The association between estimated glucose disposal rate and self-reported diabetic retinopathy: evidence from two independent cohorts and machine learning.

Background: Insulin resistance plays a key role in the pathogenesis of diabetic retinopathy (DR). Although established insulin resistance markers have been shown to predict a variety of complications, the association between the estimated glucose disposal rate (eGDR) and prevalence of DR remains incompletely characterized. This study aims to examine the relationship between eGDR and DR prevalence. Methods: This cross-sectional study analyzed complete participant data (N = 1, 536) from the 2007-2018 National Health and Nutrition Examination Survey (NHANES) for all relevant information. The relationship between the insulin resistance index and self-reported DR prevalence was evaluated by using multivariate logistic regression and a restricted cubic spline (RCS) model. Subgroup analysis was conducted to assess heterogeneity across groups, and two sensitivity analyses were performed to assess the robustness of the results. In machine learning, the Boruta algorithm is applied for feature selection. The selected features are subsequently utilized by XGBoost and random forest models for DR prevalence estimation. Use the Shapley additive explanations (SHAP) value to explain the independent contribution of eGDR. In the clinical cohort, we recruited patients who visited the Second Affiliated Hospital of Anhui Medical University from September 1, 2025, to December 30, 2025. A total of 297 participants who met the inclusion criteria were finally enrolled. Multivariable logistic regression and RCS curves were used to validate the findings from the NHANES analysis. Results: In the fully adjusted model, eGDR and self-reported DR prevalence show a significant negative linear correlation (OR = 0.79, 95% CI: 0.67-0.93, P = 0.0049). Subgroup and sensitivity analyses confirm the stability of this negative association. The Boruta algorithm identifies eGDR as a robust and important feature. Both the XGBoost (AUC = 0.773) and random forest (AUC = 0.764) models show moderate predictive performance, and eGDR has high variable importance. SHAP analysis indicates that eGDR, together with body mass index and income poverty, is a key determinant of self-reported DR prevalence. The results of the clinical cohort are like NHANES. Conclusion: This cross-sectional study suggested that lower eGDR is associated with a higher prevalence of self-reported DR. Accordingly, eGDR may serve as a potential marker for risk stratification rather than a causal or preventive factor. Prospective longitudinal research is necessary to confirm these findings and to explore whether a causal relationship exists.