Diabetologia · 2025
Association of HbA1c and an updated glucose management indicator (uGMI) with incident diabetic retinopathy in adults with type 1 diabetes: a longitudinal study.
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Shah Viral N, Xu Yongjin, Dabiri Yaghoub, Mohanadas Hemanth P, Cheng Alan, Dunn Timothy C
Аннотация
AIMS/HYPOTHESIS: This study aimed to compare the predictive performance of HbA1c 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 HbA1c, uGMI and CGM-derived metrics.
RESULTS: The uGMI demonstrated a stronger association with incident diabetic retinopathy (MIS 0.148) compared with HbA1c (MIS 0.078). ROC analysis showed that uGMI had a modestly higher AUC (AUC 0.733) than HbA1c (AUC 0.704). Decision tree models incorporating both HbA1c and uGMI did not improve clinically significant diabetic retinopathy risk prediction. Machine learning models confirmed the better predictive value of uGMI, especially for HbA1c 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 HbA1c. HbA1c and uGMI do not appear to be complementary for diabetic retinopathy risk prediction.
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 HbA1c, uGMI and CGM-derived metrics.
RESULTS: The uGMI demonstrated a stronger association with incident diabetic retinopathy (MIS 0.148) compared with HbA1c (MIS 0.078). ROC analysis showed that uGMI had a modestly higher AUC (AUC 0.733) than HbA1c (AUC 0.704). Decision tree models incorporating both HbA1c and uGMI did not improve clinically significant diabetic retinopathy risk prediction. Machine learning models confirmed the better predictive value of uGMI, especially for HbA1c 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 HbA1c. HbA1c and uGMI do not appear to be complementary for diabetic retinopathy risk prediction.