The Influence of Self‐Reported and Actigraphy‐Derived Sleep Quality on Cardiometabolic Health in a Diverse Cohort of Healthy Adults

Abstract

PurposeInsufficient sleep is associated with adverse cardiometabolic health (CMH) including high blood pressure (BP) and insulin resistance, which increases cardiovascular disease risk. However, there are limited data on insufficient sleep and CMH in healthy adults, particularly in diverse cohorts. Thus, we sought to examine associations between actigraphy‐derived sleep, self‐reported sleep quality, and CMH in healthy adults.Methods99 individuals (50 female, age: 26 ± 12yrs, BMI: 25.5 ± 4.1 kg/m2, resting BP: 108±11/ 65 ± 8 mmHg, mean ± SD) were included in this analysis. We assessed objective sleep quality, including sleep duration and efficiency using wrist‐worn Philips Actiwatch Spectrum PLUS monitors (8.4 ± 2.7 days). Self‐reported sleep was characterized using the Pittsburgh Sleep Quality Index (0 [better] to 21 [worse]) and an average weekly score to the question “how rested do you feel” (0 [better] to 4 [worse]). We combined all four sleep variables to create a multidimensional sleep score (0 [better] to 4 [worse]). Following ≥10‐minutes of supine rest, we assessed oscillometric brachial BP in triplicate. We assessed estimated central BP with pulse wave analysis and arterial stiffness via carotid‐femoral pulse wave velocity (cf‐PWV) with SphygmoCor. We evaluated endothelial function using brachial artery flow mediated dilation (FMD) via ultrasound. We are collected venous blood samples to calculate homeostatic model assessment of insulin resistance (HOMA‐IR) via blood glucose and insulin concentration. We performed stepwise regressions controlling for sex, race, age, and BMI (model 1) and with the addition of individual sleep metrics (model 2) or the multidimensional sleep score (model 3) to determine whether sleep was associated with measures of CMH.ResultsAdding independent sleep variables or composite sleep score did not improve model fit for brachial BP (e.g., systolic BP Model 1: R2= 0.301, p < 0.001, Model 3: R2= 0.262, p<0.001). The addition of sleep variables improved model fit for central BP (e.g., systolic BP Model 1: R2= 0.280, p < 0.001, Model 3: R2= 0.306, p <0.001) and cf‐PWV (Model 1: R2= 0.153 p < 0.001, Model 2: R2= 0.159, p = .014). Furthermore, the multidimensional sleep score was independently associated with central systolic BP (β± SE = ‐2.41 ± 0.961, p = 0.015) and central diastolic BP (β± SE = ‐1.97 ± 0.91, p = 0.035). Finally, the addition of the multidimensional sleep score improved model fit for FMD (Model 1: R2=0.113, p = 0.012, Model 3: R2= 0.132, p = 0.023) and HOMA‐IR (Model 1: R2= 0.229, p<0.001, Model 3: R2= 0.246, p <0.001).ConclusionOur preliminary data indicate that multidimensional sleep is independently associated with central BP and the addition of individual or multidimensional sleep adds upon the predictive value of sex, race age and BMI in predicting CMH in a diverse cohort of healthy adults.