Early Life Exposure to Perfluoroalkyl Substances (PFAS) and ADHD: A Meta-Analysis of Nine European Population-Based Studies
Bibliographic record
Abstract
INTRODUCTION: To date, the evidence for an association between perfluoroalkyl substances (PFAS) exposure and attention deficit and hyperactivity disorder (ADHD) is inconclusive. OBJECTIVE: We investigated the association between early life exposure to perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), and ADHD in a collaborative study including nine European population-based studies, encompassing 4,826 mother-child pairs. METHODS: Concentrations of PFOS and PFOA were measured in maternal serum/plasma during pregnancy, or in breast milk, with different timing of sample collection in each cohort. We used a validated pharmacokinetic model of pregnancy and lactation to estimate concentrations of PFOS and PFOA in children at birth and at 3, 6, 12, and 24 months of age. We classified ADHD using recommended cutoff points for each instrument used to derive symptoms scores. We used multiple imputation for missing covariates, logistic regression to model the association between PFAS exposure and ADHD in each study, and combined all adjusted study-specific effect estimates using random-effects meta-analysis. RESULTS: A total of 399 children were classified as having ADHD, with a prevalence ranging from 2.3% to 7.3% in the studies. Early life exposure to PFOS or PFOA was not associated with ADHD during childhood [odds ratios (ORs) ranging from 0.96 (95% CI: 0.87, 1.06) to 1.02 (95% CI: 0.93, 1.11)]. Results from stratified models suggest potential differential effects of PFAS related to child sex and maternal education. CONCLUSION: We did not identify an increased prevalence of ADHD in association with early life exposure to PFOS and PFOA. However, stratified analyses suggest that there may be an increased prevalence of ADHD in association with PFAS exposure in girls, in children from nulliparous women, and in children from low-educated mothers, all of which warrant further exploration. https://doi.org/10.1289/EHP5444.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.045 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".