Phenotypes in obstructive sleep apnea: A definition, examples and evolution of approaches.

Background 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. Methods 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. Results 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 Conclusions 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.

https://thorax.bmj.com/content/thoraxjnl/73/5/472.full.pdf

Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea

Obstructive sleep apnoea (OSA) is a major challenge for physicians and healthcare systems throughout the world. The high prevalence and the impact on daily life of OSA oblige clinicians to offer effective and acceptable treatment options. However, recent evidence has raised questions about the benefits of positive airway pressure therapy in ameliorating comorbidities. An international expert group considered the current state of knowledge based on the most relevant publications in the previous 5 years, discussed the current challenges in the field, and proposed topics for future research on epidemiology, phenotyping, underlying mechanisms, prognostic implications and optimal treatment of patients with OSA. The group concluded that a revision to the diagnostic criteria for OSA is required to include factors that reflect different clinical and pathophysiological phenotypes and relevant comorbidities ( e.g. nondipping nocturnal blood pressure). Furthermore, current severity thresholds require revision to reflect factors such as the disparity in the apnoea–hypopnoea index (AHI) between polysomnography and sleep studies that do not include sleep stage measurements, in addition to the poor correlation between AHI and daytime symptoms such as sleepiness. Management decisions should be linked to the underlying phenotype and consider outcomes beyond AHI.

https://erj.ersjournals.com/content/erj/52/3/1702616.full.pdf

Challenges and perspectives in obstructive sleep apnoea Report by an ad hoc working group of the Sleep Disordered Breathing Group of the European Respiratory Society and the European Sleep Research Society

Study objectives A recent study of patients with moderate-severe obstructive sleep apnea (OSA) in Iceland identified three clinical clusters based on symptoms and comorbidities. We sought to verify this finding in a new cohort in Iceland and examine the generalizability of OSA clusters in an international ethnically diverse cohort. Methods Using data on 972 patients with moderate-severe OSA (apnea-hypopnea index [AHI] ≥ 15 events per hour) recruited from the Sleep Apnea Global Interdisciplinary Consortium (SAGIC), we performed a latent class analysis of 18 self-reported symptom variables, hypertension, cardiovascular disease, and diabetes. Results The original OSA clusters of disturbed sleep, minimally symptomatic, and excessively sleepy replicated among 215 SAGIC patients from Iceland. These clusters also generalized to 757 patients from five other countries. The three clusters had similar average AHI values in both Iceland and the international samples, suggesting clusters are not driven by OSA severity; differences in age, gender, and body mass index were also generally small. Within the international sample, the three original clusters were expanded to five optimal clusters: three were similar to those in Iceland (labeled disturbed sleep, minimal symptoms, and upper airway symptoms with sleepiness) and two were new, less symptomatic clusters (labeled upper airway symptoms dominant and sleepiness dominant). The five clusters showed differences in demographics and AHI, although all were middle-aged (44.6-54.5 years), obese (30.6-35.9 kg/m2), and had severe OSA (42.0-51.4 events per hour) on average. Conclusions Results confirm and extend previously identified clinical clusters in OSA. These clusters provide an opportunity for a more personalized approach to the management of OSA.

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Recognizable clinical subtypes of obstructive sleep apnea across international sleep centers: a cluster analysis.

Phenotypic Subtypes of OSA: A Challenge and Opportunity for Precision Medicine

Phenotyping obstructive sleep apnea syndrome's comorbidity has been attempted for the first time only recently. The aim of our study was to determine phenotypes of comorbidity in obstructive sleep apnea syndrome patients employing a data-driven approach. Data from 1472 consecutive patient records were recovered from our hospital's database. Categorical principal component analysis and two-step clustering were employed to detect distinct clusters in the data. Univariate comparisons between clusters included one-way analysis of variance with Bonferroni correction and chi-square tests. Predictors of pairwise cluster membership were determined via a binary logistic regression model. The analyses revealed six distinct clusters: A, 'healthy, reporting sleeping related symptoms'; B, 'mild obstructive sleep apnea syndrome without significant comorbidities'; C1: 'moderate obstructive sleep apnea syndrome, obesity, without significant comorbidities'; C2: 'moderate obstructive sleep apnea syndrome with severe comorbidity, obesity and the exclusive inclusion of stroke'; D1: 'severe obstructive sleep apnea syndrome and obesity without comorbidity and a 33.8% prevalence of hypertension'; and D2: 'severe obstructive sleep apnea syndrome with severe comorbidities, along with the highest Epworth Sleepiness Scale score and highest body mass index'. Clusters differed significantly in apnea-hypopnea index, oxygen desaturation index; arousal index; age, body mass index, minimum oxygen saturation and daytime oxygen saturation (one-way analysis of variance P < 0.0001). Binary logistic regression indicated that older age, greater body mass index, lower daytime oxygen saturation and hypertension were associated independently with an increased risk of belonging in a comorbid cluster. Six distinct phenotypes of obstructive sleep apnea syndrome and its comorbidities were identified. Mapping the heterogeneity of the obstructive sleep apnea syndrome may help the early identification of at-risk groups. Finally, determining predictors of comorbidity for the moderate and severe strata of these phenotypes implies a need to take these factors into account when considering obstructive sleep apnea syndrome treatment options.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jsr.12344

Phenotypes of comorbidity in OSAS patients: combining categorical principal component analysis with cluster analysis.

Oxidative stress has a central role in the pathophysiology of obstructive sleep apnea syndrome (OSAS). The DJ-1 protein functions as a sensor of oxidative stress, acting both as a reactive oxygen species scavenger (ROS) and an antioxidative response regulator. The aim of our study is to determine the serum levels of DJ-1 in OSAS patients and assess possible correlations with their clinical, demographical, and biochemical characteristics. The study included 120 subjects from the Sleep Disorder Laboratory of the University Hospital of Thessaly (100 males vs 20 females, mean age 48 ± 10, Apnea–Hypopnea Index (AHI) >5 episodes per hour of sleep). Subjects underwent full-night polysomnography (PSG) followed by morning blood sampling. Serum DJ-1 levels were determined via ELISA kits. Statistical analysis was performed using SPSS 19. The median DJ-1 levels were 56.7 ng/mL (IQR, 34.9–99.3 ng/mL). Statistically significant correlations were detected between DJ-1’s levels and AHI (Spearman’s rho = 0.189, P = 0.04), Desaturation Index (DI; Spearman’s rho = 0.239, P = 0.012), and serum low-density lipoprotein (LDL) (Spearman’s rho = −0.205, P = 0.042). DJ-1 may be a useful biomarker in OSAS due to its correlations with AHI and DI. The correlation with serum LDL warrants further investigation regarding possible implications in OSAS patients’ cardiovascular comorbidities.

George Natsios

Papers

Phenotypes of comorbidity in OSAS patients: combining categorical principal component analysis with cluster analysis.

The DJ-1 protein as a candidate biomarker in obstructive sleep apnea syndrome