Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea.

Obstructive sleep apnoea (OSA) is a heterogeneous disorder, and improved understanding of physiologic phenotypes and their clinical implications is needed. We aimed to determine whether routine polysomnographic data can be used to identify OSA phenotypes (clusters) and to assess the associations between the phenotypes and cardiovascular outcomes. Cross-sectional and longitudinal analyses of a multisite, observational US Veteran (n=1247) cohort were performed. Principal components-based clustering was used to identify polysomnographic features in OSA's four pathophysiological domains (sleep architecture disturbance, autonomic dysregulation, breathing disturbance and hypoxia). Using these features, OSA phenotypes were identified by cluster analysis (K-means). Cox survival analysis was used to evaluate longitudinal relationships between clusters and the combined outcome of incident transient ischaemic attack, stroke, acute coronary syndrome or death. Seven patient clusters were identified based on distinguishing polysomnographic features: 'mild', 'periodic limb movements of sleep (PLMS)', 'NREM and arousal', 'REM and hypoxia', 'hypopnoea and hypoxia', 'arousal and poor sleep' and 'combined severe'. In adjusted analyses, the risk (compared with 'mild') of the combined outcome (HR (95% CI)) was significantly increased for 'PLMS', (2.02 (1.32 to 3.08)), 'hypopnoea and hypoxia' (1.74 (1.02 to 2.99)) and 'combined severe' (1.69 (1.09 to 2.62)). Conventional apnoea-hypopnoea index (AHI) severity categories of moderate (15≤AHI<30) and severe (AHI ≥30), compared with mild/none category (AHI <15), were not associated with increased risk. Among patients referred for OSA evaluation, routine polysomnographic data can identify physiological phenotypes that capture risk of adverse cardiovascular outcomes otherwise missed by conventional OSA severity classification.

Zinchuk AV ，Jeon S ，Koo BB ，Yan X ，Bravata DM ，Qin L ，Selim BJ ，Strohl KP ，Redeker NS ，Concato J ，Yaggi HK ... -  《-》

Polysomnographic Phenotypes of Obstructive Sleep Apnea and Incident Type 2 Diabetes: Results from the DREAM Study.

Ding Q ，Qin L ，Wojeck B ，Inzucchi SE ，Ibrahim A ，Bravata DM ，Strohl KP ，Yaggi HK ，Zinchuk AV ... -  《-》

Multi-perspective clustering of obstructive sleep apnea towards precision therapeutic decision including craniofacial intervention.

Kim SJ ，Alnakhli WM ，Alfaraj AS ，Kim KA ，Kim SW ，Liu SY ... -  《-》

Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach.

Obstructive sleep apnea (OSA) is heterogeneous and complex, but its severity is still based on the apnea-hypoapnea index (AHI). The present study explores using cluster analysis (CA), the additional information provided from routine polysomnography (PSG) to optimize OSA categorization. Cross-sectional study of OSA subjects diagnosed by PSG in a tertiary hospital sleep unit during 2016-2020. PSG, demographical, clinical variables, and comorbidities were recorded. Phenotypes were constructed from PSG variables using CA. Results are shown as median (interquartile range). 981 subjects were studied: 41% females, age 56 years (45-66), overall AHI 23events/h (13-42) and body mass index (BMI) 30kg/m2 (27-34). Three PSG clusters were identified: Cluster 1: "Supine and obstructive apnea predominance" (433 patients, 44%). Cluster 2: "Central, REM and shorter-hypopnea predominance" (374 patients, 38%). Cluster 3: "Severe hypoxemic burden and higher wake after sleep onset" (174 patients, 18%). Based on classical OSA severity classification, subjects are distributed among the PSG clusters as severe OSA patients (AHI≥30events/h): 46% in cluster 1, 17% in cluster 2 and 36% in cluster 3; moderate OSA (15≤AHI<30events/h): 57% in cluster 1, 34% in cluster 2 and 9% in cluster 3; mild OSA (5≤AHI<15events/h): 28% in cluster 1, 68% in cluster 2 and 4% in cluster 3. The CA identifies three specific PSG phenotypes that do not completely agree with classical OSA severity classification. This emphasized that using a simplistic AHI approach, the OSA severity is assessed by an incorrect or incomplete analysis of the heterogeneity of the disorder.

Gasa M ，Salord N ，Fontanilles E ，Pérez Ramos S ，Prado E ，Pallarés N ，Santos Pérez S ，Monasterio C ... -  《-》

Phenotypes of patients with mild to moderate obstructive sleep apnoea as confirmed by cluster analysis.

Patients with OSA manifest different patterns of disease. However, this heterogeneity is more evident in patients with mild-moderate OSA than in those with severe disease and a high total AHI. We hypothesized that mild-moderate OSA can be categorized into discreet disease phenotypes, and the aim of this study was to comprehensively describe the pattern of OSA phenotypes through the use of cluster analysis techniques. The data for 1184 consecutive patients, collected over 24 months, was analysed. Patients with a total AHI of 5-30/h were categorized according to the sleep stage and position in which they were predominantly affected. This categorization was compared with one in which patients were grouped using a K-means clustering technique with log linear modelling and cross-tabulation. Patients with mild-moderate OSA can be categorized according to polysomnographic parameters. This clinical categorization was validated by comparison with a categorization in which patients were grouped by unsupervised K-means cluster analysis. The clinical groups identified were: (i) rapid eye movement (REM) predominant OSA, 44.6%; (ii) non-REM predominant OSA, 18.9%; (iii) supine predominant OSA, 61.9%; and (iv) intermittent OSA, 12.4%. Patients categorized as having both REM and supine predominant OSA showed characteristics of both the REM predominant and supine predominant OSA groups. Patients with mild-moderate OSA show different polysomnographic phenotypes. This approach to categorization more appropriately reflects disease heterogeneity and the likely multiple pathophysiological processes involved in OSA.

Joosten SA ，Hamza K ，Sands S ，Turton A ，Berger P ，Hamilton G ... -  《-》