Chen JX, Tu ZZ, Liu G, Wu S, Pan A. Effectiveness of a Workplace-Based, Multicomponent Hypertension Management Program in Real-World Practice: A Propensity-Matched Analysis. Hypertension. 2022;79(1):230\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang YH, Wang J, Chen SH, Li JQ, Lu QD, Vitiello MV, Wang F, Tang XD, Shi J, Lu L, et al. Association of Longitudinal Patterns of Habitual Sleep Duration With Risk of Cardiovascular Events and All-Cause Mortality. JAMA Netw Open. 2020;3(5):e205246.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas AM, Pajak A. Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. Circulation. 1994;90(1):583\u0026ndash;612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpecial Report from the World Health Organization. Stroke-1989. Recommendations on stroke prevention, diagnosis, and therapy. Report of the WHO Task Force on Stroke and other Cerebrovascular Disorders. Stroke, 20(10):1407\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePM G, TM T. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81:515\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi F, Wang Y, Shi B, Sun S, Wang S, Pang S, Wu X. Association between the cumulative average triglyceride glucose-body mass index and cardiovascular disease incidence among the middle-aged and older population: a prospective nationwide cohort study in China. Cardiovasc Diabetol. 2024;23(1):16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu W, Zhao H, Han X, Liu J, Li H, Sun J, Xing A, Chen S, Wu S, Wu Y. Relationship between early-onset stroke and triglyceride-glucose index among young Chinese adults. Lipids Health Dis. 2023;22(1):3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang S, Wang C, Zhang J, Liu Z, Bai Y, Chen Z, Huang H, He Y. Triglyceride-glucose index and coronary artery disease: a systematic review and meta-analysis of risk, severity, and prognosis. Cardiovasc Diabetol. 2023;22(1):170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu S, Xu L, Wu M, Chen S, Wang Y, Tian Y. Association between triglyceride-glucose index and risk of arterial stiffness: a cohort study. Cardiovasc Diabetol. 2021;20(1):146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee EY, Yang HK, Lee J, Kang B, Yang Y, Lee SH, Ko SH, Ahn YB, Cha BY, Yoon KH, et al. Triglyceride glucose index, a marker of insulin resistance, is associated with coronary artery stenosis in asymptomatic subjects with type 2 diabetes. Lipids Health Dis. 2016;15(1):155.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Q, Yin L, Liu Z, Wei M, Feng J, Huang Q, Liu Y, Liu Z, Xia J. Association of novel lipid indicators with the risk of stroke among participants in Central China: a population-based prospective study. Front Endocrinol (Lausanne). 2023;14:1266552.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Wu M, Xu J, Sha D, Xu B, Kang L. Association between Triglyceride and glycose (TyG) index and subclinical myocardial injury. Nutr Metab Cardiovasc Dis. 2020;30(11):2072\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez-Jaramillo P, Gomez-Arbelaez D, Martinez-Bello D, Abat MEM, Alhabib KF, Avezum \u0026Aacute;, Barbarash O, Chifamba J, Diaz ML, Gulec S, et al. Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study. Lancet Healthy Longev. 2023;4(1):e23\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho YK, Lee J, Kim HS, Kim EH, Lee MJ, Yang DH, Kang JW, Jung CH, Park JY, Kim HK et al. Triglyceride Glucose-Waist Circumference Better Predicts Coronary Calcium Progression Compared with Other Indices of Insulin Resistance: A Longitudinal Observational Study. J Clin Med 2020, 10(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarzegar N, Tohidi M, Hasheminia M, Azizi F, Hadaegh F. The impact of triglyceride-glucose index on incident cardiovascular events during 16 years of follow-up: Tehran Lipid and Glucose Study. Cardiovasc Diabetol. 2020;19(1):155.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQu H, Long LZ, Chen L, Wu HT, Fu CG, Zhang SS. Triglyceride-glucose index and estimated 10-year risk of a first hard cardiovascular event. Front Cardiovasc Med. 2022;9:994329.