Relationship Between Indices of Insulin Resistance and incident Type 2 Diabetes Mellitus in Chinese Adults

Relationship Between Indices of Insulin Resistance and incident Type 2 Diabetes Mellitus in Chinese Adults | 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 Relationship Between Indices of Insulin Resistance and incident Type 2 Diabetes Mellitus in Chinese Adults Yuhan Qin, Yong Qiao, Gaoliang Yan, Dong Wang, Chengchun Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3952991/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2024 Read the published version in Endocrine → Version 1 posted 8 You are reading this latest preprint version Abstract Background Insulin resistance (IR) is a pivotal pathogenesis characteristic of type 2 diabetes mellitus (T2DM). The current study aimed to explore the association between three surrogate biomarkers for IR, including triglyceride/high-density lipoprotein cholesterol ratio (TG/HDL-c), triglyceride-glucose (TyG), and triglyceride glucose-body mass index (TyG-BMI), and T2DM incidence and compare the predictive value of these parameters in T2DM. Methods A total of 116855 Chinese adults aged over 20 without diabetes were included in the present study. T2DM incident rates were compared among participants with different levels of TG/HDL, TyG, and TyG-BMI. Multivariate Cox regression analysis and restricted cubic spine were utilized to investigate the association between these IR indicators and T2DM. The T2DM risk across different quartiles of IR parameters during follow-up was compared using Kaplan-Meier curves. The receiver operating characteristic analysis was used to investigate the predictive potential of each IR indicator for future T2DM. Stratification analyses were performed to explore the impact of age and sex on the association between IR and T2DM risk. Results 2685 participants developed T2DM during a median follow-up of 2.98 years. The T2DM incidence rate dramatically increased with the increasing quartiles of TG/HDL-c, TyG, and TyG-BMI. The adjusted hazard ratios (HR) of incident T2DM were 1.177, 2.766, and 1.1018, for TG/HDL-c, TyG, and TyG-BMI, respectively. There were significant increasing trends of T2DM across the quartiles of TG/HDL-c, TyG, and TyG-BMI. The HRs of new-onset T2DM in the highest quartiles versus the lowest quartile of TG/HDL-c, TyG, and TyG-BMI were 3.298 (95% CI: 2.615–4.610), 8.402 (95% CI: 6.176–11.429), and 8.468 (95% CI: 6.157–11.646). RCS revealed the nonlinear relationship between and T2DM risk. Significant interactions between TyG and T2DM risk were observed between age groups. The correlations between IR and T2DM were more pronounced in subjects aged less than 40. TyG-BMI had the highest predictive value for incident T2DM (AUC = 0.774), with a cut-off value of 213.289. Conclusion TG/HDL-c, TyG, and TyG-BMI index were all significantly positively associated with higher risk for future T2DM. Baseline TyG-BMI level had high predictive value for the identification of T2DM. insulin resistance TG/HDL-c TyG TyG-BMI diabetes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Type 2 diabetes mellitus (T2DM) remains a serious public health issue with a huge disease burden worldwide [ 1 ]. The prevalence of T2DM has still increasing during the last decade, with increased T2DM-associated complications and mortality [ 2 ]. The Global Burden of Disease Study 2021 reported 529 million diabetes globally and it was estimated there will be 1.31 billion diabetic people in 2050 [ 3 ]. The estimated overall standardized prevalence of diabetes in China reached 12.4% in 2018. Additionally, the arithmetic on awareness, treatment, and control rates was discouraging [ 4 ]. Insulin resistance (IR), defined as impaired insulin sensitivity and reactivity of insulin target organs or tissues, has been recognized as a typical characteristic of T2DM and the vital mechanism involved in the development of T2DM [ 5 ]. Hyper-insulinemic euglycemic clamp (HEC) technique is the gold standard method for IR evaluation. However, the clinical application is impractical due to the invasiveness, complexity, and cost of the procedure [ 6 ]. Other simple approaches without measurement of insulin were established to assess IR in recent years. For instance, triglycerides to high-density lipoprotein cholesterol ratio (TG/HDL-C) was recognized as an IR surrogate marker [ 7 , 8 ]. TyG index and TyG-related indices are well-acknowledged simple IR indicators and are widely used in clinical studies [ 9 ]. TyG could sensitively and specifically reflect insulin sensitivity [ 10 ]. In a prospective study enrolling 511 Chinese participants without diabetes, TyG-body mass index (TyG-BMI) had the highest predictive potential for IR among several simple and useful IR surrogate makers [ 11 ]. Previous reports have found positive associations between TG/HDL-c and T2DM risk and TG/HDL-c was identified as a strong predictor of T2DM [ 12 , 13 ]. TyG-related parameters including TyG, TyG-BMI, etc. also showed the good predictive potential of future T2DM among the population from different regions [ 14 , 15 ]. However, evidence from the large-sample population on the associations between these three IR indicators and incident T2DM risk in participants free of T2DM at baseline level is rare. Additionally, a comparison of the 3 IR parameters on the predictive value of incident T2DM is limited. Therefore, the present study aims to investigate the relationship between the three IR indicators and the risk of incident T2DM in a large retrospective cohort study and to compare the predictive performance of each IR surrogate index. 2. Methods and Materials 2.1 Study population Data used in the study were downloaded from a publicly available database (https://datadryad.org/stash/dataset/doi: 10.5061/dryad.ft8750v ) [ 16 ]. The dataset could be used for secondary analyses according to the instructions of the Dryad database. The original research was authorized by the Rich Healthcare Group Review Board. Therefore, no further research ethics and informed consent were needed in the current research due to the public database property. Briefly, 211833 participants aged over 20 who underwent health check at Rich Healthcare in China were recruited in the original study from 2010–2016, the subjects were without diabetes at baseline and were followed up with a median of 2.98 years. As shown in Fig. 1, 116855 individuals were finally included to explore the association between 3 IR indicators and T2DM after excluding missing data for TG and HDL-c. 2.2 Data collection and definition Demographic data and family history of included individuals comprising age, sex, smoking history, drinking history, and family history of diabetes were obtained through questionnaires. Anthropometric data were measured by trained staff. BMI was calculated by weight (kg)/height(m) 2 . Systolic and diastolic blood pressure at rest were checked using a mercury sphygmomanometer. Fasting venous blood samples were collected from participants to detect the levels of fasting plasma glucose (FPG), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and serum creatinine (Scr) through an autoanalyzer (Beckman AU5800, USA). The formula for calculating the TyG index was as follows: TyG = Ln[FPG (mg/dL)×TG (mg/dL)/2][ 17 ]. TyG-BMI was calculated using the following formula: TyG-BMI = Ln[FPG (mg/dL)×TG (mg/dL)/2]×BMI[ 18 ]. Diabetes was defined as FPG ≥ 7mmol/L or self-reported diabetes during follow-up. according to the 2023 American Diabetes Association recommendation [ 19 ]. 2.3 Statistical analysis Normally distributed continuous data were presented as means with standard deviations, skewed distributed data were shown as medians with interquartile range. Categorical variables were presented using frequencies with percentages. Student’s t-test and Mann–Whitney U test was used for data with normal and highly skewed distribution. The categorical variables were compared using the Chi-square test. Restricted cubic splines (RCS) were employed to reflect the nonlinear association between IR indexes and the probability of incident T2DM. Kaplan-Meier curves were drawn to compare the accumulative T2DM risk across different quartiles of IR parameters. The relationship between these IR indicators and incident T2DM was ascertained using Cox proportional hazard regression analysis, then the hazard ratios (HR) and 95% CI of one-unit increase and higher quartiles over the lowest quartiles in these IR parameters for T2DM were calculated. Model 1 was a crude model; Age, sex, smoking history, drinking history, and family history of diabetes were adjusted in model 2; BMI, SBP, DBP, BUN, Scr, ALT, and AST were further adjusted in model 3. Age and male were the ranked 2 risk factors for T2DM [ 19 ]. Consequently, subgroup analysis was carried out to assess whether age and sex affected the association between IR and T2DM. In detail, participants were divided according to age (< 40 years and ≥ 40 years) and gender (males and females). We assessed these IR parameters' predicting value for incident T2DM using receiver operating characteristic (ROC) curves. Calculations were made for sensitivity, specificity, and area under the curve (AUC). All statistical analyses were performed using SPSS 25.0 and R Software 4.2.2. p < 0.05 was set as statistically significant. 