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Title: Chemical mixtures and neurobehavior: a review of epidemiologic findings and future directions.

Authors: Vuong, Ann M; Yolton, Kimberly; Braun, Joseph M; Lanphear, Bruce P; Chen, Aimin

Published In Rev Environ Health, (2020 Sep 25)

Abstract: Background Epidemiological studies have historically focused on single toxicants, or toxic chemicals, and neurodevelopment, even though the interactions of chemicals and nutrients may result in additive, synergistic, antagonistic, or potentiating effects on neurological endpoints. Investigating the impact of environmentally-relevant chemical mixtures, including heavy metals and endocrine disrupting chemicals (EDCs), is more reflective of human exposures and may result in more refined environmental policies to protect the public. Objective In this review, we provide a summary of epidemiological studies that have analyzed chemical mixtures of heavy metals and EDCs and neurobehavior utilizing multi-chemical models, including frequentist and Bayesian methods. Content Studies investigating chemicals and neurobehavior have the opportunity to not only examine the impact of chemical mixtures, but they can also identify chemicals from a mixture that may play a key role in neurotoxicity, investigate interactive effects, estimate non-linear dose response, and identify potential windows of susceptibility. The examination of neurobehavioral domains is particularly challenging given that traits emerge and change over time and subclinical nuances of neurobehavior are often unrecognized. To date, only a handful of epidemiological studies examining neurodevelopment have utilized multi-pollutant models in the investigation of heavy metals and EDCs. However, these studies were successful in identifying contaminants of importance from the exposure mixtures. Summary and Outlook Investigators are encouraged to broaden their focus to include more environmentally relevant mixtures of chemicals using advanced statistical approaches, particularly to aid in identifying potential mechanisms underlying associations.

PubMed ID: 32598325 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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