Continuous Glucose Monitoring-Based Machine Learning Identification of Diurnal Glycemic Patterns and Diabetes Distress in Type 2 Diabetes.

ABSTRACT:

Background

To identify diurnal glycemic patterns in adults with type 2 diabetes (T2D) using continuous glucose monitoring (CGM)-based machine learning and examine their association with diabetes distress, a key psychosocial outcome.

Methods

In this observational study, 137 adults with T2D wore blinded CGM (FreeStyle Libre Pro), yielding 1657 days of data. Glycemic patterns were identified using unsupervised machine learning via Gaussian mixture modeling, validated with Bayesian information criterion and silhouette scores. Diabetes distress was assessed with the 17-item Diabetes Distress Scale and analyzed through analysis of covariance (ANCOVA), adjusting for age, sex, body mass index, diabetes duration, and glucose management indicator.

Results

Clustering identified

SUBMITTER: Lee M 

PROVIDER: S-EPMC12812510 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature