Novel digital markers of sleep dynamics: causal inference approach revealing age and gender phenotypes in obstructive sleep apnea.

Despite evidence that sleep-disorders alter sleep-stage dynamics, only a limited amount of these parameters are included and interpreted in clinical practice, mainly due to unintuitive methodologies or lacking normative values. Leveraging the matrix of sleep-stage transition proportions, we propose (i) a general framework to quantify sleep-dynamics, (ii) several novel markers of their alterations, and (iii) demonstrate our approach using obstructive sleep apnea (OSA), one of the most prevalent sleep-disorder and a significant risk factor. Using causal inference techniques, we address confounding in an observational clinical database and estimate markers personalized by age, gender, and OSA-severity. Importantly, our approach adjusts for five categories of sleep-wake-related comorbidities, a factor overlooked in existing research but present in 48.6% of OSA-subjects in our high-quality dataset. Key markers, such as NREM-REM-oscillations and sleep-stage-specific fragmentations, were increased across all OSA-severities and demographic groups. Additionally, we identified distinct gender-phenotypes, suggesting that females may be more vulnerable to awakenings and REM-sleep-disruptions. External validation of the transition markers on the SHHS database confirmed their robustness in detecting sleep-disordered-breathing (average AUROC = 66.4%). With advancements in automated sleep-scoring and wearable devices, our approach holds promise for developing low-cost screening tools for sleep-, neurodegenerative-, and psychiatric-disorders exhibiting altered sleep patterns.