Discordance between laboratory-measured hemoglobin A1C (HbA1C) and continuous glucose monitoring (CGM)-derived glucose management indicator (GMI) remains a recognized challenge in people with type 2 diabetes mellitus (T2DM). A machine learning analysis published in the Journal of Diabetes Science and Technology evaluated whether incorporating multidimensional CGM-derived metrics could improve HbA1C estimation beyond standard GMI-based models.
The analysis used data from a three-month randomized trial involving 159 participants with T2DM who had at least 70% CGM data coverage and valid end-of-trial HbA1C measurements. Using a standardized 90-day CGM window, the model assessed 51 CGM-derived metrics related to glycemic variability, excursions, and temporal glucose trends.
Findings
Benchmark models using mean glucose and GMI yielded an R² of 0.53.
A forward-selection model incorporating five CGM-derived metrics improved the R² to 0.60.
A model replacing nighttime GMI with nighttime glycemic risk assessment diabetes equation (GRADE) achieved a similar R² of 0.61.
Nighttime and hyperglycemia-related metrics were consistently selected across models.
The analysis showed that incorporating CGM-derived variability and temporal glucose metrics modestly improved HbA1C estimation compared with standard GMI-based approaches alone. The findings support further validation of multidimensional CGM-based models before broader clinical application.
