Prenatal exposure to polycyclic aromatic hydrocarbons and executive functions at school age: Results from a combined cohort study
Bibliographic record
Abstract
BACKGROUND: Executive functions develop rapidly in childhood, enabling problem-solving, focused attention, and planning. Exposures to environmental toxicants in pregnancy may impair healthy executive function development in children. There is increasing concern regarding polycyclic aromatic hydrocarbons (PAHs) given their ability to transfer across the placenta and the fetal blood-brain barrier, yet evidence from epidemiological studies is limited. METHODS: We examined associations between prenatal PAH exposure and executive functions in 814 children of non-smoking mothers from two U.S. cohorts in the ECHO-PATHWAYS Consortium. Seven mono-hydroxylated PAH metabolites were measured in mid-pregnancy urine and analyzed individually and as mixtures. Three executive function domains were measured at age 8-9: cognitive flexibility, working memory, and inhibitory control. A composite score quantifying overall performance was further calculated. We fitted linear regressions adjusted for socio-demographics, maternal health behaviors, and psychological measures, and examined modification by child sex and stressful life events in pregnancy. Bayesian kernel machine regression was performed to estimate the interactive and overall effects of the PAH mixture. RESULTS: The results from primary analysis of linear regressions were generally null, and no modification by child sex or maternal stress was indicated. Mixture analyses suggested several pairwise interactions between individual PAH metabolites in varied directions on working memory, particularly interactions between 2/3/9-FLUO and other PAH metabolites, but no overall or individual effects were evident. CONCLUSION: We conducted a novel exploration of PAH-executive functions association in a large, combined sample from two cohorts. Although findings were predominantly null, the study carries important implications for future research and contributes to evolving science regarding developmental origins of diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".