Clinical Phenotypes of Obstructive Sleep Apnea: A Decade of Evidence Toward Personalized Management

Background: Obstructive sleep apnea (OSA) is a heterogeneous disorder traditionally classified and stratified by the apnea-hypopnea index (AHI), which fails to capture variability in symptom burden, comorbid associations, and treatment responses. Clinical phenotyping has emerged as a promising strategy to improve disease characterization and management over the last decade. Methods: We conducted a narrative literature review of studies published between January 2014 and December 2022 that used cluster analysis to define OSA phenotypes in adults with moderate-to-severe disease (AHI ≥ 15 events/h). Eligible studies employed validated questionnaires, symptom reporting, and comorbidity profiling to identify subgroups. Findings were summarized across diverse populations, with emphasis on phenotype reproducibility, comorbidity associations, and treatment implications. Results: Across international cohorts, three reproducible symptom-based phenotypes were consistently identified: excessively sleepy (ES), disturbed sleep (DS), and minimally symptomatic (MS). Additional subtypes, such as upper airway dominant (UA) and moderately sleepy (MoS), were described in larger cohorts. Phenotypes differed in demographic profiles, comorbidity burden, and treatment adherence. ES patients exhibited the greatest symptom burden, higher cardiovascular risk, and better adherence to positive airway pressure (PAP) therapy, with significant symptomatic improvement. DS patients frequently reported insomnia symptoms, showed modest PAP-related gains, and may benefit from adjunctive insomnia-targeted interventions. MS patients, despite low symptom burden, often carried substantial comorbidity risk, specifically buildup of OSA-related cardiovascular risk. Conclusions: Symptom-based OSA phenotypes are reproducible across diverse populations and provide clinically meaningful insights beyond AHI. They allow for improved risk stratification, highlight gaps in detection of minimally symptomatic patients, and inform personalized treatment strategies. Integrating phenotyping into clinical practice has the potential to enhance diagnostic accuracy, optimize therapeutic outcomes, and refine cardiovascular risk prediction in OSA.