Association of HbA1c and an updated glucose management indicator (uGMI) with incident diabetic retinopathy in adults with type 1 diabetes: a longitudinal study
Year: 2025
Type: article
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. Graphical Abstract
Source: Diabetologia
Authors: Viral N. Shah, Yongjin Xu, Yaghoub Dabiri, Hemanth P. Mohanadas, Alan Cheng, Timothy C. Dunn
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,490
Funder: Abbott Diabetes Care
OpenAlex ID: W4416100294