Redefining Obstructive Sleep Apnea: Multidimensional Phenotyping Beyond the Apnea-Hypopnea Index

Area of Science:

• Sleep Medicine
• Cardiovascular Health
• Respiratory Disorders

Background:

• Obstructive sleep apnea (OSA) affects nearly a billion people globally, with severity often measured by the apnea-hypopnea index (AHI).
• AHI alone inadequately captures OSA's complexity, showing poor correlation with symptoms and limited predictive power for cardiovascular outcomes.
• Individual susceptibility and factors like intermittent hypoxia and hemodynamic effects necessitate refined diagnostic and prognostic approaches beyond AHI.

Purpose of the Study:

• To explore clinical, polysomnographic, and neurophysiological phenotypes for subclassification of OSA beyond the traditional AHI.
• To identify how distinct OSA phenotypes correlate with varying risk profiles, therapeutic responses, and cardiovascular outcomes.
• To establish the utility of phenotyping for improving diagnosis, prognosis, and management strategies in OSA.

Main Methods:

• Conducted a narrative literature synthesis of 70 articles.
• Focused on quantitative and qualitative (Q2) analysis of clinical traits, polysomnographic parameters, and mechanistic insights.
• Reviewed evidence from large cohorts, animal models, and pathophysiological studies to enable OSA subclassification.

Main Results:

• Phenotyping revealed significant heterogeneity in OSA risk and treatment response based on respiratory event type, duration, positional/REM dependence, hypoxic burden, and arousal characteristics.
• Hypoxic burden, REM-predominant OSA, and arousal frequency independently predicted cardiovascular events, mortality, and morbidity more effectively than AHI.
• Specific phenotypes like supine-predominant OSA showed responsiveness to auto-positive airway pressure and positional therapy.

Conclusions:

• Q2-based phenotyping incorporating clinical, polysomnographic, and neurophysiological markers significantly enhances risk stratification and prognosis for OSA patients.
• This approach facilitates individualized management strategies, moving towards precision medicine in sleep apnea care.
• Future research should integrate phenotypic subclassification into diagnostic criteria and treatment planning for OSA.