Diversity of Daily Glycemic Phenotypes Is Independently Associated With HbA1c: A Time-Resolved CGM Clustering Analysis

Abstract

Background:

Although continuous glucose monitoring (CGM)-derived summary metrics are widely used, they may obscure the intraday temporal organization and potentially overlook clinically relevant differences in daily glucose patterning across time. We aimed to identify distinct daily glycemic phenotypes using time-resolved clustering and to examine whether the diversity of these patterns is independently associated with glycated hemoglobin (HbA1c).

Methods:

We analyzed 5902 days of CGM data from 103 participants with type 1 (n = 19) and type 2 (n = 84) diabetes. Daily waveforms were clustered using k-means with dynamic time warping (DTW) to capture temporal features independent of phase shifts. A “Phenotype Diversity Score” (Shannon entropy of each individual’s cluster-label distribution) quantified the variety of phenotypes expressed per participant. Multivariable regression assessed its association with HbA1c, adjusting for established variability metrics, insulin use, and estimated glomerular filtration rate (eGFR).

Results:

Eight distinct daily glycemic phenotypes were identified. The Phenotype Diversity Score strongly correlated with HbA1c (r = 0.600, P < .0001). In a fully adjusted model, including mean of daily differences, coefficient of variation, insulin use, and eGFR, the Phenotype Diversity Score remained independently associated with HbA1c (β = 0.322, P = .004).

Conclusions:

Time-resolved CGM clustering reveals clinically meaningful daily glycemic phenotypes and demonstrates that phenotype diversity is independently associated with HbA1c. This pattern-based perspective complements conventional summary metrics and may help explain glycemic heterogeneity among individuals with similar average glucose levels.