Prenatal and childhood exposures to heavy metals and their associations with child cognition, motor skills, behaviour and mental health
Bibliographic record
Abstract
Exposure to arsenic (As), cadmium (Cd), lead (Pb) and mercury (Hg), prenatally and in childhood could pose a significant risk to children's neurodevelopmental outcomes. A mini-review synthesized the findings of original peer-reviewed prospective cohort studies that investigated associations between prenatal and/or childhood exposure to As, Cd, Pb and Hg and chemical mixtures that included these metals, and cognitive, motor, behaviour and mental health outcomes in children and adolescents. Scopus, OVID Medline, EMBASE and PsychINFO were searched for relevant studies published in English between January 01, 2022, and June 30, 2025. Of the 1089 studies identified, 77 met the criteria for inclusion. Thirty-four different cohorts for 18 countries were included, and sample sizes ranged from 48 to 96,165. Exposure was primarily assessed using biological samples such as maternal and child blood, cord blood, and maternal and child urine. The findings of this review provide strong support for the contention that higher levels of prenatal and childhood exposure to As, Cd, Pb and Hg, and their mixtures are linked with adverse cognitive, motor, behavioural and mental health outcomes in children. There is some suggestion that these effects may differ by child sex. Prenatal and childhood exposure to these toxic metals has lasting consequences for children's neurodevelopment. Future research that examines the effects of prenatal, early childhood and continued exposure to these toxic metals on adult neurodevelopment is critical. Further, the potential mitigating effects of maternal and child nutrition and the influences of the psychosocial environment on long term outcomes are areas in need of future study.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".