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. 2025 Apr 13;24(1):161.
doi: 10.1186/s12933-025-02672-1.

Insulin resistance quantified by estimated glucose disposal rate predicts cardiovascular disease incidence: a nationwide prospective cohort study

Shiyi Tao  1   2 , Lintong Yu  2 , Jun Li  3 , Ji Wu  1 , Xuanchun Huang  1 , Zicong Xie  1 , Tiantian Xue  1 , Yonghao Li  1   2 , Lilan Su  1
Affiliations

Insulin resistance quantified by estimated glucose disposal rate predicts cardiovascular disease incidence: a nationwide prospective cohort study

Shiyi Tao et al. Cardiovasc Diabetol. .

Abstract

Background: Insulin resistance (IR) is an important pathologic component in the occurrence and development of cardiovascular disease (CVD). The estimated glucose disposal rate (eGDR) is a measure of glucose handling capacity, that has demonstrated utility as a reliable marker of IR. The study aimed to determine the predictive utility of IR assessed by eGDR for CVD risk.

Methods: This nationwide prospective cohort study utilized data of 6416 participants from the China Health and Retirement Longitudinal Study (CHARLS) who were free of CVD but had complete data on eGDR at baseline. The Boruta algorithm was performed for feature selection. Multivariate Cox proportional hazards regression models and restricted cubic spline (RCS) analysis were conducted to examine the associations between eGDR and CVD, and the results were expressed with hazard ratio (HR) and 95% confidence interval (CI) values. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, Hosmer-Lemeshow test, net reclassification improvement (NRI), and decision curve analysis (DCA) were employed to evaluate the clinical efficacy of eGDR in identifying CVD. Subgroup analysis was performed to explore the potential association of with CVD in different populations.

Results: During a median follow-up of 106.5 months, 1339 (20.87%) incident CVD cases, including 1025 (15.96%) heart disease and 439 (6.84%) stroke, were recorded from CHARLS. The RCS curves demonstrated a significant and linear relationship between eGDR and all endpoints (all P for nonlinear > 0.05). After multivariate adjustment, the lower eGDR levels were found to be significantly associated with a greater prevalence of CVD. Compared to the lowest quartile, the highest eGDR quartile was associated with a decreased risk of CVD (HR 0.686, 95% CI 0.545-0.862). When assessed as a continuous variable, individuals with a unit increasement in eGDR was related to a 21.2% (HR 0.788, 95% CI 0.669-0.929) lower risk of CVD, a 18.3% (HR 0.817, 95% CI 0.678-0.985) decreased risk of heart disease, and 39.5% (HR 0.705, 95% CI 0.539-0.923) lower risk of stroke. The eGDR had an excellent predictive performance according to the results of ROC (AUC = 0.712) and χ2 likelihood ratio test (χ2 = 4.876, P = 0.771). NRI and DCA analysis also suggested the improvement from eGDR to identify prevalent CVD and the favorable clinical efficacy of the multivariate model. Subgroup analysis revealed that the trend in incident CVD risk were broadly consistent with the main results across subgroups.

Conclusion: A lower level of eGDR was found to be associated with increased risk of incident CVD, suggesting that eGDR may serve as a promising and preferable predictor for CVD.

Keywords: CHARLS; Cardiovascular disease; Estimated glucose disposal rate; Insulin resistance; Risk factor.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The CHARLS project was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015 for household survey and IRB00001052-11014 for blood sample), and all participants voluntarily participated and signed an informed consent form. Consent for publication: All authors have consent for publication. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart for participants selection in CHARLS. *Incomplete information includes age, gender, BMI, diabetes history, hypertension history, WC, HbA1c, and CVD condition
Fig. 2
Fig. 2
Feature selection for CVD using the Boruta algorithm. A. The process of feature selection; B. The value evolution of Z-score in the screening process. The horizontal axis shows the name of each variable and the number of times the classifier is run in Fig. 2A and B, respectively. While the vertical axis represents the Z-value of each variable. The green boxes and lines represent confirmed variables, the yellow ones represent tentative attributes, and the red ones represent rejected variables in the model calculation. BMI Body mass index, WC Waist circumference, SBP Systolic blood pressure, DBP Diastolic blood pressure, WBC White blood cell, PLT Platelets, Hb Hemoglobin, FPG Fasting plasma glucose, BUN Blood urea nitrogen, UA Uric acid, CRP C-reactive protein, TC Total cholesterol, TG Triglyceride, LDL-C Low-density lipoprotein cholesterol, HDL-C High-density lipoprotein cholesterol, HbA1c Hemoglobin A1c, eGDR Estimated glucose disposal rate
Fig. 3
Fig. 3
Restricted cubic spline curves for CVD according to the eGDR. Hazard ratios are indicated by solid lines and 95% CIs by shaded areas. The horizontal dotted line represents the hazard ratio of 1.0. The adjusted models adjusted for factors including age, gender, BMI, WC, SBP, DBP, smoking, hypertension, diabetes, dyslipidemia, FPG, UA, TC, TG, LDL-C, HDL-C, and HbA1c screened by the Boruta algorithm and clinical experience. CVD Cardiovascular disease, eGDR Estimated glucose disposal rate
Fig. 4
Fig. 4
Predictive utility test of eGDR for CVD risk. A. The area under the receiver operating characteristic (ROC) curve (AUC); B. Calibration curve; C. Decision curve analysis (DCA); D. Scatter diagram of the net reclassification improvement (NRI). CVD Cardiovascular disease, eGDR Estimated glucose disposal rate
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