Sleep characteristics that predict atrial fibrillation

Abstract

Background: The relationship between sleep disruption, independent of obstructive sleep apnea (OSA), and atrial fibrillation (AF) is unknown.

Objective: The purpose of this study was to determine whether poor sleep itself is a risk factor for AF.

Methods: We first performed an analysis of participants in the Health eHeart Study and validated those findings in the longitudinal Cardiovascular Health Study, including a subset of patients undergoing polysomnography. To determine whether the observed relationships readily translated to medical practice, we examined 2005-2009 data from the California Healthcare Cost and Utilization Project.

Results: Among 4553 Health eHeart participants, the 526 with AF exhibited more frequent nighttime awakening (odd ratio [OR] 1.47; 95% confidence interval [CI] 1.14-1.89; P = .003). In 5703 Cardiovascular Health Study participants followed for a median 11.6 years, frequent nighttime awakening predicted a 33% greater risk of AF (hazard ratio [HR] 1.33; 95% CI 1.17-1.51; P <.001). In patients with polysomnography (N = 1127), every standard deviation percentage decrease in rapid eye movement (REM) sleep was associated with a 18% higher risk of developing AF (HR 1.18; 95% CI 1.00-1.38; P = .047). Among 14,330,651 California residents followed for a median 3.9 years, an insomnia diagnosis predicted a 36% increased risk of new AF (HR 1.36; 95% CI 1.30-1.42; P <.001).

Conclusion: Sleep disruption consistently predicted AF before and after adjustment for OSA and other potential confounders across several different populations. Sleep quality itself may be important in the pathogenesis of AF, potentially representing a novel target for prevention.

Keywords: Atrial fibrillation; Insomnia; Obstructive sleep apnea; Rapid eye movement (REM) sleep.

Figure 1.. Associations between selected self-reported sleep characteristics and prevalent AF in the Health eHeart Study.

Odds ratios (ORs) for atrial fibrillation (AF) from multivariable logistic regression models examining individual sleep characteristics (black squares) and all sleep characteristics (white circles). Both types of analysis were adjusted for potential confounders: age, sex, race, hypertension, diabetes, obstructive sleep apnea, coronary artery disease, congestive heart failure, smoking history, alcohol use, and income. Bars denote 95% confidence intervals (CIs). ^a Continuous characteristics are scaled per SD increase (↑) or decrease (↓) in accordance with the direction of worse sleep.

Figure 2.. Associations between self-reported sleep measures and incident AF in the Cardiovascular Health Study (CHS).

Crude (black squares) and adjusted (white circles) hazard ratios (HR) from Cox proportional hazards models for incident atrial fibrillation (AF). Bars denote 95% confidence intervals (CIs). ^a All adjusted models include: age, sex, race, BMI, hypertension, diabetes, obstructive sleep apnea, coronary artery disease, congestive heart failure, smoking history, alcohol use, and income.

Figure 3.. Associations between polysomnographic (PSG) sleep measures and incident AF in a subset (N = 1,127) of the Cardiovascular Health Study (CHS).

Crude (black squares) and adjusted (white circles) hazard ratios (HR) for incident atrial fibrillation (AF). Bars denote 95% confidence intervals (CIs). ^a Sleep onset latency is scaled per SD increase (↑) while all other predictors are scaled per SD decrease (↓). ^b Covariates in the adjusted models include: age, sex, race, BMI, hypertension, diabetes, obstructive sleep apnea, coronary artery disease, congestive heart failure, smoking history, alcohol use, and income.

Figure 4.. Cumulative incidence of AF by insomnia diagnosis in the California Healthcare Cost and Utilization Project (HCUP).

Adjusted cumulative incidence of atrial fibrillation (AF) in patients with (dashed line) and without (solid line) a diagnosis of insomnia. Covariates include age, sex, race, hypertension, diabetes, obstructive sleep apnea, coronary artery disease, congestive heart failure, smoking history, alcohol use, and income. HR = hazard ratio.