Prediction of diabetic retinopathy using machine learning and its association with dementia risk in older adults with type 2 diabetes mellitus

Abstract

Aims: Diabetic Retinopathy (DR), a common microvascular complication of diabetes, has been associated with an increased risk of dementia. This study aimed to develop Machine Learning (ML) models to predict DR occurrence and evaluate its potential as a prognostic biomarker for dementia. Methods: We included 27,929 patients aged ≥ 50 years newly diagnosed with type 2 diabetes mellitus without prior dementia or eye disease. Prediction models for DR within one year were developed using three ML algorithms: extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and selection operator. The best-performing model was externally validated across eight institutions. Patients were followed for three years to assess dementia incidence. Dementia risk between ML-predicted DR and non-DR groups was compared using Kaplan-Meier and Cox regression, with results pooled via meta-analysis. Results: XGBoost demonstrated the best performance (AUROC: 0.746), with external validation AUROCs ranging from 0.555 to 0.620. Predicted DR was significantly associated with increased all-cause dementia risk (HR: 1.32, 95% confidence interval [CI] 1.12–1.56), Alzheimer's disease (HR: 1.30, 95% CI 1.07–1.58), and vascular dementia (HR: 1.38, 95% CI 1.12–1.69). Conclusions: ML-predicted DR was significantly associated with future dementia, highlighting its value in early risk stratification among patients with diabetes.