Abstract P2151: Good Sleep Health Is Associated With Better Cardiometabolic Health In Adolescents: A Multidimensional-Multimethod, Factor-Analytic Approach

Abstract

Background: Life’s Essential 8 highlights the importance of adequate sleep duration in for cardiovascular health. Current evidence demonstrates that health is also influenced by the quality, consistency, and timing of sleep within the 24-hour day. However, no studies have explored which sleep variables contribute to adolescent cardiometabolic health. Hypothesis: The quality, consistency, and timing of sleep will contribute differently to metabolic syndrome (MetS) and each of its components in adolescents. Methods: 307 adolescents (16 yr; 47% female; 23% racial/ethnic minority) from the Penn State Child Cohort had at least 3-nights of actigraphy (ACT), in-lab 9h polysomnography (PSG), self-reported (SR) insomnia symptoms, daytime sleepiness, sleep schedules, completed a physical exam, and a fasting blood draw. A continuous MetS (cMetS) score was calculated as the sum of sex-and age adjusted z-scores of waist circumference (WC), mean arterial pressure (MAP), HDL, triglycerides, and HOMA-IR. Common factor analysis was performed to understand the factor and correlation structure of 58 standardized sleep variables. LASSO Regression selected sleep variables and assessed their importance to cMetS and its components, independently. Results: Factor analysis identified five sleep domains: 1. Timing and Irregularity: means and standard deviations of SR bedtime and waketime, ACT sleep midpoint, and PSG sleep midpoint; 2. Efficiency: means of ACT sleep efficiency (SE), wake after sleep onset (WASO), sleep onset latency (SOL), and number of awakenings; 3. Duration: mean ACT total sleep time (TST), time in bed (TIB), and SR habitual bedtime; 4. Catch-up and Social Jetlag: differences in weekday and weekend SR waketime and TIB and ACT TST and sleep midpoint; 5. Physiologic Sleep Disturbance: PSG TST, SE, SOL, WASO, apnea-hypopnea index (AHI). Results from LASSO indicate that lower PSG AHI contributed to lower cMetS, WC, MAP, triglycerides, and higher HDL, while longer PSG TST contributed to higher HDL and lower MAP. Earlier sleep midpoint contributed to higher WC, HDL and higher sleep regularity contributed to lower HDL. Longer catch-up sleep contributed to lower MAP, and lower social jetlag contributed to lower MAP. Conclusions: Objective measures of sleep contribute most to cardiometabolic health in adolescents, while subjective sleep measures do not contribute to the same extent. These findings highlight the multidimensional association of sleep and cardiometabolic health.