Examining Prenatal Dietary Factors in Association with Child Autism-Related Traits Using a Bayesian Mixture Approach: Results from 2 United States Cohorts

• Authors: Kristen Lyall, Drexel University; Juliette Rando, Drexel University; Siwen Wang, Harvard T.H. Chan School of Public Health; Ghassan B. Hamra, Johns Hopkins Bloomberg School of Public Health; Jorge Chavarro, Harvard T.H. Chan School of Public Health; Marc G. Weisskopf, Harvard T.H. Chan School of Public Health; Lisa A. Croen, Kaiser Permanente Division of Research; M. Daniele Fallin, Emory University; Irva Hertz-Picciotto, University of California Davis; Heather E. Volk, Johns Hopkins Bloomberg School of Public Health; Rebecca J. Schmidt, University of California Davis; Craig J. Newschaffer, Pennsylvania State University
• Journal Title: Current Developments in Nutrition
• Volume: Volume 7, Number 8
• Publisher: Elsevier | 2023-08-01, Pages 101978-101978
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1016/j.cdnut.2023.101978
• Subjects: Health Sciences, Nutrition; Health Sciences, Obstetrics and Gynecology; Biology, Neuroscience
• Keywords: Bayesian mixture modeling; EARLI; NHSII; Social Responsiveness Scale; autism; diet

Abstract

Background: Prior work has suggested relationships between prenatal intake of certain nutrients and autism. Objectives: We examined a broad set of prenatal nutrients and foods using a Bayesian modeling approach. Methods: Participants were drawn from the Early Autism Risks Longitudinal Investigation (n = 127), a cohort following women with a child with autism through a subsequent pregnancy. Participants were also drawn from the Nurses’ Health Study II (NHSII, n = 713), a cohort of United States female nurses, for comparison analyses. In both studies, information on prospectively reported prenatal diet was drawn from food frequency questionnaires, and child autism-related traits were measured by the Social Responsiveness Scale (SRS). Bayesian kernel machine regression was used to examine the combined effects of several nutrients with neurodevelopmental relevance, including polyunsaturated fatty acids (PUFAs), iron, zinc, vitamin D, folate, and other methyl donors, and separately, key food sources of these, in association with child SRS scores in crude and adjusted models. Results: In adjusted analyses, the overall mixture effects of nutrients in Early Autism Risks Longitudinal Investigation and foods in both cohorts on SRS scores were not observed, though there was some suggestion of decreasing SRS scores with increasing overall nutrient mixture in NHSII. No associations were observed with folate within the context of this mixture, but holding other nutrients fixed, n–6 PUFAs were associated with lower SRS scores in NHSII. In both cohorts, lower SRS scores were observed with higher intake of some groupings of vegetables, though for differing types of vegetables across cohorts, and some vegetable groups were associated with higher SRS scores in NHSII. Conclusions: Our work extends prior research and suggests the need to further consider prenatal dietary factors from a combined effects perspective. In addition, findings here point to potential differences in nutrient associations based on a family history of autism, which suggests the need to consider gene interactions in future work.