Sleep Fragmentation, Not Nocturnal Hypoxemia, Is the Primary Correlate of Attentional Slowing in Obstructive Sleep Apnea

Background: Obstructive sleep apnea (OSA) is associated with slower response speed, yet conventional severity classification based on the apnea–hypopnea index (AHI) shows limited ability to predict cognitive outcomes. AHI aggregates distinct pathophysiological processes intermittent hypoxemia and sleep fragmentation. Within emerging precision sleep medicine frameworks, disentangling these mechanisms is critical for improved phenotyping and personalized risk assessment. This study aimed to replicate prior findings using a Go/No-Go Continuous Visual Attention Test (CVAT) and to identify the most informative polysomnographic predictor of attentional performance in OSA. Methods: All participants underwent full-night type I polysomnography and a 15-minute Go/No-Go CVAT. After exclusions, 84 patients with OSA and 22 polysomnographically normal controls were analyzed. Attentional performance was indexed by standardized reaction time (Z-score), referenced to a normative database (n = 1,244). Within the OSA group, multiple linear regression with backward elimination evaluated hypoxemia and sleep fragmentation metrics. Results: Patients with OSA demonstrated significantly slower reaction times than controls (p = 0.005). Within OSA, AHI was not associated with attentional performance (p = 0.398). In the final regression model, sleep stage shifts—reflecting sleep–wake instability—emerged as the sole independent predictor of attentional slowing (β = 0.27, p = 0.013), whereas all hypoxemia indices were excluded. Conclusions: Sleep-stage instability represents a key cognitive vulnerability marker in OSA, independent of respiratory event burden. Integrating sleep fragmentation metrics into precision sleep medicine models may enhance individualized phenotyping, identify patients at higher neurocognitive risk, and inform targeted interventions focused on stabilizing sleep architecture rather than relying solely on AHI-based severity classification.