Abstract
AIMS/HYPOTHESIS: This study aimed to compare the predictive performance of HbA(1c) and a continuous glucose monitoring (CGM)-based updated glucose management indicator (uGMI) in assessing incident diabetic retinopathy risk. METHODS: We used the data from a previously published longitudinal case-control study that collected CGM data for up to 7 years prior to diagnosis of incident diabetic retinopathy or no retinopathy (control participants) among adults with type 1 diabetes. Mutual information scores (MIS), receiver operating characteristics (ROC) curves and machine learning models were used to assess the associations of diabetic retinopathy with HbA(1c), uGMI and CGM-derived metrics. RESULTS: The uGMI demonstrated a stronger association with incident diabetic retinopathy (MIS 0.148) compared with HbA(1c) (MIS 0.078). ROC analysis showed that uGMI had a modestly higher AUC (AUC 0.733) than HbA(1c) (AUC 0.704). Decision tree models incorporating both HbA(1c) and uGMI did not improve clinically significant diabetic retinopathy risk prediction. Machine learning models confirmed the better predictive value of uGMI, especially for HbA(1c) values between 54 mmol/mol (7.1% NGSP) and 58 mmol/mol (7.5% NGSP), where diabetic retinopathy risk escalated significantly. CONCLUSIONS/INTERPRETATION: The uGMI is a slightly stronger predictor of diabetic retinopathy risk compared with HbA(1c). HbA(1c) and uGMI do not appear to be complementary for diabetic retinopathy risk prediction.