Observational studyJournal of diabetes investigation2025
Classification of Japanese type 1 diabetes based on clinical phenotypes and its association with diabetic complications: Across-sectional study.
Observational study in Journal of diabetes investigation, 2025. 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
introductionDespite the increasing number of studies using machine learning to develop individualized treatment strategies, only a few have been conducted in patients with type 1 diabetes. This study aimed to identify the characteristics of Japanese patients with type 1 diabetes, classified into subgroups using data-driven cluster analysis based on pancreatic beta-cell function, obesity, and glycemic control, and clarify the association between these subgroups and diabetic complications. MATERIALS AND
methodsIn this cross-sectional study, a cluster analysis using three variables (C-peptide, body mass index, and glycated hemoglobin) in 206 Japanese patients with type 1 diabetes was performed. Multivariate logistic regression analysis was performed to compare the risk of diabetic complications by subgroup.
resultsThe cluster analysis identified four subgroups. Group 2 (n = 58), characterized by high body mass index levels, had a higher risk of hepatic steatosis than the control group (Group 1, n = 90). Meanwhile, Group 3 (n = 44), characterized by high glycated hemoglobin levels, had higher risks of retinopathy, polyneuropathy, elevated brachial-ankle pulse wave velocity, and hepatic steatosis than Group 1 and Group 4 (n = 14), characterized by residual endogenous insulin, had a higher risk of chronic kidney disease than Group 1.
conclusionsThe risks of diabetic complications differed between subgroups of Japanese patients with type 1 diabetes. Tailored treatment approaches based on subgroup characteristics are a potential treatment option for reducing the risks of diabetic complications in this population.
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