Abstract MP93: Actigraphy Measured Sleep Indices and Adiposity

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Abstract

Background: Previous findings on sleep and adiposity in adults have been inconsistent. Limitations of these studies include self-reported sleep measures, one measure of adiposity, and racially/ethnically homogenous populations.

Objectives: We examined the cross-sectional relationship between indices of objectively measured sleep and adiposity in 2,146 MESA Sleep study participants.

Methods: Sleep duration, sleep efficiency (% time asleep during sleep period), and night-to-night variability in sleep duration were assessed using at-home 7-day actigraphy and were modeled categorically. Adiposity was measured as body mass index (BMI) and waist circumference (WC). Multivariate linear models were used to examine relationships between sleep exposures and each outcome. In models with dichotomous outcomes, we used logistic regression post-estimation commands to calculate adjusted probabilities standardized to the total population for each sleep measure category. These probabilities were used to calculate prevalence differences for overweight (BMI 25-29.9 kg/m2), obesity (BMI ≥ 30 kg/m2), and abdominal obesity (men: WC ≥102 cm; women ≥88 cm).

Results: Of 2146 participants (mean age 68.6 years), 53.7% were female, 30% were white, 46.2% Black/African American, 5.0% Chinese, and 41.9% Hispanic. Participants who slept <6 hours/night had significantly higher BMI and WC relative to those who slept 7-8 hours. Those who slept <5 hours had a 15% excess prevalence of obesity (BMI ≥ 30 vs. <25 kg/m2) (95% CI: 0.07-0.23) and a 10% excess prevalence of abdominal obesity (95% CI: 0.03-0.17) compared to those who slept 7-8 hours. Poor sleep efficiency and greater night-to-night sleep variability were also associated with a greater prevalence of obesity.

Conclusions: Among a multi-racial/ethnic cohort, we found robust associations across multiple indices of sleep and adiposity. Targeting sleep characteristics may be of benefit in obesity interventions, but more research is needed to rule out reverse causality.

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