3. Results 3.1 Baseline characteristics of the study population 116855 participants without diabetes at baseline were included, with an average age of 44.08 ± 12.93. Among these subjects, 62868 (53.8%) were males, during a median follow-up year of 2.98, 2685 (2.30%) developed incident diabetes. The baseline characteristics of the population were compared and presented in Table 1 . In comparison with nondiabetic subjects, diabetic subjects tended to be older, had higher levels of SBP, DBP, FPG, TG, TC, LDL-c, ALT, AST, BUN, Sc, and lower HDL-c levels at baseline levels. Furthermore, participants with diabetes were more likely to have smoking and drinking behavior, and a diabetes family history (p < 0.05). In addition, TG/HDL-c, TyG, and TyG-BMI were all higher in the diabetes group (p < 0.001) (Table 2 ). Table 1 Baseline and laboratory characteristics of participants with and without diabetes Variables Total (n = 116855) Diabetes (n = 2685) Non-diabetes (n = 114170) p value Age, years 44.08 ± 12.93 56.63 ± 16.62 43.78 ± 12.79 < 0.001 Male, n (%) 62868 (53.8%) 1890 (70.4%) 60978 (53.4%) < 0.001 BMI, kg/m 2 23.35 ± 3.30 26.03 ± 3.43 23.28 ± 3.27 < 0.001 Current smoker, n (%) 547 (0.5%) 22 (0.8%) 525 (0.5%) 0.007 Current drinker, n (%) 878 (0.8%) 31 (1.2%) 847 (0.7%) 0.014 Family history of diabetes, n (%) 2640 (2.3%) 99 (3.7%) 2541 (2.2%) < 0.001 SBP, mmHg 119.43 ± 16.67 131.95 ± 18.76 119.13 ± 16.51 < 0.001 DBP, mmHg 74.45 ± 10.97 80.57 ± 11.92 74.29 ± 10.91 < 0.001 FPG, mg/dl 4.95 ± 0.61 5.92 ± 0.71 4.92 ± 0.58 < 0.001 TG, mmol/L 1.10 (0.76–1.66) 1.70 (1.17–2.50) 1.10 (0.76–1.64) < 0.001 TC, mmol/L 4.79 ± 0.90 5.07 ± 0.95 4.78 ± 0.89 < 0.001 HDL-c, mmol/L 1.37 ± 0.30 1.29 ± 0.34 1.38 ± 0.30 < 0.001 LDL-c, mmol/L 2.77 ± 0.68 2.90 ± 0.70 2.77 ± 0.68 < 0.001 ALT, U/L 18.10 (13.00-27.60) 25.00 (17.00–38.00) 18.00 (13.00-27.20) < 0.001 AST, U/L 22.00 (18.60–26.80) 25.00 (21.00-31.70) 22.00 (18.60–26.60) < 0.001 BUN, mmol/L 4.69 ± 1.12 5.01 ± 1.29 4.68 ± 1.17 < 0.001 Scr, µmol/L 70.34 ± 15.81 73.08 ± 16.43 70.27 ± 15.80 < 0.001 Data were presented as mean ± SE or median (IQR) for continuous variables or numbers (percentages) for categorical variables. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TG, triglyceride; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; Scr, serum creatinine. Table 2 Insulin resistance indices of participants with and without diabetes Variables Total (n = 116855) Diabetes (n = 2685) Non-diabetes(n = 114170) p value TG/HDL-c 0.82 (0.52–1.34) 1.36 (0.87–2.18) 0.81 (0.52–1.32) < 0.001 TyG 8.41 ± 0.61 9.01 ± 0.61 8.40 ± 0.61 < 0.001 TyG-BMI 197.29 ± 36.96 234.98 ± 37.72 196.41 ± 36.48 < 0.001 TG/HDL: triglyceride to high-density lipoprotein ratio, TyG: triglyceride-glucose, TyG-BMI: triglyceride glucose-body mass index. 3.2 The T2DM incidence rate in subjects with different levels of IR parameters 2685 of 116855 subjects developed T2DM during the follow-up of 362268.4 person-years, the overall incidence rate of T2DM was 7.41 cases/1000 person-years. Participants were then divided into four quartiles based on the baseline value of TG/HDL-c, TyG, and TyG-BMI. Notably, T2DM incidence rate dramatically increased with the quartiles of these IR indicators increasing. The T2DM incidence rate had reached 14.85, 18.79, and 19.15 per 1000 person-years in the 4th quartile (Table 3 , Fig. 2). Table 3 Incidence rate for the development of T2DM by TG/HDL-c, TyG and TyG-BMI Variables TG/HDL-c Q1 Q2 Q3 Q4 No. of cases 193 413 715 1364 No. of person-years 90472.84 89761.55 90173.71 91860.34 Incidence rate * 2.13 4.60 7.93 14.85 TyG Q1 Q2 Q3 Q4 No. of cases 107 266 634 1678 No. of person-years 93157.98 90491.09 89339.53 89279.84 Incidence rate * 1.15 2.94 7.10 18.79 TyG-BMI Q1 Q2 Q3 Q4 No. of cases 99 232 630 1724 No. of person-years 91430.24 90761.2 90300.48 90008.52 Incidence rate * 1.08 2.56 6.98 19.15 * per 1000 person-years. TG/HDL: triglyceride to high-density lipoprotein ratio, TyG: triglyceride-glucose, TyG-BMI: triglyceride glucose-body mass index. 3.3 Univariate analysis of incident T2DM Univariate analysis of risk factors for T2DM are displayed in Table 4 . Positive associations were found between age, male, BMI, smoking, drinking, diabetes family history, SBP, DBP, FPG, TG, TC, LDL-c, ALT, AST, BUN, Scr, and T2DM (p < 0.05), while negative correlation was found between HDL-c and T2DM risk (HR = 0.575, 95% CI: 0.506–0.654, p < 0.001). Of note, these three IR parameters were all positively correlated with T2DM. Table 4 Univariate analysis of incident T2DM Variables HR 95% CI P value Age 1.064 1.062–1.067 < 0.001 Male 2.017 1.856–2.191 < 0.001 BMI 2.222 2.212–2.233 < 0.001 Smoking 1.659 1.090–2.524 0.018 Drinking 1.738 1.220–2.476 0.002 Family history of diabetes 1.403 1.148–1.715 0.001 SBP 1.037 1.035–1.039 < 0.001 DBP 1.042 1.039–1.045 < 0.001 FPG 9.982 9.452–10.542 < 0.001 TG 1.260 1.245–1.275 < 0.001 TC 1.338 1.239–1.390 < 0.001 HDL-c 0.575 0.506–0.654 < 0.001 LDL-c 1.348 1.281–1.419 < 0.001 ALT 1.004 1.004–1.004 < 0.001 AST 1.006 1.005–1.007 < 0.001 BUN 1.213 1.182–1.244 < 0.001 Scr 1.007 1.006–1.008 < 0.001 TG/HDL-c 1.245 1.229–1.262 < 0.001 TyG 3.851 3.662–4.050 < 0.001 TyG-BMI 1.022 1.022–1.023 < 0.001 HR: hazard ratio; CI: confidence interval; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TG, triglyceride; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; Scr, serum creatinine; TG/HDL: Triglyceride to high-density lipoprotein ratio, TyG: triglyceride-glucose, TyG-BMI: triglyceride glucose-body mass index. 3.4 The association between IR indicators and T2DM Cox regression analyses were conducted to assess the impact of three IR parameters on the incidence of T2DM (Table 5 ). Three models (crude model, partially and fully-adjusted model) were constructed to evaluate the effect of IR indicators on T2DM. The covariates in fully-adjusted model 3 were selected from the results of univariate analysis, which included age, sex, BMI, smoking and drinking history, diabetes family history, SBP, DBP, ALT, AST, BUN, and Scr. 1.177 (95% CI: 1.140–1.216, p < 0.001), 2.776 (95% CI: 2.551–3.020, p < 0.001), and 1.108 (95% CI: 1.016–1.019, p < 0.001) fold risk for developing T2DM were found in subjects with one-unit increase of TG/HDL-c, TyG, and TyG-BMI in model 3. Table 5 Multivariate-adjusted hazard ratios of TG/HDL-c, TyG, and TyG-BMI for incident T2DM during follow-up. Variables Model 1 Model 2 Model 3 HR (95% CI ) P value HR (95% CI ) P value HR (95% CI ) P value TG/HDL-c 1.245(1.229–1.262) < 0.001 1.218(1.197–1.238) < 0.001 1.177 (1.140–1.216) < 0.001 TyG 3.851(3.662–4.050) < 0.001 3.213 (3.038–3.399) < 0.001 2.776 (2.551–3.020) < 0.001 TyG-BMI 1.022(1.022–1.023) < 0.001 1.020 (1.020–1.021) < 0.001 1.018 (1.016–1.019) < 0.001 HR: hazard ratio; CI: confidence interval. Model 1: unadjusted. Model 2: adjusted for age, sex, smoking history, drinking history, and family history of diabetes. Model 3: adjusted for age, sex, BMI, SBP, DBP, smoking history, drinking history, family history of diabetes, ALT, AST, BUN, and Scr. TG/HDL-c, TyG, and TyG-BMI values were categorized into 4 quartiles as follows: TG/HDL-c: Q1 (≤ 0.52), Q2 (0.52–0.82), Q3 (0.82–1.34), and Q4 (> 1.34); TyG: Q1 (≤ 7.97), Q2 (7.97–8.37), Q3 (8.37–8.81), and Q4 (> 8.81); TyG-BMI: Q1 (≤ 169.19), Q2 (169.19-193.88), Q3 (193.88-221.19), and Q4 (> 221.19). The risk for T2DM increased dramatically with the increasing quartiles of these IR parameters (Fig. 3). Compared with the 1st quartile, participants in the 4th quartile of TG/HDL-c, TyG, and TyG-BMI had 3.298 (95% CI: 2.615–4.610), 8.402 (95% CI: 6.176–11.429), and 8.468 (95% CI: 6.157–11.646) fold risk for incident T2DM, respectively (p < 0.001). The cumulative T2DM incidence in participants with different levels of IR was compared using Kaplan-Meier curves. As shown in Fig. 4, the cumulative incidences of T2DM were remarkably different among these quartiles (Log-rank test, p < 0.001). The nonlinear association between these three IR indicators and T2DM was evaluated by RCS and presented in Fig. 5 (p for overall < 0.001, p for nonlinear < 0.001). 3.5 Stratification analysis Subgroup stratification analyses by age and gender was then performed to assess the effect of age and sex on the relationship between IR and diabetes. As displayed in Fig. 6, significant interactions between TyG and T2DM risk were observed between sex subgroups (p for interaction = 0.043). In addition, The association between IR and T2DM differs between age ≥ 40 and age < 40 group. The correlations between these three IR parameters and T2DM were more pronounced in subjects aged less than 40. One-unit increase of TG/HDL-c, TyG, and TyG-BMI were associated with 1.165, 4.207, and 1.025 fold risk for T2DM, respectively. 3.6 ROC Predictive value of IR indicators in the identification of T2DM ROC curves were constructed to assess the predictive value of these three IR-associated parameters for T2DM, respectively. The area of the curve (AUC) and 95% confidence interval, cut-off value, corresponding sensitivity, and specificity are listed in Table 6 and Fig. 7. Among these three IR parameters, TyG-BMI had the highest AUC for predicting T2DM, with a cut-off value of 213.289. Table 6 ROC curve analysis of the insulin resistance parameters in predicting T2DM Variables AUC 95%CI Cut-off value Sensitivity Specificity TG/HDL 0.699 0.696–0.702 1.009 0.678 0.624 TyG 0.765 0.763–0.768 8.567 0.777 0.631 TyG-BMI 0.774 0.772–0.777 213.289 0.726 0.695 TG/HDL: triglyceride to high-density lipoprotein ratio, TyG: triglyceride-glucose, TyG-BMI: triglyceride glucose-body mass index. 4. Discussion This study explored the associations between three IR surrogates and T2DM risk and comprehensively compared their predictive value of T2DM in 116855 Chinese physical examination population. The results showed T2DM risk significantly increased with the increasing levels of these IR indicators. These three IR parameters well all independent predictors of future T2DM. Among the three IR parameters, TyG-BMI had the highest predictive ability of incident T2DM risk. Impaired insulin production and IR in the insulin target tissues are key cellular defects in the development of T2DM [ 20 ]. IR is initially manifested as hyperinsulinemia, followed by reduced insulin secretion and hyperglycemia, leading to the progression of T2DM and associated complications [ 21 ]. Therefore, early accurate identification of IR before the diagnosis of T2DM are of great clinical significance. Although HEC is the “gold standard” method for IR measurement, the cost and complexity hinder its application. Therefore, numerous studies have been carried out to find simple IR surrogate markers with high diagnostic values on diabetes in the past decade. Glucose and lipid metabolism is closely related to each other. TG/HDL-c was regarded as the lipid parameter with the highest predictive value for T2DM. However, the follow-up period was not taken into account [ 22 ]. Another study revealed TG/HDL-c was a strong predictor for new-onset T2DM in a Japanese cohort [ 12 ]. In a population-based Rotterdam Study, TyG index was closely correlated with T2DM risk [ 23 ]. Findings from the meta-analysis also showed TyG index significantly enhanced the risk of T2DM [ 24 ]. Other metabolism-related parameters are also involved in the development of IR and T2DM. Therefore, increasing studies pay attention to the diagnostic value of TyG-related parameters [ 14 ]. Similarly, the current study demonstrated a