Environmental phenol mixture during pregnancy and child sleep quality in the ECHO cohort
Bibliographic record
Abstract
Introduction Poor sleep quality in childhood can predict sleep quality throughout the lifecourse and other health outcomes. Endocrine-disrupting chemicals can affect adults’ sleep quality, and prenatal phenol exposure impacts fetal development. Objective To assess associations between prenatal phenol concentrations and child sleep outcomes. Methods We used data from the National Institutes of Health-funded Environmental influences on Child Health Outcomes (ECHO) Cohort (n = 1,198) that were collected from 2008 to 2019 at several sites across the United States. The present analysis was conducted in 2023–2024. Using single-pollutant and mixture models, we examined associations between prenatal phenol concentrations and three key child sleep quality outcomes: sleep problems, disturbance, and impairment. Child sleep outcomes were assessed using the Child Behavior Checklist (CBCL) and the Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance and Sleep-Related Impairment scales. Unadjusted and multivariable-adjusted models were examined, with stratified models and interaction terms used to examine interactions with child sex. Results Of the eight phenols assessed, higher prenatal methylparaben concentrations were associated with lower child sleep-related impairment scores (β = −4.79, 95% CI: −9.45 to −0.14). Sex modified the associations for benzophenone-3 and PROMIS sleep disturbance T-scores, where the association was stronger among boys (tertile 3 vs. 1, β = 3.20; 95% CI: 0.27–6.14; p = 0.033) and did not persist among girls. Bisphenol A was associated with sleep-related impairment among boys (tertile 2 vs. 1, β = −5.69; 95% CI: 0.55–10.82; p = 0.031). Phenol mixtures were not associated with sleep outcomes overall or by sex. Conclusion The findings suggest that phenol exposure during pregnancy may be associated with child sleep quality and that child sex modifies this association.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".