Chemical mixtures and neurobehavior: a review of epidemiologic findings and future directions
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".