Integration of Immunometabolic Composite Indices and Machine Learning for Diabetic Retinopathy Risk Stratification: Insights from NHANES 2011 - 2020.

OBJECTIVE

This study aimed to investigate the association between immunometabolic composite indices and diabetic retinopathy (DR) and to develop predictive models using machine learning (ML) techniques to improve early detection and risk stratification for DR.

DESIGN

A cross-sectional study.

SUBJECTS AND CONTROLS

Data from the National Health and Nutrition Examination Survey 2011-2020 were analyzed, involving 8249 participants categorized into healthy controls (n = 6830), diabetes without retinopathy (n = 918), and DR (n = 501).

METHODS

Immunometabolic indices reflecting insulin resistance, inflammation, and lipid metabolism were evaluated. Multivariate logistic regression models assessed associations with DR, and Bayesian kernel machine regression analyzed nonlinear interactions. Eight ML models, including ensemble methods, were developed to predict DR risk, with feature importance determined by SHapley Additive exPlanations.

MAIN OUTCOME MEASURES

The primary outcome was DR status, classified according to the ETDRS criteria from fundus photography.

RESULTS

Key immunometabolic indices, notably Frailty Index (FRAILTY) and fasting serum insulin (FSI), were significantly associated with increased DR risk, whereas the metabolic score for insulin resistance (METS) showed a protective effect. Bayesian kernel machine regression highlighted complex interactions among indices. Machine learning models achieved high predictive accuracy, particularly XGBoost and LightGBM (area under the curve > 0.9). SHapley Additive exPlanations analyses identified FRAILTY, FSI, and METS as the most influential predictors.

CONCLUSIONS

Immunometabolic dysregulation significantly contributes to DR progression beyond traditional risk factors such as hyperglycemia alone. Incorporating immunometabolic indices into predictive models substantially enhances DR risk stratification, facilitating personalized screening and intervention strategies. Machine learning approaches effectively identify high-risk individuals, underscoring their utility in clinical practice for early DR detection and targeted preventive care.

FINANCIAL DISCLOSURES

The author(s) have no proprietary or commercial interest in any materials discussed in this article.