Aims/hypothesis <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions/interpretation <p>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.</p> Graphical Abstract <p></p>