J Clin Endocrinol Metab. 2026 Jun 8:dgag224. doi: 10.1210/clinem/dgag224. Online ahead of print.
ABSTRACT
CONTEXT: The importance of utilizing continuous glucose monitoring (CGM) to optimize glycemic profiles and thereby prevent the onset of diabetic complications in patients with type 1 diabetes has been increasingly recognized. Nevertheless, studies evaluating the risk of diabetic complications in patients with type 1 diabetes through machine learning approaches based on CGM data remain limited.
OBJECTIVE: To classify the glycemic profiles of Japanese patients with type 1 diabetes using a data-driven cluster analysis based on CGM and clarify the association between these clusters and diabetic complications.
METHODS: In this cross-sectional study, a cluster analysis using glycemic metrics from CGM of 153 Japanese patients with type 1 diabetes was performed. Logistic regression analysis adjusted for age, sex, and duration of diabetes was performed to compare the risk of diabetic complications by cluster.
RESULTS: The cluster analysis identified four clusters. Cluster 1 (n = 53) exhibited an optimal glycemic profile. Cluster 2 (n = 46) demonstrated an extended duration of hyperglycemia and a higher risk of elevated brachial-ankle pulse wave velocity than Cluster 1. Cluster 3 (n = 39) demonstrated an extended duration of hypoglycemia and a higher risk of severe hypoglycemia than Cluster 1. Cluster 4 (n = 15) demonstrated large glycemic variability associated with hyperglycemia and hypoglycemia. Cluster 4 had higher risks of polyneuropathy, elevated brachial-ankle pulse wave velocity, and higher cardiovascular disease risk scores than Cluster 1.
CONCLUSION: High-risk diabetic complications were identified for each cluster classified by glycemic profile.
PMID:42258635 | DOI:10.1210/clinem/dgag224