Leng Yue, Chen Jiong, Cavaillès Clémence, Sun Haoqi, Zhao Haoran, Gao Yaqing, Xie Donglin, Chen Xuesong, Huang Weijun, Stone Katie, Yi Hongliang, Hong Shenda, Gao Song
UCSF Weill Institute for Neurosciences.
Peking University Health Science Center.
Res Sq. 2025 Sep 5:rs.3.rs-7433027. doi: 10.21203/rs.3.rs-7433027/v1.
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 Ushaped 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.
睡眠片段化日益被认为是心血管代谢和死亡率结果的潜在风险因素。然而，现有的指标通常仅关注睡眠-觉醒转换，忽视特定睡眠阶段内的片段化，并且缺乏针对临床结果的比较验证。为了填补这一关键空白，我们开发了睡眠时间熵（STE），这是一种源自香农熵的新型生物标志物，它使用睡眠图数据量化整体和特定阶段的片段化。我们使用了两个队列——临床上海睡眠健康研究队列（SSHSC，n = 3219）和基于社区的睡眠心脏健康研究（SHHS，n = 4862）——应用机器学习和Cox回归来评估其预测效用。在SSHSC中，STE在预测糖尿病、高血压和高脂血症方面优于传统指标。在SHHS中，STE与死亡率呈U形关联：与快速眼动（REM）STE的参考组（Q3）相比，最低五分位数（Q1）与全因死亡率较高相关（风险比[HR] = 1.97，95%置信区间[CI]：1.63 - 2.38），最高五分位数（Q5；HR = 1.35，95% CI：1.06 - 1.73）也是如此。心血管疾病死亡率也观察到类似模式。这些发现支持STE作为一种新型、非侵入性、可解释且可扩展的数字生物标志物，用于量化睡眠片段化及其相关的健康风险。
