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

Abstract

Obstructive sleep apnea (OSA) is a complex and heterogeneous disorder and the apnea hypopnea index alone can not capture the diverse spectrum of the condition. Enhanced phenotyping can improve prognostication, patient selection for clinical trials, understanding of mechanisms, and personalized treatments. In OSA, multiple condition characteristics have been termed "phenotypes." To help classify patients into relevant prognostic and therapeutic categories, an OSA phenotype can be operationally defined as: "A category of patients with OSA distinguished from others by a single or combination of disease features, in relation to clinically meaningful attributes (symptoms, response to therapy, health outcomes, quality of life)." We review approaches to clinical phenotyping in OSA, citing examples of increasing analytic complexity. Although clinical feature based OSA phenotypes with significant prognostic and treatment implications have been identified (e.g., excessive daytime sleepiness OSA), many current categorizations lack association with meaningful outcomes. Recent work focused on pathophysiologic risk factors for OSA (e.g., arousal threshold, craniofacial morphology, chemoreflex sensitivity) appears to capture heterogeneity in OSA, but requires clinical validation. Lastly, we discuss the use of machine learning as a promising phenotyping strategy that can integrate multiple types of data (genomic, molecular, cellular, clinical) to identify unique, meaningful OSA phenotypes.

Keywords: Cluster analysis; Obstructive sleep apnea; Personalized medicine; Phenotype; Positional; Rapid eye movement (REM) related.

Figure 1. Heat map of key endotype and phenotype qualities illustrated through examples of possible phenotypes described in the literature

Phenotype illustrated above as having at least two data levels with strong supporting evidence (one must be “Outcomes”). Endotype illustrated above as having high degree of supporting evidence for each level (please see text for discussion). CCC – Complete concentric palatal collapse, EDS – Excessive daytime sleepiness, Non-anatomic - (e.g. low arousal threshold or sensitive chemoreflex), OSA – Obstructive sleep apnea * Degree of supporting evidence assigned by authors based on literature review (no formal definitions, for illustration purposes only).

Figure 2. Data levels in obstructive sleep apnea (OSA) phenotyping and the potential benefits

Illustration of phenotyping data levels (risk factor/environment, clinical, pathophysiologic, biological, gen-etic/omic) in OSA and the potential benefits (right-hand column). Each level shows only some examples of the potential components (not intended to be comprehensive). Arrows signify integration of the levels to better understand their relationship in OSA. CCC – complete concentric palatal collapse, CV d/o – cardiovascular disorders, EDS – excessive daytime sleepiness, GWA – genome-wide associations, HTN – hypertension, IL – interleukin, miRNA – microRNA, ncDNA – non-coding DNA, PALM - Passive P_crit, Arousal threshold, Loop gain, and upper airway Muscle responsiveness model, PSG – polysomnographic, UA – upper airway. Structure adapted with permission from Agusti et al., 2011 [115], with permission of the American Thoracic Society. Copyright © 2016 American Thoracic Society.

Figure 3. Iterative process of phenotype identification and validation in obstructive sleep apnea (OSA)

Multiple points of entry into the process are possible. For example, phenotyping process may begin by differentiation of patient group based on a biomarker, later validated by similar clinical prognosis or response to treatment within that subgroup. Alternatively, patient groups may be identified by similar clinical outcomes potentially suggesting a physiologic target for focused treatment. Adapted from Han et al., 2010 [23], with permission of the American Thoracic Society. Copyright © 2016 American Thoracic Society.