ArticleFrontiers in endocrinology2026
Distinct insulin sensitivity profiles revealed by clustering based on continuous glucose monitoring data in people living with type 1 diabetes.
Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Continuous glucose monitoring (CGM) has improved glycemic management in Type 1 Diabetes Mellitus (T1DM). However, interpretation of CGM data remains challenging. CGM data clustering may support individualized management and identify insulin resistance. Aim: This study aimed to cluster CGM data based on interstitial glucose levels (2.2-27.8 mmol/L) and glucose variability, and to compare the identified clusters with biochemical and anthropometric markers of insulin sensitivity. Methods: CGM data from 75 T1DM patients and 20 subjects without diabetes were analyzed using Uniform Manifold Approximation and Projection (UMAP) for dimension reduction, followed by K-means clustering. Estimated glucose disposal rate (eGDR) was calculated to assess insulin sensitivity. Results: Seven clinically distinct CGM phenotypes were identified: Normoglycemia-in-Diabetes (N), Normoglycemia-in-Non-Diabetes (No), Mild Hyperglycemia (MiH), Moderate Hyperglycemia (ModH), Severe Hyperglycemia (SeH), Hypoglycemia-Associated Normoglycemia (HAN), and Hypoglycemia-Associated Hyperglycemia (HAH). The Conclusion: This exploratory CGM-based clustering approach identified phenotypically distinct T1DM subgroups with differing insulin sensitivity profiles. These preliminary findings require validation in larger independent cohorts. If confirmed, such phenotyping may support individualized diabetes management and identification of insulin resistance.
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