Diversity of Daily Glycemic Phenotypes Is Independently Associated With HbA1c: A Time-Resolved Continuous Glucose Monitoring (CGM) Clustering Analysis
Authors' conclusion
Time-resolved CGM clustering reveals clinically meaningful daily glycemic phenotypes and demonstrates that phenotype diversity is independently associated with HbA1c.
Study context
- Population
- Participants with T1D or T2D; 5902 days of CGM data5,902 CGM days
- Participants
- 103 participants
- Exposure
- Type 1 vs Type 2
- Type 1n = 19
- Type 2n = 84
- Outcome
- Association of Phenotype Diversity Score (Shannon entropy of each individual's cluster-label distribution) with HbA1c
Methodological detailstime-resolved CGM clustering, k-means with dynamic time warping, multivariable regression
- Time-resolved CGM clustering
- K-means with dynamic time warping
- Multivariable regression
Main finding
As reported in the abstract
Distinct daily glycemic phenotypes identified
8
Correlation of Phenotype Diversity Score with HbA1c (r)
r = 0.600
P < .0001
Fully adjusted association with HbA1c (adjusted for mean of daily differences, CV, insulin use, estimated glomerular filtration rate (eGFR)) (β)
β = 0.322
P = .004
Observational data: associations, not effects.
Original publication
Diversity of Daily Glycemic Phenotypes Is Independently Associated With HbA1c: A Time-Resolved CGM Clustering Analysis. J Diabetes Sci Technol. Published 6 July 2026. doi:10.
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