Chronobiologically-informed features from CGM data provide unique information for XGBoost prediction of longer-term glycemic dysregulation in 8,000 individuals with type-2 diabetes
Open accessAbstract
Type 2 Diabetes causes dysregulation of blood glucose, which leads to long-term, multi-tissue damage. Continuous glucose monitoring devices are commercially available and used to track glucose at high temporal resolution so that individuals can make informed decisions about their metabolic health. Algorithms processing these continuous data have also been developed that can predict glycemic excursion in the near future. These data might also support prediction of glycemic stability over longer time horizons. In this work, we leverage longitudinal Dexcom continuous glucose monitoring data to test the hypothesis that additional information about glycemic stability comes from chronobiologically-informed features. We develop a computationally efficient multi-timescale complexity index, and find that inclusion of time-of-day complexity features increases the performance of an out-of-the-box XGBoost model in predicting the change in glucose across days. These findings support the use of chronobiologically-inspired and explainable features to improve glucose prediction algorithms with relatively long time-horizons.
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Multiscale analyses and features of entropy elucidate more information related to the complexity of a data stream than statistics alone.
“Multiscale analyses and features of entropy elucidate more information related to the complexity of a data stream than statistics alone40–42.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [42]
The Results section on model performance reports that multiscale complexity and across-day variance features added information beyond statistical features in CGM data. The Discussion describes sample entropy as a measure of signal complexity, but the paper used a different complexity index; it does not establish the claim for entropy features or data streams generally. No page number is available in our copy. (LLM, conf=0.84)
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- Authors
- Burks, J. H., Joe, L., Kanjaria, K., Monsivais, C., O'laughlin, K., & Smarr, B. L.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Smarr, Benjamin L.
- Venue
- PLOS Digital Health
- Publisher
- Public Library of Science (PLoS)
- Published
- 2025-04-09
- Volume
- 4
- Issue
- 4
- Pages
- e0000815
- ISSN
- 2767-3170 (ISSN-L)
- PMC
- PMC11981153
- OpenAlex
- W4409313475
- Cited by
- 5
- References
- 41
- Citations by year
- 2026: 3 · 2025: 2
- FWCI
- 3.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLOS Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- type 2 diabetes, continuous glucose monitoring, glycemic stability, glycemic dysregulation, chronobiology, glucose prediction, XGBoost, Dexcom
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