Physiological phenotypes of obstructive sleep apnea identified by cluster analysis: Association with type 2 diabetes

AIMS/INTRODUCTION: Obstructive sleep apnea (OSA) is a multifaceted condition that may variably influence metabolic risk. This study aimed to identify physiological phenotypes of OSA using polysomnographic (PSG) data and examine their association with type 2 diabetes (T2DM) in a Japanese cohort. MATERIALS AND METHODS: Among 3,683 individuals who underwent attended PSG between April 2019 and March 2024, 602 adults with OSA were included. Phenotypes were identified by k-means clustering. The association between phenotype and T2DM was assessed using logistic regression adjusted for covariates. RESULTS: Five phenotypes were identified: Severe desaturation, REM-predominant, PLMS-dominant, Sleep-fragmented, and Mild. Severe desaturation and REM-predominant types showed higher T2DM prevalence (54.5% and 45.7%). In multivariable analysis, both were independently associated with T2DM compared with the Mild type (adjusted odds ratio [OR] 5.00, 95% CI 1.52-16.4; and OR 3.12, 95% CI 1.32-7.38). To further explore the underlying mechanisms, logistic regression including all participants showed that CT90 was independently associated with T2DM, even after adjustment for arousal threshold and AHI. CONCLUSIONS: Data-driven clustering identified distinct physiological phenotypes of OSA. In addition to the Severe desaturation type, the REM-predominant type was also strongly associated with T2DM, underscoring the metabolic heterogeneity of OSA. Prospective studies are needed to determine causal relationships and evaluate phenotype-targeted interventions.