Beyond the Mean: A Machine Learning-Based Trend Analysis of CGM Metrics for Improved HbA 1C Estimation in Type 2 Diabetes
Year: 2026
Type: article
Abstract: Background: Hemoglobin A 1C (HbA 1C ) is the gold standard for assessing long-term glycemic control in people with diabetes. Increasing use of continuous glucose monitoring (CGM) has led to adoption of the glucose management indicator (GMI) as a CGM‑based HbA 1C estimate, but GMI often differs from laboratory HbA 1C , especially in type 2 diabetes. This discordance may be associated with the fact that GMI, as a measure of central tendency, fails to capture temporal glycemic trends and variability that relate to HbA 1C formation. Objective: To evaluate whether combining CGM-derived metrics capturing variability, excursions, and temporal trends improves estimation of laboratory-measured HbA 1C in type 2 diabetes. Methods: A machine learning framework was applied to CGM data from a three-month randomized trial, including 159 participants with type 2 diabetes. Participants had ≥70% CGM data coverage and valid end-of-trial HbA 1C . From a standardized 90-day CGM window, 51 metrics were extracted. Benchmark models (mean glucose and GMI) were compared with models developed using forward and exhaustive feature selection with threefold cross-validated multiple linear regression. Results: Benchmark models yielded R -squared = 0.53. A forward selection model including five metrics (GMI at night, night-to-overall mean glucose ratio, glycemic risk assessment diabetes equation, time in tight range [3.0-7.8 mmol/L], time above range [13.9 mmol/L] at night) improved R -squared to 0.60. The best-performing model (substituting GRADE at night for GMI at night) achieved a similar R -squared (0.61). Nighttime and hyperglycemia‑related metrics were consistently selected. Conclusion: Continuous glucose monitoring‑based HbA 1C estimation improves when variability and temporal patterns are included. Nighttime hyperglycemia adds notable predictive value, though further validation is needed.
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7160601853