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

doi:10.21203/rs.3.rs-3952991/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-3952991/v1

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published 20 Apr, 2024

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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

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.

2.1 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.

3.1 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/m²	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.

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.

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.

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.

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No competing interests reported.

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

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Journal Publication

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Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Methods and Materials

2.1 Study population

2.2 Data collection and definition

2.3 Statistical analysis

3. Results

3.1 Baseline characteristics of the study population

3.2 The T2DM incidence rate in subjects with different levels of IR parameters

3.3 Univariate analysis of incident T2DM

3.4 The association between IR indicators and T2DM

3.5 Stratification analysis

3.6 ROC

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1