positive association between the three IR indicators and new-onset T2DM. Furthermore, we for the first time compared the predictive ability of the three IR indices, and found TyG-BMI had the highest AUC for future T2DM, which was consistent with the findings of another Chinese prospective study [ 25 ]. However, the research findings are still controversial. A cohort study in Hebei General Hospital showed TyG had a higher predictive value than TyG-related indicators [ 26 ]. The differences in the included study population and follow-up period might explain the controversial research results with previous reports. Furthermore, the subgroup analysis revealed significant disparities in the relationship between these three IR parameters and T2DM risk stratified by age and sex. Specifically, the association was particularly pronounced among participants aged less than 40. The sex disparity also existed in the relationship between TyG and T2DM. Similarly, the association between TyG and atherosclerosis also differs between males and females [ 27 ]. Previous studies clarified women were more vulnerable to high TG levels, the protective impact of estrogen is attenuated under dysregulated lipid concentrations [ 28 ]. The relationship between IR parameters and T2DM in subjects under 40 requires further investigation. The excellent performance of TyG and TyG-BMI in the prediction of T2DM might be attributed to these three IR parameters reflecting both the metabolism of glucose and lipids. Both lipotoxicity and glucotoxicity play a vital role in T2DM [ 29 ]. It is well-acknowledged islet tissue is vulnerable to oxidative stress injury and toxicity caused by hyperglycemia [ 30 ]. Notably, the deposition of TG in islets also damages islet cells and impacts islet function. The increased TG levels in peripheral organ tissue will affect the utilization of glucose and aggravate peripheral IR [ 31 ]. Additionally, free fatty acid levels (FFAs), the metabolites of TG, increase glucagon secretion [ 32 ], trigger inflammation, as well as disturb insulin receptor entry by changing the fatty acid of cell membranes [ 33 ]. While HDL-c promotes insulin secretion through the inhibition of β-cell apoptosis and promotion of reverse cholesterol transport [ 34 ]. HDL also promotes glucose uptake in skeletal muscle cells via the AMPK pathway [ 35 ]. The present study has some strengths as follows: (1) The current study included 116855 subjects aged over 20 and followed up for a median of 2.98 years. (2) This is a longitudinal cohort but not a cross-sectional study. (3) We further compared the predictive value of baseline three IR indicators for the new onset of T2DM. (4) Stratified analysis was performed to explore the effect of age and sex on the relationship between IR parameters and T2DM risk. This study also has some limitations: (1) There are numerous kinds of IR parameters, however, we only explored three indicators of IR in the research due to limited data that could be obtained from the dataset. Insulin and many other parameters were absent in the original research. (2) HbA1c level and 2h-postprandial blood glucose levels were all absent, which might contribute to underestimated T2DM incidence. (3) This is a Chinese cohort study including healthy adults, further validation is needed for the extrapolation to the general population. 5. Conclusion It is of great significance to explore IR indicators for identification of people at high risk of developing T2DM. The results of this study indicated TG/HDL-c, TyG, and TyG-BMI were all independently associated with increased T2DM risk. TyG-BMI had the most superior predictive ability on future T2DM. TyG-BMI might act as a simple parameter for predicting future T2DM in clinical practice. Declarations Acknowledgements All authors thank all staff of the original study conducted by Rich Healthcare group, and we appreciate DRYAD for providing a platform for sharing data. Author Contributions YHQ designed the study, YHQ, GLY collected the data, YHQ, YQ, DW analyzed the data, YHQ wrote the manuscript, CCT revised the manuscript. All authors approved the final manuscript. Funding This study was supported by the National Natural Science Foundation of China (NO.82170433). Availability of data and materials The data of this study can be downloaded from the Dryad public database (https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.ft8750v). Ethics approval and consent to participate The original research was authorized by the Rich Healthcare Group Review Board and the informed consent was provided. Therefore, no further research ethics and informed consent were needed in the current secondary-analysis research. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interest in this manuscript. References Squires E, Duber H, Campbell M, Cao J, Chapin A, Horst C, Li Z, Matyasz T, Reynolds A, Hirsch IB et al : Health Care Spending on Diabetes in the U.S., 1996-2013. Diabetes Care 2018, 41(7):1423-1431. Ali MK, Pearson-Stuttard J, Selvin E, Gregg EW: Interpreting global trends in type 2 diabetes complications and mortality. Diabetologia 2022, 65(1):3-13. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2023, 402(10397):203-234. Wang L, Peng W, Zhao Z, Zhang M, Shi Z, Song Z, Zhang X, Li C, Huang Z, Sun X et al : Prevalence and Treatment of Diabetes in China, 2013-2018. JAMA 2021, 326(24):2498-2506. Yang Q, Vijayakumar A, Kahn BB: Metabolites as regulators of insulin sensitivity and metabolism. Nat Rev Mol Cell Biol 2018, 19(10):654-672. DeFronzo RA, Tobin JD, Andres R: Glucose clamp technique: a method for quantifying insulin secretion and resistance. The American journal of physiology 1979, 237(3):E214-223. Pantoja-Torres B, Toro-Huamanchumo CJ, Urrunaga-Pastor D, Guarnizo-Poma M, Lazaro-Alcantara H, Paico-Palacios S, Del Carmen Ranilla-Seguin V, Benites-Zapata VA: High triglycerides to HDL-cholesterol ratio is associated with insulin resistance in normal-weight healthy adults. Diabetes Metab Syndr 2019, 13(1):382-388. Oliveri A, Rebernick RJ, Kuppa A, Pant A, Chen Y, Du X, Cushing KC, Bell HN, Raut C, Prabhu P et al : Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank. Nat Genet 2024. Tahapary DL, Pratisthita LB, Fitri NA, Marcella C, Wafa S, Kurniawan F, Rizka A, Tarigan TJE, Harbuwono DS, Purnamasari D et al : Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metab Syndr 2022, 16(8):102581. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MG, Hernández-González SO, Jacques-Camarena O, Rodríguez-Morán M: The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. The Journal of clinical endocrinology and metabolism 2010, 95(7):3347-3351. Er LK, Wu S, Chou HH, Hsu LA, Teng MS, Sun YC, Ko YL: Triglyceride Glucose-Body Mass Index Is a Simple and Clinically Useful Surrogate Marker for Insulin Resistance in Nondiabetic Individuals. PLoS One 2016, 11(3):e0149731. Yuge H, Okada H, Hamaguchi M, Kurogi K, Murata H, Ito M, Fukui M: Triglycerides/HDL cholesterol ratio and type 2 diabetes incidence: Panasonic Cohort Study 10. Cardiovascular diabetology 2023, 22(1):308. Kim J, Shin SJ, Kim YS, Kang HT: Positive association between the ratio of triglycerides to high-density lipoprotein cholesterol and diabetes incidence in Korean adults. Cardiovascular diabetology 2021, 20(1):183. Kuang M, Yang R, Huang X, Wang C, Sheng G, Xie G, Zou Y: Assessing temporal differences in the predictive power of baseline TyG-related parameters for future diabetes: an analysis using time-dependent receiver operating characteristics. Journal of translational medicine 2023, 21(1):299. Park B, Lee HS, Lee YJ: Triglyceride glucose (TyG) index as a predictor of incident type 2 diabetes among nonobese adults: a 12-year longitudinal study of the Korean Genome and Epidemiology Study cohort. Transl Res 2021, 228:42-51. Chen Y, Zhang XP, Yuan J, Cai B, Wang XL, Wu XL, Zhang YH, Zhang XY, Yin T, Zhu XH et al : Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study. BMJ Open 2018, 8(9):e021768. Khan SH, Sobia F, Niazi NK, Manzoor SM, Fazal N, Ahmad F: Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetol Metab Syndr 2018, 10:74. Cosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, Federici M, Filippatos G, Grobbee DE, Hansen TB et al : 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J 2020, 41(2):255-323. ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, Collins BS, Hilliard ME, Isaacs D, Johnson EL et al : 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023. Diabetes Care 2023, 46(Suppl 1):S19-s40. Kahn SE, Cooper ME, Del Prato S: Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet 2014, 383(9922):1068-1083. Park SE, Park CY, Sweeney G: Biomarkers of insulin sensitivity and insulin resistance: Past, present and future. Crit Rev Clin Lab Sci 2015, 52(4):180-190. Yang T, Liu Y, Li L, Zheng Y, Wang Y, Su J, Yang R, Luo M, Yu C: Correlation between the triglyceride-to-high-density lipoprotein cholesterol ratio and other unconventional lipid parameters with the risk of prediabetes and Type 2 diabetes in patients with coronary heart disease: a RCSCD-TCM study in China. Cardiovascular diabetology 2022, 21(1):93. Brahimaj A, Rivadeneira F, Muka T, Sijbrands EJG, Franco OH, Dehghan A, Kavousi M: Novel metabolic indices and incident type 2 diabetes among women and men: the Rotterdam Study. Diabetologia 2019, 62(9):1581-1590. da Silva A, Caldas APS, Rocha D, Bressan J: Triglyceride-glucose index predicts independently type 2 diabetes mellitus risk: A systematic review and meta-analysis of cohort studies. Prim Care Diabetes 2020, 14(6):584-593. Li X, Sun M, Yang Y, Yao N, Yan S, Wang L, Hu W, Guo R, Wang Y, Li B: Predictive Effect of Triglyceride Glucose-Related Parameters, Obesity Indices, and Lipid Ratios for Diabetes in a Chinese Population: A Prospective Cohort Study. Front Endocrinol (Lausanne) 2022, 13:862919. Xing Y, Liu J, Gao Y, Zhu Y, Zhang Y, Ma H: Stronger Associations of TyG Index with Diabetes Than TyG-Obesity-Related Parameters: More Pronounced in Young, Middle-Aged, and Women. Diabetes Metab Syndr Obes 2023, 16:3795-3805. Lu YW, Chang CC, Chou RH, Tsai YL, Liu LK, Chen LK, Huang PH, Lin SJ: Gender difference in the association between TyG index and subclinical atherosclerosis: results from the I-Lan Longitudinal Aging Study. Cardiovascular diabetology 2021, 20(1):206. Tramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, Gourdy P: Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia 2020, 63(3):453-461. Sivitz WI: Lipotoxicity and glucotoxicity in type 2 diabetes. Effects on development and progression. Postgrad Med 2001, 109(4):55-59, 63-54. Robertson RP, Harmon J, Tran PO, Poitout V: Beta-cell glucose toxicity, lipotoxicity, and chronic oxidative stress in type 2 diabetes. Diabetes 2004, 53 Suppl 1:S119-124. Kelley DE, Goodpaster BH: Skeletal muscle triglyceride. An aspect of regional adiposity and insulin resistance. Diabetes Care 2001, 24(5):933-941. Manell H, Kristinsson H, Kullberg J, Ubhayasekera SJK, Mörwald K, Staaf J, Cadamuro J, Zsoldos F, Göpel S, Sargsyan E et al : Hyperglucagonemia in youth is associated with high plasma free fatty acids, visceral adiposity, and impaired glucose tolerance. Pediatr Diabetes 2019, 20(7):880-891. Lai M, Fang F, Ma Y, Yang J, Huang J, Li N, Kang M, Xu X, Zhang J, Wang Y et al : Elevated Midtrimester Triglycerides as a Biomarker for Postpartum Hyperglycemia in Gestational Diabetes. J Diabetes Res 2020, 2020:3950652. Waldman B, Jenkins AJ, Davis TM, Taskinen MR, Scott R, O'Connell RL, Gebski VJ, Ng MK, Keech AC: HDL-C and HDL-C/ApoA-I predict long-term progression of glycemia in established type 2 diabetes. Diabetes Care 2014, 37(8):2351-2358. Han R, Lai R, Ding Q, Wang Z, Luo X, Zhang Y, Cui G, He J, Liu W, Chen Y: Apolipoprotein A-I stimulates AMP-activated protein kinase and improves glucose metabolism. Diabetologia 2007, 50(9):1960-1968. