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Article

Use of Continuous Glucose Monitoring With Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes.

Journal of diabetes science and technology - 1 May 2026

Metwally Ahmed A, Park Heyjun, Wu Yue, McLaughlin Tracey, Snyder Michael P

Abstract excerpt

The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily driven by insulin resistance (IR), β-cell dysfunction, and incretin deficiency. This review demonstrates that continuous glucose monitoring (CGM) and wearable technologies enable a paradigm shift toward non-invasive, dynamic metabolic phenotyping. We show evidence that...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Identifiers

PMID: 41978474
DOI: 10.1177/19322968261431860
PMCID: PMC13079557

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

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