Chest
Sleep: CHEST ReviewsPhenotypic Subtypes of OSA: A Challenge and Opportunity for Precision Medicine
Section snippets
Examples of OSA Patient Clusters and Associated Outcomes
In the last decade, many clusters have been identified among study participants evaluated for sleep apnea. These studies vary dramatically in terms of individuals included (eg, population, clinical, administrative cohorts), sample size (n = 161-72,217), patient features used to identify the clusters (eg, symptoms, polysomnographic indices), and outcomes (eg, cardiovascular disease, CPAP use). A summary of the designs and main findings are noted in Table 1, and the following sections describe
Common Themes
Although direct comparison between results of the aforementioned studies in OSA are not possible due to large discrepancies in populations, OSA features, analytic techniques, and outcomes studied, we attempted (in the following sections) to synthesize the common themes. We first focused on potential OSA subtypes based on relative differences between clusters in age, BMI, sex, symptoms, and comorbidities (Fig 1),52 followed by OSA physiology as assessed by using PSG (Fig 2).
Subtype A. This group
A Potential Role for Phenotypic Clusters in a Precision Medicine Approach to OSA
There is growing evidence and consensus that the one-size-fits-all approaches are insufficient for the diagnosis and management of individuals with sleep apnea.2,16,26,27 Ideally, caring for patients with such a complex disorder would incorporate genetic, pathophysiologic, biomarker, phenotypic, and treatment response characteristics that form the foundation of precision medicine approaches. Targeted prevention, prognostication, and treatment selection based on phenotypes, including the
Limitations of Current Literature, Unanswered Questions, and Future Directions
The examples discussed here highlight the potential utility of phenotypic clusters. However, the studies reviewed also reflect a number of limitations to phenotype-based approaches, in general, and those using unsupervised learning methods, in particular, that will need to be addressed prior to such approaches being used in personalized treatment of patients with OSA. For example, phenotypes based on clinical or polysomnographic features may not reflect unique pathophysiological or biological
Acknowledgments
Financial/nonfinancial disclosures: The authors have reported to CHEST the following: A. Z. is supported by the Parker B. Francis Fellowship Award. H. K. Y. is supported by the NIH/NHLBI K24 HL 132093 Mentoring in Sleep Research and Sleep Interventions in Heart Disease and Stroke.
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