Prenatal Exposure to Synthetic Phenols Assessed in Multiple Urine Samples and Dysregulation of Steroid Hormone Homeostasis in Two European Cohorts
Bibliographic record
Abstract
BACKGROUND: Some synthetic phenols alter hormonal pathways involved in successful pregnancy and fetal development. Despite high within-subject temporal variability of phenols, previous studies mostly utilized spot urine samples to assess pregnancy exposure. Herein, we investigated associations between pregnancy exposure to eight phenols assessed in multiple pooled urine samples and steroid hormones assessed in maternal hair reflecting cumulative hormone levels over the previous weeks to months. METHODS: , and 11-dehydrocorticosterone) and two reproductive (progesterone and testosterone) hormones. We ran adjusted linear regressions for each exposure biomarker-outcome pair and Bayesian kernel machine regression for phenols mixture. RESULTS: Bisphenol S was associated with higher cortisol and 11-dehydrocorticosterone concentrations. Propylparaben was associated with lower levels of cortisol, cortisone, and 11-dehydrocorticosterone, while methylparaben was linked to a reduction in cortisol levels. Interestingly, associations identified for parabens were stronger for women carrying female fetuses. No associations for phenol mixture were detected. CONCLUSIONS: Our study suggests that pregnancy exposure to bisphenol S and some parabens (propyl- and methylparaben) may affect production of maternal corticosteroid hormones that are important for a successful pregnancy and fetal development. https://doi.org/10.1289/EHP15117.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".