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2024 Read the published version in Endocrine → Version 1 posted Editorial decision: Revision requested 07 Apr, 2024 Reviews received at journal 08 Mar, 2024 Reviewers agreed at journal 08 Mar, 2024 Reviewers agreed at journal 08 Mar, 2024 Reviewers invited by journal 17 Feb, 2024 Submission checks completed at journal 14 Feb, 2024 Editor assigned by journal 14 Feb, 2024 First submitted to journal 13 Feb, 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. We do this by developing innovative software and high quality services for the global research community. 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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-3952991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273743059,"identity":"25e1f337-e31a-4578-a1f8-bc03af8881d0","order_by":0,"name":"Yuhan Qin","email":"","orcid":"","institution":"Zhongda Hospital Affiliated to Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Qin","suffix":""},{"id":273743060,"identity":"f2d75a9e-d62f-45f8-8571-54144732806b","order_by":1,"name":"Yong Qiao","email":"","orcid":"","institution":"Zhongda Hospital Affiliated to Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Qiao","suffix":""},{"id":273743061,"identity":"4f60a136-a867-4b01-b360-30fddd1a4e1d","order_by":2,"name":"Gaoliang Yan","email":"","orcid":"","institution":"Zhongda Hospital Affiliated to Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Gaoliang","middleName":"","lastName":"Yan","suffix":""},{"id":273743062,"identity":"4cee1120-0314-4c46-919a-4e39cb13aefe","order_by":3,"name":"Dong Wang","email":"","orcid":"","institution":"Zhongda Hospital Affiliated to Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Wang","suffix":""},{"id":273743063,"identity":"45ff4346-3539-4998-8f6c-388e81c04117","order_by":4,"name":"Chengchun Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYLACxgYQyQPEFQfAAgce4FHNg6rlzAEwdSCBaC2MbRAtDPi02LOfPfzi5w6bPHn3s8ekC+fdkbMXO/wQaIudnG4DDlt48tIse8+kFRueyUuTnrntmTGPdJoBUEuysdkBXA7LMTNmbDucuHEGj5k077bDiT3SCSAtBxK34dLC/wak5T9UyxyQlvQP+LVI5Bg/Bvo6cb4ESEsDSEsOAVtuvDFj7G1LTtzAk2NszXPssDHP7ZyCAwkGuP3C3p9j/OFnm13i/PYzhrd5ag7Lsc9O3/zhQ4WdHC4tQMAmASINUBUY4FQOAswfQKR8A15Fo2AUjIJRMJIBAD7bYL3GlIuiAAAAAElFTkSuQmCC","orcid":"","institution":"Zhongda Hospital Affiliated to Southeast University","correspondingAuthor":true,"prefix":"","firstName":"Chengchun","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-02-13 07:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3952991/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3952991/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12020-024-03830-3","type":"published","date":"2024-04-20T22:39:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51381769,"identity":"e64d4f0a-f354-4019-a0b8-78617a5a70a3","added_by":"auto","created_at":"2024-02-20 16:09:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110669,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of participants selection.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3952991/v1/88bf7bcfa8605deb7c938664.png"},{"id":51381770,"identity":"dbdf42fd-6569-47ff-a27b-8a0e75b3d4de","added_by":"auto","created_at":"2024-02-20 16:09:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95124,"visible":true,"origin":"","legend":"\u003cp\u003eThe incidence rate for T2DM across four quartiles of TG/HDL-c, TyG, and TyG-BMI.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3952991/v1/8c03cf64c78233510ac7c422.png"},{"id":51381771,"identity":"1dc359f3-a666-4f10-bb90-e450fd5fbfd8","added_by":"auto","created_at":"2024-02-20 16:09:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89312,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate-adjusted HRs of TG/HDL-c, TyG, and TyG-BMI for incident T2DM.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3952991/v1/cd4474c5f694c00b12e0bd66.png"},{"id":51382255,"identity":"f54dda30-2082-4fef-86b4-487f218bd288","added_by":"auto","created_at":"2024-02-20 16:17:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":323458,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier analysis of T2DM risk across quartiles of TG/HDL-c, TyG, and TyG-BMI.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3952991/v1/3105d5ae422a6bf851f0ef30.png"},{"id":51381774,"identity":"30e3452c-80fe-4ef3-a9df-350a10be604d","added_by":"auto","created_at":"2024-02-20 16:09:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":152863,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spine showing the nonlinear relationship between IR parameters and T2DM.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3952991/v1/62df1da5e0312120643ff379.png"},{"id":51381773,"identity":"5234427a-681a-4956-8774-55c6950dd429","added_by":"auto","created_at":"2024-02-20 16:09:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":275085,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between IR indicators and T2DM risk, stratified by sex and age.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3952991/v1/501f825849302621643f9f3e.png"},{"id":51381772,"identity":"42ee03a7-396e-4fd0-8ec6-a4823bf38751","added_by":"auto","created_at":"2024-02-20 16:09:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":50568,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of TG/HDL-c, TG, and TG-BMI in predicting T2DM.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3952991/v1/6dfbdd06ec5cf73f06824faa.png"},{"id":55690365,"identity":"4664576e-5155-4d7d-b251-2b36362728c1","added_by":"auto","created_at":"2024-05-01 22:39:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1620819,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3952991/v1/639b269e-c51b-4545-b589-912c98d383fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship Between Indices of Insulin Resistance and incident Type 2 Diabetes Mellitus in Chinese Adults","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) remains a serious public health issue with a huge disease burden worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence of T2DM has still increasing during the last decade, with increased T2DM-associated complications and mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The Global Burden of Disease Study 2021 reported 529\u0026nbsp;million diabetes globally and it was estimated there will be 1.31\u0026nbsp;billion diabetic people in 2050 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The estimated overall standardized prevalence of diabetes in China reached 12.4% in 2018. Additionally, the arithmetic on awareness, treatment, and control rates was discouraging [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInsulin resistance (IR), defined as impaired insulin sensitivity and reactivity of insulin target organs or tissues, has been recognized as a typical characteristic of T2DM and the vital mechanism involved in the development of T2DM [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Hyper-insulinemic euglycemic clamp (HEC) technique is the gold standard method for IR evaluation. However, the clinical application is impractical due to the invasiveness, complexity, and cost of the procedure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Other simple approaches without measurement of insulin were established to assess IR in recent years. For instance, triglycerides to high-density lipoprotein cholesterol ratio (TG/HDL-C) was recognized as an IR surrogate marker [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. TyG index and TyG-related indices are well-acknowledged simple IR indicators and are widely used in clinical studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. TyG could sensitively and specifically reflect insulin sensitivity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In a prospective study enrolling 511 Chinese participants without diabetes, TyG-body mass index (TyG-BMI) had the highest predictive potential for IR among several simple and useful IR surrogate makers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous reports have found positive associations between TG/HDL-c and T2DM risk and TG/HDL-c was identified as a strong predictor of T2DM [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. TyG-related parameters including TyG, TyG-BMI, etc. also showed the good predictive potential of future T2DM among the population from different regions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, evidence from the large-sample population on the associations between these three IR indicators and incident T2DM risk in participants free of T2DM at baseline level is rare. Additionally, a comparison of the 3 IR parameters on the predictive value of incident T2DM is limited. Therefore, the present study aims to investigate the relationship between the three IR indicators and the risk of incident T2DM in a large retrospective cohort study and to compare the predictive performance of each IR surrogate index.