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

Area of Science:

• Sleep Medicine
• Pulmonology
• Genetics

Background:

• Obstructive sleep apnea (OSA) is a heterogeneous disorder where the apnea-hypopnea index alone is insufficient for comprehensive characterization.
• Existing OSA classifications often lack association with clinically meaningful outcomes, necessitating improved phenotyping strategies.

Purpose of the Study:

• To review current and emerging approaches to clinical phenotyping in obstructive sleep apnea.
• To highlight the potential of enhanced phenotyping for improving prognostication, clinical trial selection, mechanistic understanding, and personalized treatment of OSA.

Main Methods:

• Review of literature on clinical phenotyping in OSA, from basic clinical features to complex analytic approaches.
• Discussion of pathophysiologic risk factors and their role in capturing OSA heterogeneity.
• Exploration of machine learning as a strategy for integrating diverse data types for OSA phenotyping.

Main Results:

• Clinical feature-based OSA phenotypes with prognostic and treatment implications have been identified, though many lack outcome association.
• Pathophysiologic factors show promise in reflecting OSA heterogeneity but require clinical validation.
• Machine learning offers a powerful approach to integrate multi-modal data for identifying novel OSA phenotypes.

Conclusions:

• Enhanced phenotyping is crucial for addressing the complexity and heterogeneity of obstructive sleep apnea.
• Future research should focus on clinically validating pathophysiologic phenotypes and leveraging machine learning for data integration to define meaningful OSA categories.