Sex difference of pre- and post-natal exposure to six developmental neurotoxicants on intellectual abilities: a systematic review and meta-analysis of human studies
Bibliographic record
Abstract
BACKGROUND: Early life exposure to lead, mercury, polychlorinated biphenyls (PCBs), polybromide diphenyl ethers (PBDEs), organophosphate pesticides (OPPs), and phthalates have been associated with lowered IQ in children. In some studies, these neurotoxicants impact males and females differently. We aimed to examine the sex-specific effects of exposure to developmental neurotoxicants on intelligence (IQ) in a systematic review and meta-analysis. METHOD: We screened abstracts published in PsychINFO and PubMed before December 31st, 2021, for empirical studies of six neurotoxicants (lead, mercury, PCBs, PBDEs, OPPs, and phthalates) that (1) used an individualized biomarker; (2) measured exposure during the prenatal period or before age six; and (3) provided effect estimates on general, nonverbal, and/or verbal IQ by sex. We assessed each study for risk of bias and evaluated the certainty of the evidence using Navigation Guide. We performed separate random effect meta-analyses by sex and timing of exposure with subgroup analyses by neurotoxicant. RESULTS: Fifty-one studies were included in the systematic review and 20 in the meta-analysis. Prenatal exposure to developmental neurotoxicants was associated with decreased general and nonverbal IQ in males, especially for lead. No significant effects were found for verbal IQ, or postnatal lead exposure and general IQ. Due to the limited number of studies, we were unable to analyze postnatal effects of any of the other neurotoxicants. CONCLUSION: During fetal development, males may be more vulnerable than females to general and nonverbal intellectual deficits from neurotoxic exposures, especially from lead. More research is needed to examine the nuanced sex-specific effects found for postnatal exposure to toxic chemicals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".