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eData used in the study were downloaded from a publicly available database (https://datadryad.org/stash/dataset/doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5061/dryad.ft8750v\u003c/span\u003e\u003cspan address=\"10.5061/dryad.ft8750v\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The dataset could be used for secondary analyses according to the instructions of the Dryad database. The original research was authorized by the Rich Healthcare Group Review Board. Therefore, no further research ethics and informed consent were needed in the current research due to the public database property. Briefly, 211833 participants aged over 20 who underwent health check at Rich Healthcare in China were recruited in the original study from 2010\u0026ndash;2016, the subjects were without diabetes at baseline and were followed up with a median of 2.98 years. As shown in Fig.\u0026nbsp;1, 116855 individuals were finally included to explore the association between 3 IR indicators and T2DM after excluding missing data for TG and HDL-c.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection and definition\u003c/h2\u003e \u003cp\u003eDemographic data and family history of included individuals comprising age, sex, smoking history, drinking history, and family history of diabetes were obtained through questionnaires. Anthropometric data were measured by trained staff. BMI was calculated by weight (kg)/height(m)\u003csup\u003e2\u003c/sup\u003e. Systolic and diastolic blood pressure at rest were checked using a mercury sphygmomanometer. Fasting venous blood samples were collected from participants to detect the levels of fasting plasma glucose (FPG), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and serum creatinine (Scr) through an autoanalyzer (Beckman AU5800, USA).\u003c/p\u003e \u003cp\u003eThe formula for calculating the TyG index was as follows: TyG\u0026thinsp;=\u0026thinsp;Ln[FPG (mg/dL)\u0026times;TG (mg/dL)/2][\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. TyG-BMI was calculated using the following formula: TyG-BMI\u0026thinsp;=\u0026thinsp;Ln[FPG (mg/dL)\u0026times;TG (mg/dL)/2]\u0026times;BMI[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiabetes was defined as FPG\u0026thinsp;\u0026ge;\u0026thinsp;7mmol/L or self-reported diabetes during follow-up. according to the 2023 American Diabetes Association recommendation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eNormally distributed continuous data were presented as means with standard deviations, skewed distributed data were shown as medians with interquartile range. Categorical variables were presented using frequencies with percentages. Student\u0026rsquo;s t-test and Mann\u0026ndash;Whitney U test was used for data with normal and highly skewed distribution. The categorical variables were compared using the Chi-square test. Restricted cubic splines (RCS) were employed to reflect the nonlinear association between IR indexes and the probability of incident T2DM. Kaplan-Meier curves were drawn to compare the accumulative T2DM risk across different quartiles of IR parameters. The relationship between these IR indicators and incident T2DM was ascertained using Cox proportional hazard regression analysis, then the hazard ratios (HR) and 95% CI of one-unit increase and higher quartiles over the lowest quartiles in these IR parameters for T2DM were calculated. Model 1 was a crude model; Age, sex, smoking history, drinking history, and family history of diabetes were adjusted in model 2; BMI, SBP, DBP, BUN, Scr, ALT, and AST were further adjusted in model 3. Age and male were the ranked 2 risk factors for T2DM [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consequently, subgroup analysis was carried out to assess whether age and sex affected the association between IR and T2DM. In detail, participants were divided according to age (\u0026lt;\u0026thinsp;40 years and \u0026ge;\u0026thinsp;40 years) and gender (males and females). We assessed these IR parameters' predicting value for incident T2DM using receiver operating characteristic (ROC) curves. Calculations were made for sensitivity, specificity, and area under the curve (AUC). All statistical analyses were performed using SPSS 25.0 and R Software 4.2.2. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of the study population\u003c/h2\u003e \u003cp\u003e116855 participants without diabetes at baseline were included, with an average age of 44.08\u0026thinsp;\u0026plusmn;\u0026thinsp;12.93. Among these subjects, 62868 (53.8%) were males, during a median follow-up year of 2.98, 2685 (2.30%) developed incident diabetes. The baseline characteristics of the population were compared and presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In comparison with nondiabetic subjects, diabetic subjects tended to be older, had higher levels of SBP, DBP, FPG, TG, TC, LDL-c, ALT, AST, BUN, Sc, and lower HDL-c levels at baseline levels. Furthermore, participants with diabetes were more likely to have smoking and drinking behavior, and a diabetes family history (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, TG/HDL-c, TyG, and TyG-BMI were all higher in the diabetes group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline and laboratory characteristics of participants with and without diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;116855)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiabetes (n\u0026thinsp;=\u0026thinsp;2685)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-diabetes (n\u0026thinsp;=\u0026thinsp;114170)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.08\u0026thinsp;\u0026plusmn;\u0026thinsp;12.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.63\u0026thinsp;\u0026plusmn;\u0026thinsp;16.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.78\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62868 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1890 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60978 (53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.03\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e547 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e525 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e878 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e847 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of diabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2640 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2541 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.43\u0026thinsp;\u0026plusmn;\u0026thinsp;16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.95\u0026thinsp;\u0026plusmn;\u0026thinsp;18.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.13\u0026thinsp;\u0026plusmn;\u0026thinsp;16.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.45\u0026thinsp;\u0026plusmn;\u0026thinsp;10.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.57\u0026thinsp;\u0026plusmn;\u0026thinsp;11.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.29\u0026thinsp;\u0026plusmn;\u0026thinsp;10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.76\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70 (1.17\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.76\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.10 (13.00-27.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.00 (17.00\u0026ndash;38.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.00 (13.00-27.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.00 (18.60\u0026ndash;26.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.00 (21.00-31.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.00 (18.60\u0026ndash;26.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.34\u0026thinsp;\u0026plusmn;\u0026thinsp;15.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.08\u0026thinsp;\u0026plusmn;\u0026thinsp;16.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.27\u0026thinsp;\u0026plusmn;\u0026thinsp;15.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE or median (IQR) for continuous variables or numbers (percentages) for categorical variables.\u003c/p\u003e \u003cp\u003eBMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TG, triglyceride; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; Scr, serum creatinine.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \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\u003eInsulin resistance indices of participants with and without diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;116855)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiabetes (n\u0026thinsp;=\u0026thinsp;2685)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-diabetes(n\u0026thinsp;=\u0026thinsp;114170)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.52\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36 (0.87\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81 (0.52\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197.29\u0026thinsp;\u0026plusmn;\u0026thinsp;36.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234.98\u0026thinsp;\u0026plusmn;\u0026thinsp;37.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196.41\u0026thinsp;\u0026plusmn;\u0026thinsp;36.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTG/HDL: triglyceride to high-density lipoprotein ratio, TyG: triglyceride-glucose, TyG-BMI: triglyceride glucose-body mass index.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The T2DM incidence rate in subjects with different levels of IR parameters\u003c/h2\u003e \u003cp\u003e2685 of 116855 subjects developed T2DM during the follow-up of 362268.4 person-years, the overall incidence rate of T2DM was 7.41 cases/1000 person-years. Participants were then divided into four quartiles based on the baseline value of TG/HDL-c, TyG, and TyG-BMI. Notably, T2DM incidence rate dramatically increased with the quartiles of these IR indicators increasing. The T2DM incidence rate had reached 14.85, 18.79, and 19.15 per 1000 person-years in the 4th quartile (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence rate for the development of T2DM by TG/HDL-c, TyG and TyG-BMI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of person-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90472.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89761.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90173.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91860.