Supplementary file 1_Distinct insulin sensitivity profiles revealed by clustering based on continuous glucose monitoring data in people living with type 1 diabetes.docx

However, interpretation of CGM data remains challenging. CGM data clustering may support individualized management and identify insulin resistance.</p>Aim<p>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.</p>Methods<p>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.</p>Results<p>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 SeH cluster demonstrated the highest median average glucose level (13.83 [13.07;14.12] mmol/L), while HAH exhibited the highest median coefficient of variation (50 [44;52]%).