Prenatal particulate air pollution exposure and sleep disruption in preschoolers: Windows of susceptibility. (March 2019)

• Record Type: Journal Article
• Authors: Bose, Sonali; Ross, Kristie R.; Rosa, Maria J.; Chiu, Yueh-Hsiu Mathilda; Just, Allan; Kloog, Itai; Wilson, Ander; Thompson, Jennifer; Svensson, Katherine; Rojo, Martha María Téllez; Schnaas, Lourdes; Osorio-Valencia, Erika; Oken, Emily; Wright, Robert O.; Wright, Rosalind J.
• Is Part Of: Environment international. Volume 124(2019)
• Page Start: 329
• Page End: 335
• Publication Date: 2019-03
• Subjects: DLM distributive lag models -- PM particulate matter -- CNS central nervous system -- BMI body mass index -- REM rapid eye movement -- AS active sleep -- QS quiet sleep -- IS indeterminate sleep -- AOD aerosol optimal depth -- LUR land use regression; Particulate matter -- Air pollution -- Prenatal -- Sleep -- Child -- Preschool-aged; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2019.01.012
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: Background: The programming of sleep architecture begins in pregnancy and depends upon optimal in utero formation and maturation of the neural connectivity of the brain. Particulate air pollution exposure can disrupt fetal brain development but associations between fine particulate matter (PM2.5 ) exposure during pregnancy and child sleep outcomes have not been previously explored. Methods: Analyses included 397 mother-child pairs enrolled in a pregnancy cohort in Mexico City. Daily ambient prenatal PM2.5 exposure was estimated using a validated satellite-based spatio-temporally resolved prediction model. Child sleep periods were estimated objectively using wrist-worn, continuous actigraphy over a 1-week period at age 4–5 years. Data-driven advanced statistical methods (distributed lag models (DLMs)) were employed to identify sensitive windows whereby PM2.5 exposure during gestation was significantly associated with changes in sleep duration or efficiency. Models were adjusted for maternal education, season, child's age, sex, and BMI z-score. Results: Mother's average age was 27.7 years, with 59% having at least a high school education. Children slept an average of 7.7 h at night, with mean 80.1% efficiency. The adjusted DLM identified windows of PM2.5 exposure between 31 and 35 weeks gestation that were significantly associated with decreased sleep duration in children. In addition, increased PM2.5 during weeks 1–8 was associated with decreased sleep efficiency. In other exposure windows (weeks 39–40), PM2.5 was associated with increased sleep duration. Conclusion: Prenatal PM2.5 exposure is associated with altered sleep in preschool-aged children in Mexico City. Pollutant exposure during sensitive windows of pregnancy may have critical influence upon sleep programming. Highlights: Prenatal PM2.5 exposure was associated with sleep disruption in preschoolers. Higher PM2.5 was linked to decreased sleep efficiency and altered sleep duration. Significant associations were identified during sensitive windows of gestation.