期刊
REVIEWS ON ENVIRONMENTAL HEALTH
卷 35, 期 3, 页码 245-256出版社
WALTER DE GRUYTER GMBH
DOI: 10.1515/reveh-2020-0010
关键词
chemical mixtures; environmental mixture analysis; multi-pollutant; neurobehavior; statistical methods for exposure mixtures
资金
- National Institute of Environmental Health Sciences [P01 ES11261, R01 ES020349, R01 ES024381, R01 ES025214, R01 ES014575, R01ES028277]
- Environmental Protection Agency [P01 R829389]
Background: Epidemiological studies have historically focused on single toxicants, or toxic chemicals, and neuro-development, 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.
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