Dataset Information

Nutrition implications for fetal alcohol spectrum disorder.

ABSTRACT: Prenatal alcohol exposure produces a multitude of detrimental alcohol-induced defects in children collectively known as fetal alcohol spectrum disorder (FASD). Children with FASD often exhibit delayed or abnormal mental, neural, and physical growth. Socioeconomic status, race, genetics, parity, gravidity, age, smoking, and alcohol consumption patterns are all factors that may influence FASD. Optimal maternal nutritional status is of utmost importance for proper fetal development, yet is often altered with alcohol consumption. It is critical to determine a means to resolve and reduce the physical and neurological malformations that develop in the fetus as a result of prenatal alcohol exposure. Because there is a lack of information on the role of nutrients and prenatal nutrition interventions for FASD, the focus of this review is to provide an overview of nutrients (vitamin A, docosahexaenoic acid, folic acid, zinc, choline, vitamin E, and selenium) that may prevent or alleviate the development of FASD. Results from various nutrient supplementation studies in animal models and FASD-related research conducted in humans provide insight into the plausibility of prenatal nutrition interventions for FASD. Further research is necessary to confirm positive results, to determine optimal amounts of nutrients needed in supplementation, and to investigate the collective effects of multiple-nutrient supplementation.

SUBMITTER: Young JK

PROVIDER: S-EPMC4224205 | biostudies-other | 2014 Nov

REPOSITORIES: biostudies-other

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Nutrition implications for fetal alcohol spectrum disorder.

Young Jennifer K JK Giesbrecht Heather E HE Eskin Michael N MN Aliani Michel M Suh Miyoung M

Advances in nutrition (Bethesda, Md.) 20141114 6

Prenatal alcohol exposure produces a multitude of detrimental alcohol-induced defects in children collectively known as fetal alcohol spectrum disorder (FASD). Children with FASD often exhibit delayed or abnormal mental, neural, and physical growth. Socioeconomic status, race, genetics, parity, gravidity, age, smoking, and alcohol consumption patterns are all factors that may influence FASD. Optimal maternal nutritional status is of utmost importance for proper fetal development, yet is often al ...[more]

PMID: 25398731

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Project description:Background: Fetal alcohol spectrum disorder (FASD) is a developmental disorder that manifests through a range of cognitive, adaptive, physiological, and neurobiological deficits resulting from prenatal alcohol exposure. Although the North American prevalence is currently estimated at 2-5%, FASD has proven difficult to identify in the absence of the overt physical features characteristic of fetal alcohol syndrome. As interventions may have the greatest impact at an early age, accurate biomarkers are needed to identify children at risk for FASD. Building on our previous work identifying distinct DNA methylation patterns in children and adolescents with FASD, we have attempted to validate these associations in a different clinical cohort and to use our DNA methylation signature to develop a possible epigenetic predictor of FASD. Methods: Genome-wide DNA methylation patterns were analyzed using the Illumina HumanMethylation450 array in the buccal epithelial cells of a cohort of 48 individuals aged 3.5-18 (24 FASD cases, 24 controls). The DNA methylation predictor of FASD was built using a stochastic gradient boosting model on our previously published dataset FASD cases and controls (GSE80261). The predictor was tested on the current dataset and an independent dataset of 48 autism spectrum disorder cases and 48 controls (GSE50759). Results: We validated findings from our previous study that identified a DNA methylation signature of FASD, replicating the altered DNA methylation levels of 161/648 CpGs in this independent cohort, which may represent a robust signature of FASD in the epigenome. We also generated a predictive model of FASD using machine learning in a subset of our previously published cohort of 179 samples (83 FASD cases, 96 controls), which was tested in this novel cohort of 48 samples and resulted in a moderately accurate predictor of FASD status. Upon testing the algorithm in an independent cohort of individuals with autism spectrum disorder, we did not detect any bias towards autism, sex, age, or ethnicity. Conclusion: These findings further support the association of FASD with distinct DNA methylation patterns, while providing a possible entry point towards the development of epigenetic biomarkers of FASD.

Project description:Background:Fetal alcohol spectrum disorder (FASD) is a developmental disorder that manifests through a range of cognitive, adaptive, physiological, and neurobiological deficits resulting from prenatal alcohol exposure. Although the North American prevalence is currently estimated at 2-5%, FASD has proven difficult to identify in the absence of the overt physical features characteristic of fetal alcohol syndrome. As interventions may have the greatest impact at an early age, accurate biomarkers are needed to identify children at risk for FASD. Building on our previous work identifying distinct DNA methylation patterns in children and adolescents with FASD, we have attempted to validate these associations in a different clinical cohort and to use our DNA methylation signature to develop a possible epigenetic predictor of FASD. Methods:Genome-wide DNA methylation patterns were analyzed using the Illumina HumanMethylation450 array in the buccal epithelial cells of a cohort of 48 individuals aged 3.5-18 (24 FASD cases, 24 controls). The DNA methylation predictor of FASD was built using a stochastic gradient boosting model on our previously published dataset FASD cases and controls (GSE80261). The predictor was tested on the current dataset and an independent dataset of 48 autism spectrum disorder cases and 48 controls (GSE50759). Results:We validated findings from our previous study that identified a DNA methylation signature of FASD, replicating the altered DNA methylation levels of 161/648 CpGs in this independent cohort, which may represent a robust signature of FASD in the epigenome. We also generated a predictive model of FASD using machine learning in a subset of our previously published cohort of 179 samples (83 FASD cases, 96 controls), which was tested in this novel cohort of 48 samples and resulted in a moderately accurate predictor of FASD status. Upon testing the algorithm in an independent cohort of individuals with autism spectrum disorder, we did not detect any bias towards autism, sex, age, or ethnicity. Conclusion:These findings further support the association of FASD with distinct DNA methylation patterns, while providing a possible entry point towards the development of epigenetic biomarkers of FASD.

Dataset Information

Nutrition implications for fetal alcohol spectrum disorder.

Publications

Nutrition implications for fetal alcohol spectrum disorder.

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