Delta wave activity is a prominent feature of deep sleep, which is significantly associated with sleep quality. The authors hypothesized that delta wave activity disruption during sleep could predict long-term cardiovascular disease (CVD) and CVD mortality risk. The authors used a comprehensive power spectral entropy-based method to assess delta wave activity during sleep based on overnight polysomnograms in 4,058 participants in the SHHS (Sleep Heart Health Study) and 2,193 participants in the MrOS (Osteoporotic Fractures in Men Study) Sleep study. During 11.0 ± 2.8 years of follow-up in SHHS, 729 participants had incident CVD and 192 participants died due to CVD. During 15.5 ± 4.4 years of follow-up in MrOS, 547 participants had incident CVD, and 391 died due to CVD. In multivariable Cox regression models, lower delta wave entropy during sleep was associated with higher risk of coronary heart disease (SHHS: HR: 1.46; 95% CI: 1.02-2.06; = 0.03; MrOS: HR: 1.79; 95% CI: 1.17-2.73; < 0.01), CVD (SHHS: HR: 1.60; 95% CI: 1.21-2.11; < 0.01; MrOS: HR: 1.43; 95% CI: 1.00-2.05; = 0.05), and CVD mortality (SHHS: HR: 1.94; 95% CI: 1.18-3.18; < 0.01; MrOS: HR: 1.66; 95% CI: 1.12-2.47; = 0.01) after adjusting for covariates. The Shapley Additive Explanations method indicates that low delta wave entropy was more predictive of coronary heart disease, CVD, and CVD mortality risks than conventional sleep parameters. The results suggest that delta wave activity disruption during sleep may be a useful metric to identify those at increased risk for CVD and CVD mortality.

中文翻译：

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睡眠期间 Delta 波活动中断与长期心血管疾病和死亡率的关系

Delta波活动是深度睡眠的一个显着特征，与睡眠质量显着相关。作者假设睡眠期间 δ 波活动中断可以预测长期心血管疾病 (CVD) 和 CVD 死亡风险。作者使用基于功率谱熵的综合方法，根据 SHHS（睡眠心脏健康研究）的 4,058 名参与者和 MrOS（男性骨质疏松性骨折研究）睡眠研究的 2,193 名参与者的夜间多导睡眠图来评估睡眠期间的 δ 波活动。在 SHHS 11.0 ± 2.8 年的随访期间，729 名参与者发生了 CVD，192 名参与者因 CVD 死亡。在 MrOS 15.5 ± 4.4 年的随访期间，547 名参与者发生了 CVD，391 人因 CVD 死亡。在多变量 Cox 回归模型中，睡眠期间较低的 δ 波熵与较高的冠心病风险相关（SHHS：HR：1.46；95% CI：1.02-2.06；= 0.03；MrOS：HR：1.79；95% CI：1.17 -2.73；< 0.01）、CVD（SHHS：HR：1.60；95% CI：1.21-2.11；< 0.01；MrOS：HR：1.43；95% CI：1.00-2.05；= 0.05）和 CVD 死亡率（SHHS： HR：1.94；95% CI：1.18-3.18；MrOS：HR：1.66；95% CI：1.12-2.47；调整协变量后。 Shapley 加法解释方法表明，低 δ 波熵比传统睡眠参数更能预测冠心病、CVD 和 CVD 死亡风险。结果表明，睡眠期间 δ 波活动中断可能是识别 CVD 风险增加和 CVD 死亡风险的有用指标。