LAUSR

RATIONALE Obstructive sleep apnea (OSA) is associated with cardiovascular disease and incident type 2 diabetes (T2DM). Seven OSA phenotypes, labeled based on their most distinguishing polysomnographic features, have been shown to be differentially associated with incident cardiovascular disease. However, little is known about the relevance of polysomnographic phenotypes for risk of T2DM. OBJECTIVES To assess whether polysomnographic phenotypes are associated with incident T2DM, and to compare the predictive value of baseline polysomnographic phenotypes with the apnea-hypopnea index (AHI) for T2DM. METHODS The study included 840 individuals without baseline diabetes from a multisite observational US Veteran cohort who underwent OSA evaluation between 2000 and 2004, with follow-up through 2012. The primary outcome was incident T2DM, defined as no diagnosis at baseline, and a new physician diagnosis confirmed by fasting blood glucose (FBG) >126mg/dL during follow-up. Relationships between the seven polysomnographic phenotypes (1. mild, 2. periodic limb movements of sleep (PLMS), 3. non-rapid eye movement and poor sleep, 4. rapid-eye movement, and hypoxia, 5. hypopnea and hypoxia, 6. arousal and poor sleep, 7. combined severe) and incident T2DM were investigated using Cox proportional hazards regression and competing risk regression models, with and without adjustment for baseline covariates. Likelihood ratio tests were conducted to compare the predictive value of the phenotypes with AHI. RESULTS During a median follow-up period of 61 months, 122 (14.5%) patients developed incident T2DM. After adjustment for baseline sociodemographics, FBG, body mass index, comorbidities, and behavioral risk factors, hazard ratios among persons with "hypopnea and hypoxia" and "PLMS" phenotypes as compared with persons with "mild" phenotype were: 3.18 [95% confidence interval (CI), 1.53 to 6.61] and 2.26 (95% CI, 1.06 to 4.83) for incident T2DM, respectively. Mild OSA (5≤AHI<15) (vs. no OSA) was directly associated with incident T2DM in both unadjusted and multivariable-adjusted regression models. The addition of polysomnographic phenotypes, but not AHI, to known T2DM risk factors, greatly improved the predictive value of the computed prediction model. CONCLUSIONS Polysomnographic phenotypes "hypopnea and hypoxia" and "PLMS" independently predict risk of T2DM among a predominantly male Veteran population. Polysomnographic phenotypes improved T2DM risk prediction compared with the use of AHI.