{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:57:25Z","timestamp":1790575045687,"version":"4.1.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This study aimed to cluster CGM data based on interstitial glucose levels (2.2\u201327.8 mmol\/L) and glucose variability, and to compare the identified clusters with biochemical and anthropometric markers of insulin sensitivity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats: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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic>SeH<\/jats:italic>\n                      cluster demonstrated the highest median average glucose level (13.83 [13.07;14.12] mmol\/L), while\n                      <jats:italic>HAH<\/jats:italic>\n                      exhibited the highest median coefficient of variation (50 [44;52]%). The lowest median average glucose level was observed in\n                      <jats:italic>HAN<\/jats:italic>\n                      (6.14 [5.95;6.17] mmol\/L). A marginally significant difference in ALT levels was observed between SeH and No (p=0.07). eGDR differed significantly between\n                      <jats:italic>SeH<\/jats:italic>\n                      \/\n                      <jats:italic>HAN<\/jats:italic>\n                      (p=0.001),\n                      <jats:italic>ModH<\/jats:italic>\n                      \/\n                      <jats:italic>HAN<\/jats:italic>\n                      (p=0.019), and\n                      <jats:italic>MiH<\/jats:italic>\n                      \/\n                      <jats:italic>HAN<\/jats:italic>\n                      (p=0.043), with marginally significant differences between\n                      <jats:italic>HAN<\/jats:italic>\n                      \/\n                      <jats:italic>HAH<\/jats:italic>\n                      (p=0.063). Triglycerides differed significantly between\n                      <jats:italic>HAN\/SeH<\/jats:italic>\n                      (p=0.029),\n                      <jats:italic>HAN<\/jats:italic>\n                      \/\n                      <jats:italic>MiH<\/jats:italic>\n                      (p=0.024).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1963929","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:45:57Z","timestamp":1790574357000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Distinct insulin sensitivity profiles revealed by clustering based on continuous glucose monitoring data in people living with type 1 diabetes"],"prefix":"10.3389","volume":"17","author":[{"given":"Svjatoslavs","family":"Kistkins","sequence":"first","affiliation":[{"name":"Research Institute of Pauls Stradins Clinical University Hospital","place":["Riga, Latvia"]},{"name":"Faculty of Medicine and Life Sciences, University of Latvia","place":["Riga, Latvia"]},{"name":"Institute of Applied Computer Systems, Riga Technical University","place":["Riga, 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