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride glucose (TyG), Triglyceride glucose-body mass index (TyG-BMI), Triglyceride glucose-waist circumference (TyG-WC), Triglyceride glucose-waist-height ratio (TyG-WHtR), Atherosclerotic cardiovascular disease (ASCVD)","lastPublishedDoi":"10.21203/rs.3.rs-4157386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4157386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e Triglyceride glucose (TyG) index and its related parameters has been introduced as cost-effective surrogate indicators of insulin resistance, while prospective evidence on their effects on risk of atherosclerotic cardiovascular disease (ASCVD) remained scattered and inconsistent. We aimed to evaluate the association of TyG with new-onset ASCVD and further compare their predictive capacity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/em\u003e A total of 95,342 ASCVD-free participants were enrolled from the Kailuan study. TyG and its related parameters were defined by fasting blood glucose, triglyceride, BMI, WC and WHtR. The primary outcome was incident ASCVD, comprising myocardial infarction (MI) and ischemic stroke (IS). Cox proportional hazard models and restricted cubic spline analyses were adopted to investigate the association between each index and ASCVD. The C-index, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used for comparison of their predictive value for ASCVD.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e During a median follow-up of 15.0 years, 8,031 new cases of ASCVD were identified. The incidence rate of ASCVD increased along with elevated levels of each index, and the relationships were found to be nonlinear in the RCS analyses. The hazard ratio (HR) and 95% confidence interval (95% CI) for ASCVD was 1.39 (1.35, 1.43), 1.46 (1.41, 1.50), 1.50 (1.46, 1.55), and 1.52 (1.48, 1.57) per 1 IQR increase of baseline TyG, TyG-BMI, TyG-WC, and TyG-WHtR, respectively, and the association were more pronounced for females and younger individuals aged \u0026lt;60 years (\u003cem\u003eP \u003c/em\u003e\u003csub\u003efor interaction\u003c/sub\u003e\u0026lt;0.05). Using the updated mean or time-varying measurements instead of baseline indicators did not significantly alter the primary findings. Additionally, TyG-WC and TyG-WHtR showed better performance in predicting risk of ASCVD than TyG, with the IDI (95% CI) of (0.001, 0.004) and 0.004 (0.001, 0.004) and the category-free NRI (95% CI) of 0.120 (0.025, 0.138) and 0.143 (0.032, 0.166), respectively. Similar findings were observed for MI and IS.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e Both the TyG index and its related parameters were significantly and positively associated with ASCVD. TyG-WC and TyG-WHtR had better performance in predicting incident ASCVD than TyG, which might be more suitable indices for risk stratification and enhance the primary prevention of ASCVD.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Association of triglyceride-glucose index and its related parameters with atherosclerotic cardiovascular disease: evidence from a 15-year follow-up of Kailuan cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-29 18:33:37","doi":"10.21203/rs.3.rs-4157386/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-14T17:29:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-12T12:31:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-04T18:04:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-28T10:39:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1bf21dde-8c5f-421c-a3b1-cbcd1be31ea7","date":"2024-03-28T08:53:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1977d347-2804-4e85-9142-00472c34d174","date":"2024-03-27T18:22:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1f06e846-e988-4c0d-9798-564f6aca6172","date":"2024-03-27T06:43:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-26T23:59:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-25T16:04:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-25T13:20:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2024-03-24T09:41:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d274bf6a-240a-437e-ac35-b6a90b60b224","owner":[],"postedDate":"March 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-20T01:02:14+00:00","versionOfRecord":{"articleIdentity":"rs-4157386","link":"https://doi.org/10.1186/s12933-024-02290-3","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2024-06-19 01:02:14","publishedOnDateReadable":"June 19th, 2024"},"versionCreatedAt":"2024-03-29 18:33:37","video":"","vorDoi":"10.1186/s12933-024-02290-3","vorDoiUrl":"https://doi.org/10.1186/s12933-024-02290-3","workflowStages":[]},"version":"v1","identity":"rs-4157386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4157386","identity":"rs-4157386","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}