Association of estimated glucose disposal rate and body mass index with stroke risk in middle-aged and elderly populations: evidence from two cohort studies

BACKGROUND: Estimated Glucose Disposal Rate (eGDR) is one of the markers of insulin resistance (IR). However, current literature lacks robust evidence to clarify the correlation between eGDR-Body Mass Index (eGDR-BMI) and stroke incidence. Therefore, this study aims to explore the potential relationship between eGDR-BMI and stroke risk. METHODS: This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS). A multivariable logistic regression model was applied to examine the association between eGDR-BMI and stroke risk. Restricted cubic splines (RCS) were employed to explore the dose-response relationship and non-linear association between eGDR-BMI and stroke risk. The predictive performance of the models was evaluated using Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA). RESULTS: A total of 18,837 participants were included (CHARLS: 9,202; NHANES: 9,635). Multivariable-adjusted analyses demonstrated that eGDR-BMI was independently associated with reduced stroke risk. Each 1-SD increase in eGDR-BMI corresponded to an 8.7% risk reduction (HR = 0.913; 95% CI: 0.786–0.990; p < 0.001) in the CHARLS cohort, with consistent results in NHANES. RCS analysis also indicated a significant linear relationship between eGDR-BMI and stroke. Subgroup analysis revealed that eGDR-BMI was significantly predictive of stroke across different age groups and genders. Finally, both the ROC curve and DCA results demonstrated that eGDR-BMI has substantial predictive potential for stroke (AUC = 0.699). CONCLUSION: eGDR-BMI is significantly associated with a reduced risk of stroke, and there exists a specific non-linear relationship between eGDR-BMI and stroke. Moreover, eGDR-BMI demonstrates substantial predictive potential for stroke.