Assessment of arsenic, cadmium, lead, mercury, and per- and polyfluoroalkyl substances concentrations in human milk and infant formula in the United States: a systematic review
Bibliographic record
Abstract
BACKGROUND: Foods, including human milk (HM) and infant formula (IF), can be sources of environmental contaminant exposure for infants, which can impact health and development. OBJECTIVES: This systematic review describes arsenic, cadmium, lead, mercury, and per- and polyfluoroalkyl substances (PFAS) concentrations in HM and IF in the United States (PROSPERO #CRD42024528756). METHODS: We searched CAB Abstracts, CENTRAL, CINAHL, Embase, and MEDLINE for peer-reviewed articles published in English through 2 April, 2025 (no date restrictions). Studies that assessed contaminant concentrations in HM or IF from countries rated as "high" or "very high" on the Human Development Index were eligible. Screening, data extraction, and risk of bias assessments were performed by 2 independent reviewers. We narratively synthesized United States studies and assessed the certainty of evidence with Grading of Recommendations Assessment, Development, and Evaluation (GRADE). We developed heat maps for studies from all countries that may help inform evidence gaps in future systematic reviews. RESULTS: From the United States, 14 HM and 16 IF studies were included. For HM, perfluorooctanoic acid (PFOA) concentrations ranged from undetected to 36.1 pg/mL, and perfluorooctane sulfonic acid (PFOS) ranged from undetected to 106 pg/mL (GRADE: moderate); evidence was lacking for perfluorononanoic acid and perfluorohexanesulfonic acid. For IF, all PFAS were largely undetected (GRADE: moderate). For HM and IF, studies for arsenic, cadmium, lead, and mercury had small and unrepresentative samples, and most were published before 2000. We identified 317 and 108 articles for HM and IF, respectively, from other countries. CONCLUSIONS: In published, peer-reviewed United States studies, PFOA and PFOS were detected in HM; PFAS were largely undetected in IF. There was a paucity of contemporary evidence for arsenic, cadmium, lead, and mercury in HM or IF in the United States, but we identified evidence from other countries that could help inform these knowledge gaps. Public health agencies recommend feeding infants HM given the benefits outweigh potential risks of contaminant exposure. This trial was registered at PROSPERO as CRD42024528756 (https://www.crd.york.ac.uk/PROSPERO/view/CRD42024528756).
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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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".