SLEEP AND CARDIAC TACHYARRHYTHMIA: RESULTS FROM THE CROSS-SECTIONAL SLEEP HEART HEALTH STUDY

Abstract

Despite the well-known relationship between sleep disorders and general cardiovascular risk, few studies have examined sleep quality and quantity at pre-clinical levels in patients with cardiac arrhythmias (CA). Patients with CA are at an elevated risk of stroke, sudden cardiac death, disability, and reduced quality of life. In this study, we sought to elucidate the sleep-related predictors of arrhythmia using both subjective and objective measures of sleep. Baseline, comorbidity, electrocardiogram, and polysomnography data from the Sleep Heart Health Study (age 44-90 y) was available for secondary analysis. ECG data was used to identify participants with cardiac arrhythmias during polysomnography. Exposure variables used included blood oxygen saturation, sleep stages, and tertiles of sleep quality and quantity. Unadjusted and adjusted logistic regression was used to quantify the association between sleep and non-sleep related factors and arrhythmia. Of the 5,804 original SHHS sample, a total of 3,453 participants with complete variables of interest were included in the final analysis (mean age: 68.1 ± 10.6 Years, 54% male, 499 with arrhythmia (Atrial Tachyarrhythmia and Conduction Abnormalities), and 2,954 Controls). At the bivariate level, underweight (BMI < 18.5) (OR: 2.86, 95% CI: 1.1 – 7.2, P < 0.0001), sleep time < 6 hours (OR: 2.58, 1.5-4.3, P < 0.0001), % time in REM sleep ( < 17.6; OR: 1.53, 1.2-1.0, P < 0.0001), sleep efficiency ( < 81; OR: 1.9, 1.5-2.3) and regular afternoon naps (OR:1.8, 1.3-2.4, P < 0.0001) were significantly associated with CA. Once adjusted for age and sex, only % time in REM sleep and minimum oxygen saturation during REM (OR: 0.97, 0.96-0.98, P=0.006, and; OR: 0.97, 0.96-0.98, P=0.001, respectively) were significantly associated with CA. Preliminary analyses suggest differences in sleep stages and oxygen saturation as potential targets for future work in the prevention and early management of CA. Further study is necessary to understand the nature of these relationships using longitudinal data with adjustment for traditional CA markers.