Identifying Patients with Rapid Progression to Diabetic Retinopathy in Type 2 Diabetes: A Simple Prediction Model for Community Settings.

Abstract

Background: Diabetic retinopathy (DR) progression in community-based type 2 diabetes (T2D) remains challenging to predict. This multi-site cohort study aimed to identify novel metabolic risk factors beyond glucose control and develop a resource-adaptable model for prioritizing DR risk screening.

Methods: A prospective, multi-site study was conducted among patients with T2D from 2021 to 2023 across 14 community health centers in Northwestern China (Shaanxi). Baseline and follow-up data on demographic, medical history, and laboratory data were collected. DR was assessed by trained retinal specialists through fundus photography and slit-lamp bio-microscopy. LASSO-penalized logistic regression with 10-fold cross-validation was first performed to identify candidate predictors, followed by multivariable logistic regression to construct the prediction model. Model discrimination was evaluated using receiver operating characteristic (ROC) curve analysis and bootstrap internal validation. Clinical characteristics before and after onset of DR were analyzed using the paired-sample Wilcoxon test.

Results: Among 363 participants who completed follow-up, 50 (13.77%) patients developed DR. Patients who developed DR had longer duration of diabetes, higher HbA1c levels, poorer glycemic control, and elevated urine pH. Multivariate logistic regression identified HbA1c (OR = 1.30), urine pH (OR = 2.55), and diabetes duration (OR = 1.06) as independent risk factors. The prediction model showed moderate discrimination, with an apparent AUC of 0.72 and an optimism-corrected AUC of 0.693 after bootstrap internal validation. In paired analyses, patients exhibited significantly higher BUN and eGFR levels and lower serum creatinine after DR onset (all P < 0.05).

Conclusions: In this community-based cohort, a prediction model incorporating urine pH, HbA1c, and diabetes duration showed potential for identifying individuals at high risk of incident DR. Urine pH may serve as a simple and accessible metabolic marker for risk stratification, while renal functional alterations may reflect early systemic changes beyond glycemic control.