Objective Sleep Efficiency Predicts Cardiovascular Disease in a Community Population: The Sleep Heart Health Study

Literature - Yan B, Yang J, Zhao B et al., - J Am Heart Assoc. 2021 Mar 15;e016201. doi: 10.1161/JAHA.120.016201.

Introduction and methods

Previous studies used questionnaires to evaluate the relationship between sleep quality and risk of CVD. These studies showed that sleep quality is associated with CVD, T2DM and metabolic syndrome [1-3]. However, there is little evidence about the link between objective sleep efficiency (SE), measured by polysomnography, and CVD. This study investigated the association between objective SE and CVD using data from the Sleep Heart Health Study (SHHS).

The SHHS is a community-based, prospective cohort study investigating the consequences of sleep-disordered breathing [4]. This analysis included data from 3810 participants (mean age 63.2±11.0 years, 2097 [55%] women). Participants underwent in-home overnight polysomnography. Participants with a history of HF, MI, stroke, revascularization, or whose sleep was affected by polysomnography were excluded. SE was defined as the ratio of total time sleeping/time spent in bed and was classified into 4 groups (≥90%, 85-89.9%, 80-84.9%,<80%). Wake after sleep onset (WASO) was defined as the total time spent awake after going to sleep and categorized into 4 quartiles (Q1: <29.5 minutes; Q2: 29.5-47.0 minutes; Q3: 47.5-78.0 minutes; Q4: >78 minutes). The apnea-hypopnea index (AHI) was defined as all apnea and hypopnea episodes per hour of sleep with at least a 4% drop in oxygen saturation. The primary outcome was first occurrence of MACE, defined as CV death, CHF, MI, and stroke. The secondary outcome was MACE plus revascularization. Mean follow-up was 10.9±2.8 years from baseline polysomnography.

Main results

• Patients with poor SE (<80%) had a higher incidence of the primary and secondary outcomes and CVD mortality compared to patients with SE≥90% after adjustment for age, sex, smoking status, BMI, diabetes mellitus, hypertension, and AHI (primary outcome: HR 1.338, 95%CI 1.025-1.745, P=0.032; secondary outcome: HR 1.250, 95%CI 1.027-1.521, P=0.026; CVD mortality: HR 1.887, 95%CI 1.224-2.909, P=0.004).
• As a continuous variable, SE per 1% was associated with the incidence of the primary outcome, secondary outcome and CVD mortality (primary outcome: HR 0.988, 95%CI 0.979-0.996, P=0.005; secondary outcome: HR 0.989, 95%CI 0.982-0.995, P=0.005; CVD mortality: HR 0.979, 95%CI 0.967-0.991, P=0.001).
• Analyses stratified by AHI <5 events/h versus ≥5 events/h showed that SE was associated with CVD in participants with or without sleep-disordered breathing.
• Patients with a WASO in Q4 had a higher incidence of the primary and secondary outcomes and CVD mortality compared to Q1 after multivariable adjustment (primary outcome: HR 1.436, 95%CI 1.066-1.934, P=0.017; secondary outcome: HR 1.374, 95%CI 1.103-1.712, P=0.005; CVD mortality: HR 2.240, 95%CI 1.377-3.642, P=0.001).
• As a continuous variable, WASO per 1 minute was also associated with the incidence of the primary outcome, secondary outcome and CVD mortality (primary outcome: HR 1.003, 95%CI 1.001-1.005, P=0.012; secondary outcome: HR 1.003, 95%CI 1.001-1.004, P=0.001; CVD mortality: HR 1.005, 95%CI 1.002-1.008, P=0.002).
• A total sleep time of <5 hours was associated with CVD mortality (HR 2.049, 95%CI 1.200-3.500, P=0.009).

Conclusion

References

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