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

Background: Obstructive sleep apnea (OSA) is a complex and diverse disorder affecting almost one billion individuals worldwide. Severity of untreated OSA, measured by the apnea–hypopnea index (AHI), is noted to be associated with an increased all-cause and cardiovascular mortality. Although widely used, AHI insufficiently captures disease variability as there is a poor correlation of symptoms with the AHI. There lies individual susceptibility to the effects of OSA and that parameter alone poorly predicts cardiovascular outcomes without considering intermittent hypoxia and the hemodynamic effects of OSA. Recognition of clinical, polysomnographic, and neurophysiological phenotypes offers an opportunity to refine diagnosis, prognosis, and management strategies. Methods: We conducted a narrative synthesis of the literature involving 70 articles, focusing on quantitative and qualitative (Q2) clinical traits, polysomnographic parameters, and mechanistic insights that enable subclassification of OSA beyond AHI. Evidence from large cohorts, animal models, and pathophysiological studies were reviewed. Results: Phenotyping based on a Q2 analysis of polysomnographic respiratory event predominance, event duration, positional and REM dependence, hypoxic burden, and arousal characteristics reveals significant heterogeneity in risk profiles and therapeutic response. Apnea-predominant OSA correlates with a higher oxygen desaturation index and Epworth sleepiness scale. Hypopnea-predominant OSA correlates with a cardiometabolic disease burden and may show a more favorable response to surgical therapies. The duration of respiratory events is related to cardiovascular risk, and REM-predominant OSA independently predicts hypertension and adverse cardiovascular outcomes. Supine-predominant OSA demonstrates treatment responsiveness to auto-positive airway pressure and positional therapy. Respiratory effort–related arousals (RERAs), RERA-predominant OSA and the broader respiratory disturbance index (RDI) provide neurophysiological insight often missed by AHI-based classifications. Hypoxic burden, rather than AHI, emerged as a superior predictor of cardiovascular events and mortality. Finally, arousal frequency and periodic limb movements independently predict cardiovascular morbidity. Conclusions: Employing Q2-based phenotyping that incorporates clinical, polysomnographic, and neurophysiological markers improves risk stratification, prognosis, and individualized management of OSA. Future investigations should prioritize integrating phenotypic subclassification into diagnostic criteria and treatment planning to advance precision medicine in sleep apnea care.