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence rate\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.85\u003c/p\u003e \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\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of person-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93157.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90491.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89339.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89279.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence rate\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.79\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\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of person-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91430.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90761.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90300.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90008.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence rate\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e per 1000 person-years.\u003c/p\u003e \u003cp\u003eTG/HDL: triglyceride to high-density lipoprotein ratio, TyG: triglyceride-glucose, TyG-BMI: triglyceride glucose-body mass index.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Univariate analysis of incident T2DM\u003c/h2\u003e \u003cp\u003eUnivariate analysis of risk factors for T2DM are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Positive associations were found between age, male, BMI, smoking, drinking, diabetes family history, SBP, DBP, FPG, TG, TC, LDL-c, ALT, AST, BUN, Scr, and T2DM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while negative correlation was found between HDL-c and T2DM risk (HR\u0026thinsp;=\u0026thinsp;0.575, 95% CI: 0.506\u0026ndash;0.654, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Of note, these three IR parameters were all positively correlated with T2DM.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of incident T2DM\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 \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.062\u0026ndash;1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.856\u0026ndash;2.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.212\u0026ndash;2.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.090\u0026ndash;2.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.220\u0026ndash;2.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.148\u0026ndash;1.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.035\u0026ndash;1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.039\u0026ndash;1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.452\u0026ndash;10.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.245\u0026ndash;1.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.239\u0026ndash;1.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.506\u0026ndash;0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.281\u0026ndash;1.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.004\u0026ndash;1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.005\u0026ndash;1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.182\u0026ndash;1.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.006\u0026ndash;1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.229\u0026ndash;1.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.662\u0026ndash;4.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.022\u0026ndash;1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHR: hazard ratio; CI: confidence interval; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TG, triglyceride; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; Scr, serum creatinine; TG/HDL: Triglyceride to high-density lipoprotein ratio, TyG: triglyceride-glucose, TyG-BMI: triglyceride glucose-body mass index.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The association between IR indicators and T2DM\u003c/h2\u003e \u003cp\u003eCox regression analyses were conducted to assess the impact of three IR parameters on the incidence of T2DM (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Three models (crude model, partially and fully-adjusted model) were constructed to evaluate the effect of IR indicators on T2DM. The covariates in fully-adjusted model 3 were selected from the results of univariate analysis, which included age, sex, BMI, smoking and drinking history, diabetes family history, SBP, DBP, ALT, AST, BUN, and Scr. 1.177 (95% CI: 1.140\u0026ndash;1.216, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 2.776 (95% CI: 2.551\u0026ndash;3.020, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 1.108 (95% CI: 1.016\u0026ndash;1.019, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) fold risk for developing T2DM were found in subjects with one-unit increase of TG/HDL-c, TyG, and TyG-BMI in model 3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate-adjusted hazard ratios of TG/HDL-c, TyG, and TyG-BMI for incident T2DM during follow-up.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.245(1.229\u0026ndash;1.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.218(1.197\u0026ndash;1.238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.177 (1.140\u0026ndash;1.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.851(3.662\u0026ndash;4.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.213 (3.038\u0026ndash;3.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.776 (2.551\u0026ndash;3.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.022(1.022\u0026ndash;1.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.020 (1.020\u0026ndash;1.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.018 (1.016\u0026ndash;1.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eHR: hazard ratio; CI: confidence interval.\u003c/p\u003e \u003cp\u003eModel 1: unadjusted.\u003c/p\u003e \u003cp\u003eModel 2: adjusted for age, sex, smoking history, drinking history, and family history of diabetes.\u003c/p\u003e \u003cp\u003eModel 3: adjusted for age, sex, BMI, SBP, DBP, smoking history, drinking history, family history of diabetes, ALT, AST, BUN, and Scr.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTG/HDL-c, TyG, and TyG-BMI values were categorized into 4 quartiles as follows: TG/HDL-c: Q1 (\u0026le;\u0026thinsp;0.52), Q2 (0.52\u0026ndash;0.82), Q3 (0.82\u0026ndash;1.34), and Q4 (\u0026gt;\u0026thinsp;1.34); TyG: Q1 (\u0026le;\u0026thinsp;7.97), Q2 (7.97\u0026ndash;8.37), Q3 (8.37\u0026ndash;8.81), and Q4 (\u0026gt;\u0026thinsp;8.81); TyG-BMI: Q1 (\u0026le;\u0026thinsp;169.19), Q2 (169.19-193.88), Q3 (193.88-221.19), and Q4 (\u0026gt;\u0026thinsp;221.19). The risk for T2DM increased dramatically with the increasing quartiles of these IR parameters (Fig.\u0026nbsp;3). Compared with the 1st quartile, participants in the 4th quartile of TG/HDL-c, TyG, and TyG-BMI had 3.298 (95% CI: 2.615\u0026ndash;4.610), 8.402 (95% CI: 6.176\u0026ndash;11.429), and 8.468 (95% CI: 6.157\u0026ndash;11.646) fold risk for incident T2DM, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The cumulative T2DM incidence in participants with different levels of IR was compared using Kaplan-Meier curves. As shown in Fig.\u0026nbsp;4, the cumulative incidences of T2DM were remarkably different among these quartiles (Log-rank test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The nonlinear association between these three IR indicators and T2DM was evaluated by RCS and presented in Fig.\u0026nbsp;5 (p for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Stratification analysis\u003c/h2\u003e \u003cp\u003eSubgroup stratification analyses by age and gender was then performed to assess the effect of age and sex on the relationship between IR and diabetes. As displayed in Fig.\u0026nbsp;6, significant interactions between TyG and T2DM risk were observed between sex subgroups (p for interaction\u0026thinsp;=\u0026thinsp;0.043). In addition, The association between IR and T2DM differs between age\u0026thinsp;\u0026ge;\u0026thinsp;40 and age\u0026thinsp;\u0026lt;\u0026thinsp;40 group. The correlations between these three IR parameters and T2DM were more pronounced in subjects aged less than 40. One-unit increase of TG/HDL-c, TyG, and TyG-BMI were associated with 1.165, 4.207, and 1.025 fold risk for T2DM, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6 ROC\u003c/h2\u003e \u003cp\u003ePredictive value of IR indicators in the identification of T2DM\u003c/p\u003e \u003cp\u003eROC curves were constructed to assess the predictive value of these three IR-associated parameters for T2DM, respectively. The area of the curve (AUC) and 95% confidence interval, cut-off value, corresponding sensitivity, and specificity are listed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;7. Among these three IR parameters, TyG-BMI had the highest AUC for predicting T2DM, with a cut-off value of 213.289.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC curve analysis of the insulin resistance parameters in predicting T2DM\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\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.696\u0026ndash;0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.624\u003c/p\u003e \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\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.763\u0026ndash;0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.631\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\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.772\u0026ndash;0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTG/HDL: triglyceride to high-density lipoprotein ratio, TyG: triglyceride-glucose, TyG-BMI: triglyceride glucose-body mass index.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study explored the associations between three IR surrogates and T2DM risk and comprehensively compared their predictive value of T2DM in 116855 Chinese physical examination population. The results showed T2DM risk significantly increased with the increasing levels of these IR indicators. These three IR parameters well all independent predictors of future T2DM. Among the three IR parameters, TyG-BMI had the highest predictive ability of incident T2DM risk.