A systematic assessment of the association of polysomnographic indices with blood pressure: the Multi-Ethnic Study of Atherosclerosis (MESA)

Abstract

Study objective: Blood pressure (BP) may be adversely affected by a variety of sleep disturbances, including sleep fragmentation, hypoxemia, respiratory disturbances, and periodic limb movements. We aim to identify which polysomnography indices are most strongly and consistently associated with systolic and diastolic blood pressure (SBP, DBP) levels in a population-based sample.

Design: Cross-sectional analysis of data from 2,040 participants in the Multi-Ethnic Study of Atherosclerosis (MESA) who underwent polysomnography at MESA Exam 5 in 2011-2013.

Setting: Multisite cohort study.

Participants: Participants were mean age 68 y (54% females; 28% African American, 24% Hispanic, 11% Chinese).

Measurements: Thirty-two candidate polysomnography predictors were identified representing the domains of breathing disturbance frequency, hypoxemia, sleep architecture, and periodic limb movements. Cluster analysis was used for variable reduction. Statistical models, adjusted for potential confounders, were derived using stepwise regression. Final models were selected using cross-validation techniques.

Results: The apnea-hypopnea index (AHI) defined using a 4% desaturation hypopnea criterion (AHI4P) was most consistently associated with SBP level. The AHI and periodic limb movement index (associated with arousals; PLMIA) were significantly associated with DBP. Estimated adjusted differences in SBP and DBP levels between an individual with no sleep apnea (AHI4P = 0) and one with moderately severe sleep apnea (AHI4P = 30) were 2.2 mm Hg and 1.1 mm Hg, respectively. Each 10-unit increase in the PLMIA was associated with an increase in DBP of 1.2 mm Hg.

Conclusion: Our results support the use of a currently recommended apnea-hypopnea index definition as a marker of blood pressure risk and indicate that measurement of limb movements with arousals is also independently associated with diastolic blood pressure.

Keywords: cluster analysis; cohort study; cross validation; diastolic blood pressure; hypertension; imputation; multiethnic; sleep exposure; systolic blood pressure.

Figure 1

Sleep exposure variable selection schematic. (A) Exposures were selected from the technician scored and automatically generated variables generated for each subject. The exposures include 2,687 variables across 11 sections. (B) The first data reduction was conducted by selecting 32 variables commonly used in the literature and by clinician input. (C) Cluster analysis was used to reduce the 32 selected exposures to eight clusters each represented by an exposure. The numbers of clusters were selected by an automated algorithm. Two exposures (AHI4PA-REM and Sleep Efficiency) were replaced with clinically relevant alternatives (AHI4PA and Sleep Maintenance). (D) Eight sleep exposures selected for modeling are presented.