Chest
Volume 157, Issue 2, February 2020, Pages 403-420
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Sleep: CHEST Reviews
Phenotypic Subtypes of OSA: A Challenge and Opportunity for Precision Medicine

https://doi.org/10.1016/j.chest.2019.09.002Get rights and content

Current strategies for the management of OSA reflect a one-size-fits-all approach. Diagnosis and severity of OSA are based on the apnea-hypopnea index and treatment initiated with CPAP, followed by trials of alternatives (eg, oral appliances) if CPAP “fails.” This approach does not consider the heterogeneity of individuals with OSA, reflected by varying risk factors, pathophysiological causes, clinical manifestations, and consequences. Recently, studies using analytic approaches such as cluster analysis have taken advantage of this heterogeneity to identify OSA phenotypes, or subtypes of patients with unique characteristics, that may enable more personalized approaches to prognostication and treatment. Examples include symptom-based subtypes such as “excessively sleepy” and “disturbed sleep” with differing impact of CPAP on symptoms and health-related quality of life. Polysomnographic subtypes, distinguished by respiratory event association with hypoxemia, arousals, or both, exhibit varying risks of cardiovascular disease and response to therapy. This review summarizes the findings from recent cluster analysis studies in sleep apnea and synthesizes common themes to describe the potential role (and limitations) of phenotypic subtypes in precision medicine for OSA. It also highlights future directions, including linking of phenotypes to clinically relevant outcomes, rigorous and transparent assessment of phenotype reproducibility, and need for tools that categorize patients into subtypes, to prospectively validate phenotype-based prognostication and treatment approaches. Finally, we highlight the critical need to include women and more racially/ethnically diverse populations in this area of research if we are to leverage the heterogeneity of OSA to improve patient lives.

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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