\u003c/p\u003e \u003cp\u003eImpaired insulin production and IR in the insulin target tissues are key cellular defects in the development of T2DM [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. IR is initially manifested as hyperinsulinemia, followed by reduced insulin secretion and hyperglycemia, leading to the progression of T2DM and associated complications [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, early accurate identification of IR before the diagnosis of T2DM are of great clinical significance. Although HEC is the \u0026ldquo;gold standard\u0026rdquo; method for IR measurement, the cost and complexity hinder its application. Therefore, numerous studies have been carried out to find simple IR surrogate markers with high diagnostic values on diabetes in the past decade.\u003c/p\u003e \u003cp\u003eGlucose and lipid metabolism is closely related to each other. TG/HDL-c was regarded as the lipid parameter with the highest predictive value for T2DM. However, the follow-up period was not taken into account [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Another study revealed TG/HDL-c was a strong predictor for new-onset T2DM in a Japanese cohort [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In a population-based Rotterdam Study, TyG index was closely correlated with T2DM risk [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Findings from the meta-analysis also showed TyG index significantly enhanced the risk of T2DM [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Other metabolism-related parameters are also involved in the development of IR and T2DM. Therefore, increasing studies pay attention to the diagnostic value of TyG-related parameters [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, the current study demonstrated a positive association between the three IR indicators and new-onset T2DM. Furthermore, we for the first time compared the predictive ability of the three IR indices, and found TyG-BMI had the highest AUC for future T2DM, which was consistent with the findings of another Chinese prospective study [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the research findings are still controversial. A cohort study in Hebei General Hospital showed TyG had a higher predictive value than TyG-related indicators [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The differences in the included study population and follow-up period might explain the controversial research results with previous reports. Furthermore, the subgroup analysis revealed significant disparities in the relationship between these three IR parameters and T2DM risk stratified by age and sex. Specifically, the association was particularly pronounced among participants aged less than 40. The sex disparity also existed in the relationship between TyG and T2DM. Similarly, the association between TyG and atherosclerosis also differs between males and females [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous studies clarified women were more vulnerable to high TG levels, the protective impact of estrogen is attenuated under dysregulated lipid concentrations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The relationship between IR parameters and T2DM in subjects under 40 requires further investigation.\u003c/p\u003e \u003cp\u003eThe excellent performance of TyG and TyG-BMI in the prediction of T2DM might be attributed to these three IR parameters reflecting both the metabolism of glucose and lipids. Both lipotoxicity and glucotoxicity play a vital role in T2DM [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. It is well-acknowledged islet tissue is vulnerable to oxidative stress injury and toxicity caused by hyperglycemia [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Notably, the deposition of TG in islets also damages islet cells and impacts islet function. The increased TG levels in peripheral organ tissue will affect the utilization of glucose and aggravate peripheral IR [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, free fatty acid levels (FFAs), the metabolites of TG, increase glucagon secretion [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], trigger inflammation, as well as disturb insulin receptor entry by changing the fatty acid of cell membranes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. While HDL-c promotes insulin secretion through the inhibition of β-cell apoptosis and promotion of reverse cholesterol transport [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. HDL also promotes glucose uptake in skeletal muscle cells via the AMPK pathway [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study has some strengths as follows: (1) The current study included 116855 subjects aged over 20 and followed up for a median of 2.98 years. (2) This is a longitudinal cohort but not a cross-sectional study. (3) We further compared the predictive value of baseline three IR indicators for the new onset of T2DM. (4) Stratified analysis was performed to explore the effect of age and sex on the relationship between IR parameters and T2DM risk. This study also has some limitations: (1) There are numerous kinds of IR parameters, however, we only explored three indicators of IR in the research due to limited data that could be obtained from the dataset. Insulin and many other parameters were absent in the original research. (2) HbA1c level and 2h-postprandial blood glucose levels were all absent, which might contribute to underestimated T2DM incidence. (3) This is a Chinese cohort study including healthy adults, further validation is needed for the extrapolation to the general population.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIt is of great significance to explore IR indicators for identification of people at high risk of developing T2DM. The results of this study indicated TG/HDL-c, TyG, and TyG-BMI were all independently associated with increased T2DM risk. TyG-BMI had the most superior predictive ability on future T2DM. TyG-BMI might act as a simple parameter for predicting future T2DM in clinical practice.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors thank all staff of the original study conducted by Rich Healthcare group, and we appreciate DRYAD for providing a platform for sharing data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYHQ designed the study, YHQ, GLY collected the data, YHQ, YQ, DW analyzed the data, YHQ wrote the manuscript, CCT revised the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (NO.82170433).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of this study can be downloaded from the Dryad public database (https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.ft8750v).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original research was authorized by the Rich Healthcare Group Review Board and the informed consent was provided. Therefore, no further research ethics and informed consent were needed in the current secondary-analysis research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest in this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSquires E, Duber H, Campbell M, Cao J, Chapin A, Horst C, Li Z, Matyasz T, Reynolds A, Hirsch IB\u003cem\u003e et al\u003c/em\u003e: Health Care Spending on Diabetes in the U.S., 1996-2013. \u003cem\u003eDiabetes Care \u003c/em\u003e2018, 41(7):1423-1431.\u003c/li\u003e\n\u003cli\u003eAli MK, Pearson-Stuttard J, Selvin E, Gregg EW: Interpreting global trends in type 2 diabetes complications and mortality. \u003cem\u003eDiabetologia \u003c/em\u003e2022, 65(1):3-13.\u003c/li\u003e\n\u003cli\u003eGlobal, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet \u003c/em\u003e2023, 402(10397):203-234.\u003c/li\u003e\n\u003cli\u003eWang L, Peng W, Zhao Z, Zhang M, Shi Z, Song Z, Zhang X, Li C, Huang Z, Sun X\u003cem\u003e et al\u003c/em\u003e: Prevalence and Treatment of Diabetes in China, 2013-2018. \u003cem\u003eJAMA \u003c/em\u003e2021, 326(24):2498-2506.\u003c/li\u003e\n\u003cli\u003eYang Q, Vijayakumar A, Kahn BB: Metabolites as regulators of insulin sensitivity and metabolism. \u003cem\u003eNat Rev Mol Cell Biol \u003c/em\u003e2018, 19(10):654-672.\u003c/li\u003e\n\u003cli\u003eDeFronzo RA, Tobin JD, Andres R: Glucose clamp technique: a method for quantifying insulin secretion and resistance. \u003cem\u003eThe American journal of physiology \u003c/em\u003e1979, 237(3):E214-223.\u003c/li\u003e\n\u003cli\u003ePantoja-Torres B, Toro-Huamanchumo CJ, Urrunaga-Pastor D, Guarnizo-Poma M, Lazaro-Alcantara H, Paico-Palacios S, Del Carmen Ranilla-Seguin V, Benites-Zapata VA: High triglycerides to HDL-cholesterol ratio is associated with insulin resistance in normal-weight healthy adults. \u003cem\u003eDiabetes Metab Syndr \u003c/em\u003e2019, 13(1):382-388.\u003c/li\u003e\n\u003cli\u003eOliveri A, Rebernick RJ, Kuppa A, Pant A, Chen Y, Du X, Cushing KC, Bell HN, Raut C, Prabhu P\u003cem\u003e et al\u003c/em\u003e: Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank. \u003cem\u003eNat Genet \u003c/em\u003e2024.\u003c/li\u003e\n\u003cli\u003eTahapary DL, Pratisthita LB, Fitri NA, Marcella C, Wafa S, Kurniawan F, Rizka A, Tarigan TJE, Harbuwono DS, Purnamasari D\u003cem\u003e et al\u003c/em\u003e: Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index. \u003cem\u003eDiabetes Metab Syndr \u003c/em\u003e2022, 16(8):102581.\u003c/li\u003e\n\u003cli\u003eGuerrero-Romero F, Simental-Mend\u0026iacute;a LE, Gonz\u0026aacute;lez-Ortiz M, Mart\u0026iacute;nez-Abundis E, Ramos-Zavala MG, Hern\u0026aacute;ndez-Gonz\u0026aacute;lez SO, Jacques-Camarena O, Rodr\u0026iacute;guez-Mor\u0026aacute;n M: The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. \u003cem\u003eThe Journal of clinical endocrinology and metabolism \u003c/em\u003e2010, 95(7):3347-3351.\u003c/li\u003e\n\u003cli\u003eEr LK, Wu S, Chou HH, Hsu LA, Teng MS, Sun YC, Ko YL: Triglyceride Glucose-Body Mass Index Is a Simple and Clinically Useful Surrogate Marker for Insulin Resistance in Nondiabetic Individuals. \u003cem\u003ePLoS One \u003c/em\u003e2016, 11(3):e0149731.\u003c/li\u003e\n\u003cli\u003eYuge H, Okada H, Hamaguchi M, Kurogi K, Murata H, Ito M, Fukui M: Triglycerides/HDL cholesterol ratio and type 2 diabetes incidence: Panasonic Cohort Study 10. \u003cem\u003eCardiovascular diabetology \u003c/em\u003e2023, 22(1):308.\u003c/li\u003e\n\u003cli\u003eKim J, Shin SJ, Kim YS, Kang HT: Positive association between the ratio of triglycerides to high-density lipoprotein cholesterol and diabetes incidence in Korean adults. \u003cem\u003eCardiovascular diabetology \u003c/em\u003e2021, 20(1):183.