Multidimensional assessment and cluster analysis for OSA phenotyping.

Abstract

Obstructive sleep apnea (OSA) is a heterogeneous disease with varying phenotype. A cluster analysis based on multidimensional disease characteristics, including symptoms, anthropometry, polysomnography, and craniofacial morphology, in combination with auto-continuous positive airway pressure titration response and comorbidity profiles, was conducted within a well-characterized cohort of patients with OSA, with the aim to refine the current phenotypic expressions of OSA with clinical implications. Two hundred ninety-one patients with a new diagnosis of moderate to severe OSA referred for auto-continuous positive airway pressure titration to the sleep center were included for analysis. In-laboratory polysomnography and craniofacial computed tomography scanning were performed, followed by an auto-continuous positive airway pressure titration. The symptom of excessive daytime sleepiness was assessed using the Epworth Sleepiness Scale. Three patient phenotypes-normal weight, nonsleepy, moderate OSA; obese, nonsleepy, severe OSA; and obese, sleepy, very severe OSA with craniofacial limitation-were identified. Among the polysomnography parameters, only percentage of N3 time of total sleep time (N3%) and mean pulse oxygen saturation were found to be associated with the Epworth Sleepiness Scale score, and they only explained a small fraction of the variation (R2 = .136). Neck circumference and craniofacial limitation were associated with the more severe phenotype, which had a higher prevalence of hypertension and metabolic syndrome, greater diurnal blood gas abnormalities, and worse positive airway pressure titration response. Three OSA phenotypes were identified according to multiple aspects of clinical features in patients with moderate to severe OSA, who differed in their prevalence of hypertension, metabolic syndrome, diurnal blood gas parameters, and continuous positive airway pressure titration response. Self-reported excessive daytime sleepiness was not related with the severity of sleep breathing disturbance, and craniofacial limitation was associated with the more severe phenotype. These findings highlight the necessity of integrating multiple disease characteristics into phenotyping to achieve a better understanding of the clinical features of OSA. Zhang XL, Zhang L, Li YM, etal. Multidimensional assessment and cluster analysis for OSA phenotyping. J Clin Sleep Med. 2022;18(7):1779-1788.