Sleep Temporal Entropy as a Novel Digital Biomarker of Sleep Fragmentation for Cardiometabolic and Mortality Risk

Sleep fragmentation has been increasingly recognized as a potential risk factor for cardiometabolic and mortality outcomes. However, existing metrics often focus solely on sleep–wake transitions, overlooking fragmentation within specific sleep stages, and lacking comparative validation for clinical outcomes. To address this critical gap, we developed Sleep Temporal Entropy (STE), a novel biomarker derived from Shannon entropy that quantifies overall and stage-specific fragmentation using hypnogram data. Using two cohorts—the clinical Shanghai Sleep Health Study Cohort (SSHSC, n = 3,219) and the community-based Sleep Heart Health Study (SHHS, n = 4,862) —we applied machine learning and Cox regression to evaluate its predictive utility. In SSHSC, STE outperformed traditional metrics in predicting diabetes, hypertension, and hyperlipidemia. In SHHS, STE showed U-shaped associations with mortality: compared to the reference group (Q3) of rapid eye movement (REM) STE, the lowest quintile (Q1) was associated with higher all-cause mortality (hazard ratio [HR] = 1.97, 95% confidence interval [CI]: 1.63–2.38), as was the highest quintile (Q5; HR = 1.35, 95% CI: 1.06–1.73). Similar patterns were observed for CVD mortality. These findings support STE as a novel, non-invasive, interpretable, and scalable digital biomarker for quantifying sleep fragmentation and its associated health risks.