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. 2024 Apr 30;83(17):1671-1684.
doi: 10.1016/j.jacc.2024.02.040. Epub 2024 Apr 3.

Association of Disrupted Delta Wave Activity During Sleep With Long-Term Cardiovascular Disease and Mortality

Sizhi Ai  1 , Shuo Ye  2 , Guohua Li  2 , Yue Leng  3 , Katie L Stone  4 , Min Zhang  5 , Yun-Kwok Wing  6 , Jihui Zhang  7 , Yannis Yan Liang  8
Affiliations

Association of Disrupted Delta Wave Activity During Sleep With Long-Term Cardiovascular Disease and Mortality

Sizhi Ai et al. J Am Coll Cardiol. .

Abstract

Background: Delta wave activity is a prominent feature of deep sleep, which is significantly associated with sleep quality.

Objectives: The authors hypothesized that delta wave activity disruption during sleep could predict long-term cardiovascular disease (CVD) and CVD mortality risk.

Methods: 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.

Results: 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; P = 0.03; MrOS: HR: 1.79; 95% CI: 1.17-2.73; P < 0.01), CVD (SHHS: HR: 1.60; 95% CI: 1.21-2.11; P < 0.01; MrOS: HR: 1.43; 95% CI: 1.00-2.05; P = 0.05), and CVD mortality (SHHS: HR: 1.94; 95% CI: 1.18-3.18; P < 0.01; MrOS: HR: 1.66; 95% CI: 1.12-2.47; P = 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.

Conclusions: 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.

Keywords: cardiovascular disease; delta wave activity; mortality; spectral entropy.

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Conflict of interest statement

Funding Support and Author Disclosures Dr Ai was supported by the National Key R&D Program of China (2021YFC2501500) and the Young Elite Scientists Sponsorship Program by CAST (2021QNRC001). The Sleep Heart Health Study (SHHS) was supported by National Heart, Lung, and Blood Institute cooperative agreements U01HL53916 (University of California-Davis), U01HL53931 (New York University), U01HL53934 (University of Minnesota), U01HL53937 and U01HL64360 (Johns Hopkins University), U01HL53938 (University of Arizona), U01HL53940 (University of Washington), U01HL53941 (Boston University), and U01HL63463 (Case Western Reserve University). The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (NHLBI) (R24 HL114473, 75N92019R002). The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health (NIH) funding. The following institutes provide support: the National Institute on Aging, the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Center for Advancing Translational Sciences, and the NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, R01 AG066671, and UL1 TR000128. NHLBI provides funding for the MrOS Sleep ancillary study “Outcomes of Sleep Disorders in Older Men” under the following grant numbers: R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839. Dr Wing has received personal fees from Eisai Co., Ltd for lecture and travel support from Lundbeck HK Limited and Aculys Pharma, Japan, which were not related to the current study. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. All the individual participants of the SHHS data generated during this study will be made available at the sleepdata.org website. Data from MrOS are available on the MrOS webite. The analysis data set for this specific manuscript is also available from the corresponding author upon request.

Figures

FIGURE 1
FIGURE 1. Delta Wave Entropy and Cardiovascular Risk
Kaplan-Meier curves indicate delta wave entropy percentiles for cardiovascular risks from the SHHS (Sleep Heart Health Study) (A to C) and the MrOS (Osteoporotic Fractures in Men Study) Sleep cohort (D to F). The P values show log-rank test results. Low, 5th percentiles; Mid, 5th to 95th percentiles; High, 95th percentiles. CHD = coronary heart disease; CVD = cardiovascular disease.
FIGURE 1
FIGURE 1. Delta Wave Entropy and Cardiovascular Risk
Kaplan-Meier curves indicate delta wave entropy percentiles for cardiovascular risks from the SHHS (Sleep Heart Health Study) (A to C) and the MrOS (Osteoporotic Fractures in Men Study) Sleep cohort (D to F). The P values show log-rank test results. Low, 5th percentiles; Mid, 5th to 95th percentiles; High, 95th percentiles. CHD = coronary heart disease; CVD = cardiovascular disease.
FIGURE 1
FIGURE 1. Delta Wave Entropy and Cardiovascular Risk
Kaplan-Meier curves indicate delta wave entropy percentiles for cardiovascular risks from the SHHS (Sleep Heart Health Study) (A to C) and the MrOS (Osteoporotic Fractures in Men Study) Sleep cohort (D to F). The P values show log-rank test results. Low, 5th percentiles; Mid, 5th to 95th percentiles; High, 95th percentiles. CHD = coronary heart disease; CVD = cardiovascular disease.
FIGURE 2
FIGURE 2. Exposure-Response Associations of Delta Wave Entropy With Cardiovascular Risk
The potential nonlinear association between delta wave entropy and CVD was assessed by restricted cubic splines with knots at the 5th, 35th, 65th, and 95th percentiles, adjusted for age, sex (only in the SHHS cohort), body mass index, total sleep duration, sleep efficiency, time of sleep spent below 90% oxygen saturation, wake after sleep onset, apnea-hypopnea index, arousal index, history of diabetes and hypertension, and smoking and drinking habit (A to C for the SHHS study; D to F for the MrOS study). Shaded areas represent the 95% CIs in fully adjusted models. P values show the nonlinearity test results. Abbreviations as in Figure 1.
FIGURE 3
FIGURE 3. SHAP Value Variables of Importance
Shapley Additive Explanations (SHAP) values from an XGBoost model trained to predict the cardiovascular risk in both cohorts (A to C for the SHHS study; D to F for the MroS study). Features are ordered based on their cumulative effect on model output. AHI = apnea-hypopnea index; AI = arousal index; BMI = body mass index; SE = sleep efficiency; T90 = time of sleep spent below 90% oxygen saturation; TST = total sleep duration; WASO = wake after sleep onset; other abbreviations as in Figure 1.
CENTRAL ILLUSTRATION
CENTRAL ILLUSTRATION. Predicting Cardiovascular Diseases and Cardiovascular Mortality Using Delta Wave Activity
Delta wave activity on overnight polysomnograms was quantified by a comprehensive entropy-based measure across 2 large independent cohort studies and its associations with cardiovascular diseases (CVDs) and CVD mortality were investigated by machine learning and Cox regression models. Both models suggested that delta wave entropy may better predict the risk of CVD and CVD mortality than conventional sleep parameters. MrOS = Osteoporotic Fractures in Men study; SHAP = Shapley Additive Explanations; SHHS = Sleep Heart Health Study; XGBoost = eXtreme Gradient Boosting.
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