Assessment of six insulin resistance surrogate indexes for predicting stroke incidence in Chinese middle-aged and elderly populations with abnormal glucose metabolism: a nationwide prospective cohort study.

Abstract

Background: Estimate glucose disposal rate (eGDR), Chinese visceral adiposity index (CVAI), triglyceride-glucose (TyG), TyG-body mass index (TyG-BMI), metabolic score for insulin resistance (METS-IR), and atherogenic index of plasma (AIP) are considered surrogate indexes of insulin resistance (IR). There is a lack of studies comparing the predictive values of different IR surrogate indexes for stroke risk among individuals with abnormal glucose metabolism. This study aimed to investigate the relationships between six IR surrogate indexes and stroke risk in individuals with abnormal glucose metabolism, evaluate their predictive abilities for stroke risk.

Methods: Data from the China Health and Retirement Longitudinal Study (CHARLS) were analysed in this study. Multivariate logistic regression models were applied to analyse the relationships of IR surrogate indexes with stroke risk. The dose-response relationships between IR surrogate indexes and stroke risk were explored using restricted cubic splines. The areas under the curve (AUCs) of IR surrogate indexes were calculated by receiver operating characteristic (ROC) analysis.

Results: After adjusting for potential confounders, we observed that each standard deviation (SD) increase in eGDR was associated with a reduced risk of stroke, with an adjusted odds ratio (OR) of 0.746 [95% confidence interval (CI): 0.661-0.842]. In contrast, each SD increase in CVAI, TyG, TyG-BMI, METS-IR, and AIP were associated with an increased risk of stroke, with adjusted ORs (95% CIs) of 1.232 (1.106-1.373), 1.246 (1.050-1.479), 1.186 (1.022-1.376), 1.222 (1.069-1.396), and 1.193 (1.050-1.355), respectively. Dose-response analyses showed that eGDR, CVAI, TyG-BMI and METS-IR were linearly associated with stroke risk (P_nonlinear ≥ 0.05), whereas TyG and AIP were nonlinearly associated with stroke risk (P_nonlinear < 0.05). According to ROC analysis, The AUC of eGDR for predicting stroke risk in the overall population with abnormal glucose metabolism (AUC: 0.612, 95% CI: 0.584-0.640) was significantly higher than that of other indexes.

Conclusion: The six IR surrogate indexes were closely associated with high risk of stroke in individuals with abnormal glucose metabolism. The eGDR showed promising potential in predicting stroke risk in Chinese middle-aged and elderly populations with abnormal glucose metabolism.