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

Abstract

Summmary 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.

Personalised medicine in sleep respiratory disorders: focus on obstructive sleep apnoea diagnosis and treatment

“…As highlighted in a recent review [18], there are many levels that have to be considered in dissecting the steps leading to the different phenotypes of OSA before a personalised clinical and therapeutic approach can be successfully developed (figure 1). Moreover, differences are evident between OSA in children, adults and the elderly, and little is known about the evolution of sleep disordered breathing over the lifespan.…”

Year in review 2017: Interstitial lung disease, pulmonary vascular disease and sleep

“…Phenotyping may be based on clinical or polysomnographic features, or according to physiological factors contributing to upper airway obstruction . In addition to anatomical traits such as craniofacial restriction and obesity, non‐anatomical factors including upper airway muscle responsiveness, ventilatory control (loop gain) and respiratory arousal threshold contribute to and modify the tendency for airway closure .…”

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

“…In scoring polysomnographic data, the standard metric used to characterize OSA remains the apnea–hypopnea index (AHI), the sum of the apnea and hypopnea events per hour of sleep. Although used to define OSA severity, this metric poorly correlates to clinical consequences and oversimplifies the complexity of the disorder . The current definition of flow limitation and duration of apnea and hypopnea events may not fully characterize the physiological consequences of respiratory events .…”

“…Although used to define OSA severity, this metric poorly correlates to clinical consequences and oversimplifies the complexity of the disorder. 6 The current definition of flow limitation and duration of apnea and hypopnea events may not fully characterize the physiological consequences of respiratory events. 7 Better characterization of physiological consequences of a respiratory event may be achieved with additional features such as magnitude of the oxygen desaturation, arousal threshold, sleep fragmentation and sympathetic activation.…”