\u003c/li\u003e\n\u003cli\u003eKuang M, Yang R, Huang X, Wang C, Sheng G, Xie G, Zou Y: Assessing temporal differences in the predictive power of baseline TyG-related parameters for future diabetes: an analysis using time-dependent receiver operating characteristics. \u003cem\u003eJournal of translational medicine \u003c/em\u003e2023, 21(1):299.\u003c/li\u003e\n\u003cli\u003ePark B, Lee HS, Lee YJ: Triglyceride glucose (TyG) index as a predictor of incident type 2 diabetes among nonobese adults: a 12-year longitudinal study of the Korean Genome and Epidemiology Study cohort. \u003cem\u003eTransl Res \u003c/em\u003e2021, 228:42-51.\u003c/li\u003e\n\u003cli\u003eChen Y, Zhang XP, Yuan J, Cai B, Wang XL, Wu XL, Zhang YH, Zhang XY, Yin T, Zhu XH\u003cem\u003e et al\u003c/em\u003e: Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study. \u003cem\u003eBMJ Open \u003c/em\u003e2018, 8(9):e021768.\u003c/li\u003e\n\u003cli\u003eKhan SH, Sobia F, Niazi NK, Manzoor SM, Fazal N, Ahmad F: Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. \u003cem\u003eDiabetol Metab Syndr \u003c/em\u003e2018, 10:74.\u003c/li\u003e\n\u003cli\u003eCosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, Federici M, Filippatos G, Grobbee DE, Hansen TB\u003cem\u003e et al\u003c/em\u003e: 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. \u003cem\u003eEur Heart J \u003c/em\u003e2020, 41(2):255-323.\u003c/li\u003e\n\u003cli\u003eElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, Collins BS, Hilliard ME, Isaacs D, Johnson EL\u003cem\u003e et al\u003c/em\u003e: 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023. \u003cem\u003eDiabetes Care \u003c/em\u003e2023, 46(Suppl 1):S19-s40.\u003c/li\u003e\n\u003cli\u003eKahn SE, Cooper ME, Del Prato S: Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. \u003cem\u003eLancet \u003c/em\u003e2014, 383(9922):1068-1083.\u003c/li\u003e\n\u003cli\u003ePark SE, Park CY, Sweeney G: Biomarkers of insulin sensitivity and insulin resistance: Past, present and future. \u003cem\u003eCrit Rev Clin Lab Sci \u003c/em\u003e2015, 52(4):180-190.\u003c/li\u003e\n\u003cli\u003eYang T, Liu Y, Li L, Zheng Y, Wang Y, Su J, Yang R, Luo M, Yu C: Correlation between the triglyceride-to-high-density lipoprotein cholesterol ratio and other unconventional lipid parameters with the risk of prediabetes and Type 2 diabetes in patients with coronary heart disease: a RCSCD-TCM study in China. \u003cem\u003eCardiovascular diabetology \u003c/em\u003e2022, 21(1):93.\u003c/li\u003e\n\u003cli\u003eBrahimaj A, Rivadeneira F, Muka T, Sijbrands EJG, Franco OH, Dehghan A, Kavousi M: Novel metabolic indices and incident type 2 diabetes among women and men: the Rotterdam Study. \u003cem\u003eDiabetologia \u003c/em\u003e2019, 62(9):1581-1590.\u003c/li\u003e\n\u003cli\u003eda Silva A, Caldas APS, Rocha D, Bressan J: Triglyceride-glucose index predicts independently type 2 diabetes mellitus risk: A systematic review and meta-analysis of cohort studies. \u003cem\u003ePrim Care Diabetes \u003c/em\u003e2020, 14(6):584-593.\u003c/li\u003e\n\u003cli\u003eLi X, Sun M, Yang Y, Yao N, Yan S, Wang L, Hu W, Guo R, Wang Y, Li B: Predictive Effect of Triglyceride Glucose-Related Parameters, Obesity Indices, and Lipid Ratios for Diabetes in a Chinese Population: A Prospective Cohort Study. \u003cem\u003eFront Endocrinol (Lausanne) \u003c/em\u003e2022, 13:862919.\u003c/li\u003e\n\u003cli\u003eXing Y, Liu J, Gao Y, Zhu Y, Zhang Y, Ma H: Stronger Associations of TyG Index with Diabetes Than TyG-Obesity-Related Parameters: More Pronounced in Young, Middle-Aged, and Women. \u003cem\u003eDiabetes Metab Syndr Obes \u003c/em\u003e2023, 16:3795-3805.\u003c/li\u003e\n\u003cli\u003eLu YW, Chang CC, Chou RH, Tsai YL, Liu LK, Chen LK, Huang PH, Lin SJ: Gender difference in the association between TyG index and subclinical atherosclerosis: results from the I-Lan Longitudinal Aging Study. \u003cem\u003eCardiovascular diabetology \u003c/em\u003e2021, 20(1):206.\u003c/li\u003e\n\u003cli\u003eTramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, Gourdy P: Sex differences in metabolic regulation and diabetes susceptibility. \u003cem\u003eDiabetologia \u003c/em\u003e2020, 63(3):453-461.\u003c/li\u003e\n\u003cli\u003eSivitz WI: Lipotoxicity and glucotoxicity in type 2 diabetes. Effects on development and progression. \u003cem\u003ePostgrad Med \u003c/em\u003e2001, 109(4):55-59, 63-54.\u003c/li\u003e\n\u003cli\u003eRobertson RP, Harmon J, Tran PO, Poitout V: Beta-cell glucose toxicity, lipotoxicity, and chronic oxidative stress in type 2 diabetes. \u003cem\u003eDiabetes \u003c/em\u003e2004, 53 Suppl 1:S119-124.\u003c/li\u003e\n\u003cli\u003eKelley DE, Goodpaster BH: Skeletal muscle triglyceride. An aspect of regional adiposity and insulin resistance. \u003cem\u003eDiabetes Care \u003c/em\u003e2001, 24(5):933-941.\u003c/li\u003e\n\u003cli\u003eManell H, Kristinsson H, Kullberg J, Ubhayasekera SJK, M\u0026ouml;rwald K, Staaf J, Cadamuro J, Zsoldos F, G\u0026ouml;pel S, Sargsyan E\u003cem\u003e et al\u003c/em\u003e: Hyperglucagonemia in youth is associated with high plasma free fatty acids, visceral adiposity, and impaired glucose tolerance. \u003cem\u003ePediatr Diabetes \u003c/em\u003e2019, 20(7):880-891.\u003c/li\u003e\n\u003cli\u003eLai M, Fang F, Ma Y, Yang J, Huang J, Li N, Kang M, Xu X, Zhang J, Wang Y\u003cem\u003e et al\u003c/em\u003e: Elevated Midtrimester Triglycerides as a Biomarker for Postpartum Hyperglycemia in Gestational Diabetes. \u003cem\u003eJ Diabetes Res \u003c/em\u003e2020, 2020:3950652.\u003c/li\u003e\n\u003cli\u003eWaldman B, Jenkins AJ, Davis TM, Taskinen MR, Scott R, O\u0026apos;Connell RL, Gebski VJ, Ng MK, Keech AC: HDL-C and HDL-C/ApoA-I predict long-term progression of glycemia in established type 2 diabetes. \u003cem\u003eDiabetes Care \u003c/em\u003e2014, 37(8):2351-2358.\u003c/li\u003e\n\u003cli\u003eHan R, Lai R, Ding Q, Wang Z, Luo X, Zhang Y, Cui G, He J, Liu W, Chen Y: Apolipoprotein A-I stimulates AMP-activated protein kinase and improves glucose metabolism. \u003cem\u003eDiabetologia \u003c/em\u003e2007, 50(9):1960-1968.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"insulin resistance, TG/HDL-c, TyG, TyG-BMI, diabetes","lastPublishedDoi":"10.21203/rs.3.rs-3952991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3952991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInsulin resistance (IR) is a pivotal pathogenesis characteristic of type 2 diabetes mellitus (T2DM). The current study aimed to explore the association between three surrogate biomarkers for IR, including triglyceride/high-density lipoprotein cholesterol ratio (TG/HDL-c), triglyceride-glucose (TyG), and triglyceride glucose-body mass index (TyG-BMI), and T2DM incidence and compare the predictive value of these parameters in T2DM.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 116855 Chinese adults aged over 20 without diabetes were included in the present study. T2DM incident rates were compared among participants with different levels of TG/HDL, TyG, and TyG-BMI. Multivariate Cox regression analysis and restricted cubic spine were utilized to investigate the association between these IR indicators and T2DM. The T2DM risk across different quartiles of IR parameters during follow-up was compared using Kaplan-Meier curves. The receiver operating characteristic analysis was used to investigate the predictive potential of each IR indicator for future T2DM. Stratification analyses were performed to explore the impact of age and sex on the association between IR and T2DM risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e2685 participants developed T2DM during a median follow-up of 2.98 years. The T2DM incidence rate dramatically increased with the increasing quartiles of TG/HDL-c, TyG, and TyG-BMI. The adjusted hazard ratios (HR) of incident T2DM were 1.177, 2.766, and 1.1018, for TG/HDL-c, TyG, and TyG-BMI, respectively. There were significant increasing trends of T2DM across the quartiles of TG/HDL-c, TyG, and TyG-BMI. The HRs of new-onset T2DM in the highest quartiles versus the lowest quartile of TG/HDL-c, TyG, and TyG-BMI were 3.298 (95% CI: 2.615\u0026ndash;4.610), 8.402 (95% CI: 6.176\u0026ndash;11.429), and 8.468 (95% CI: 6.157\u0026ndash;11.646). RCS revealed the nonlinear relationship between and T2DM risk. Significant interactions between TyG and T2DM risk were observed between age groups. The correlations between IR and T2DM were more pronounced in subjects aged less than 40. TyG-BMI had the highest predictive value for incident T2DM (AUC\u0026thinsp;=\u0026thinsp;0.774), with a cut-off value of 213.289.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eTG/HDL-c, TyG, and TyG-BMI index were all significantly positively associated with higher risk for future T2DM. Baseline TyG-BMI level had high predictive value for the identification of T2DM.\u003c/p\u003e","manuscriptTitle":"Relationship Between Indices of Insulin Resistance and incident Type 2 Diabetes Mellitus in Chinese Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 16:09:22","doi":"10.21203/rs.3.rs-3952991/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-07T10:32:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-08T20:32:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"817dbbcd-3185-4e5f-9858-cf7c6628289a","date":"2024-03-08T20:27:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47d80876-a455-4af8-82ae-6b83120deb89","date":"2024-03-08T20:21:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-17T19:29:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-14T06:45:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-14T06:45:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Endocrine","date":"2024-02-13T07:41:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2fdc80f4-3d34-42df-82da-14f3b67d01f1","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-01T22:39:06+00:00","versionOfRecord":{"articleIdentity":"rs-3952991","link":"https://doi.org/10.1007/s12020-024-03830-3","journal":{"identity":"endocrine","isVorOnly":false,"title":"Endocrine"},"publishedOn":"2024-04-20 22:39:06","publishedOnDateReadable":"April 20th, 2024"},"versionCreatedAt":"2024-02-20 16:09:22","video":"","vorDoi":"10.1007/s12020-024-03830-3","vorDoiUrl":"https://doi.org/10.1007/s12020-024-03830-3","workflowStages":[]},"version":"v1","identity":"rs-3952991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3952991","identity":"rs-3952991","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}