Prenatal air pollution and children’s autism traits score: Examination of joint associations with maternal intake of vitamin D, methyl donors, and polyunsaturated fatty acids using mixture methods

Author:

Bragg Megan G.1ORCID,Gorski-Steiner Irena2,Song Ashley2,Chavarro Jorge E.3,Hart Jaime E.45,Tabb Loni P.6,Weisskopf Marc G.5,Volk Heather2,Lyall Kristen1,

Affiliation:

1. AJ Drexel Autism Institute, Drexel University, Philadelphia, Pennsylvania

2. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland

3. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

4. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts

5. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

6. Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania

Abstract

Background: Maternal nutrient intake may moderate associations between environmental exposures and children’s neurodevelopmental outcomes, but few studies have assessed joint effects. We aimed to evaluate whether prenatal nutrient intake influences the association between air pollutants and autism-related trait scores. Methods: We included 126 participants from the EARLI (Early Autism Risk Longitudinal Investigation, 2009–2012) cohort, which followed US pregnant mothers who previously had a child with autism. Bayesian kernel machine regression and traditional regression models were used to examine joint associations of prenatal nutrient intake (vitamins D, B12, and B6; folate, choline, and betaine; and total omega 3 and 6 polyunsaturated fatty acids, reported via food frequency questionnaire), air pollutant exposure (particulate matter <2.5 μm [PM2.5], nitrogen dioxide [NO2], and ozone [O3], estimated at the address level), and children’s autism-related traits (measured by the Social Responsiveness Scale [SRS] at 36 months). Results: Most participants had nutrient intakes and air pollutant exposures that met US standards. Bayesian kernel machine regression mixture models and traditional regression models provided little evidence of individual or joint associations of nutrients and air pollutants with SRS scores or of an association between the overall mixture and SRS scores. Conclusion: In this cohort with a high familial likelihood of autism, we did not observe evidence of joint associations between air pollution exposures and nutrient intake with autism-related traits. Future work should examine the use of these methods in larger, more diverse samples, as our results may have been influenced by familial liability and/or relatively high nutrient intakes and low air pollutant exposures.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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