4.3 Article

Associations of sleep patterns with metabolic syndrome indices, body composition, and energy intake in children and adolescents

期刊

PEDIATRIC OBESITY
卷 14, 期 6, 页码 -

出版社

WILEY
DOI: 10.1111/ijpo.12507

关键词

energy intake; metabolic syndrome; obesity; sleep

资金

  1. Eunice Kennedy Shriver National Institute of Child Health and Human Development [1F32HD082982, Z1A-HD00641]
  2. Office of Behavioral and Social Sciences Research
  3. NICHD [1F32HD082982, Z1A-HD00641]
  4. EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [ZIAHD000641] Funding Source: NIH RePORTER
  5. NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [ZICDK071013] Funding Source: NIH RePORTER

向作者/读者索取更多资源

BackgroundSelf-reported short sleep duration is associated with greater risk for metabolic syndrome (MetS), obesity, and higher energy intake (EI). However, studies of these associations in children using objective methods are sparse. ObjectivesThe study aims to determine the associations for sleep patterns with MetS indices, body composition, and EI using objective measures in children. MethodsFree-living sleep and physical activity were measured in 125 children (aged 8-17years, BMI z=0.571.0, 55% female) using wrist-worn actigraphs for 14 nights. Blood pressure, fasting blood levels of lipids, insulin, glucose, waist circumference, and body composition (dual-energy X-ray absorptiometry [DXA]) were obtained during outpatient visits. EI was assessed during an ad libitum buffet meal. ResultsLater weekday and weekend bedtimes were associated with higher systolic blood pressure (Ps<0.05). Sleep duration and bedtime were not significantly associated with other components of MetS, body composition, or EI. Short sleepers (duration less than 7hours) consumed a greater percentage of carbohydrates than those with adequate (greater than or equal to 7hours) sleep (P<0.05). ConclusionIndicators of sleep duration were variably associated with children's eating patterns and risk for chronic disease. Prospective data are needed to determine whether these indicators of sleep quality represent unique or shared risk factors for poor health outcomes.

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