Arsenic, cadmium, lead, mercury, and PFAS exposure during pregnancy or lactation and respective concentrations in human milk: Systematic review
Bibliographic record
Abstract
Contaminant exposure during pregnancy or lactation may result in contaminant transfer into human milk (HM). This systematic review (PROSPERO# CRD42024530326) assessed relationships between arsenic, cadmium, lead, mercury, and per- and polyfluoroalkyl substances (PFAS) exposure measured by various biospecimen concentrations collected during pregnancy or lactation and respective concentrations in HM using studies conducted in countries rated 'high' or 'very high' on the Human Development Index. CAB Abstracts, CENTRAL, CINAHL, Embase, and MEDLINE, were searched for peer-reviewed English language articles through April 2, 2025. Direction, magnitude, and statistical significance of reported correlations (r, ρ, β, or unspecified) were synthesized narratively. Risk of bias (ROB) was assessed using ROBINS-E. Certainty of evidence was assessed using GRADE. From 3836 records identified, 48 articles from 46 studies (16 prospective cohorts, 30 cross-sectional) conducted in 25 countries were included (arsenic n = 4 articles, cadmium n = 5, lead n = 18, mercury n = 15, PFAS n = 10). Higher exposure to lead, mercury, PFOA, and PFOS during pregnancy or lactation correlated with higher concentrations of these contaminants in HM, respectively (correlation = 0.05-0.88 for lead, 0.0265-0.66 for mercury, 0.353-0.97 for PFOA, and 0.32-0.97 for PFOS). Certainty of evidence was moderate for lead, PFOA, and PFOS, and low for mercury. The evidence for arsenic and cadmium was limited and inconclusive, as well as evidence about these contaminants specifically from foods. In conclusion, higher exposure to lead, mercury, PFOA, and PFOS during pregnancy or lactation correlated with concentrations of these contaminants in HM. Reducing contaminant exposure during pregnancy or lactation could potentially reduce concentrations in HM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".