Longitudinal Associations between Prenatal Exposure to Phthalates and Steroid Hormones in Maternal Hair Samples from the SEPAGES Cohort
Bibliographic record
Abstract
We assessed phthalate-hormone associations in 382 pregnant women of the new-generation SEPAGES cohort (2014-2017, France) using improved exposure and outcome assessments. Metabolites from seven phthalate compounds and the replacement di(isononyl)cyclohexane-1,2-dicarboxylate (DINCH) were measured in within-subject pools of repeated urine samples collected at the second and third pregnancy trimesters (≈21 samples/trimester). Metabolites from five steroid hormones were measured in maternal hair samples collected at delivery, reflecting cumulative levels over the previous weeks to months. Adjusted linear regression and Bayesian weighted quantile sum (BWQS) mixture models were performed. Each doubling in third-trimester urinary mono-benzyl phthalate (MBzP) concentrations was associated with an average increase of 13.3% (95% CI: 2.65, 24.9) for ∑cortisol, 10.0% (95% CI: 0.26, 20.7) for ∑cortisone, 17.3% (95% CI: 1.67, 35.4) for 11-dehydrocorticosterone, and 16.2% (95% CI: 2.20, 32.1) for testosterone, together with a suggestive 10.5% (95% CI: -1.57, 24.1) increase in progesterone levels. Each doubling in second-trimester urinary di-isononyl phthalate (DiNP) concentrations was inversely associated with testosterone levels (-11.6%; 95% CI: -21.6, -0.31). For most hormones, a nonsignificant trend toward a positive phthalate mixture effect was observed in the third but not in the second trimester. Our study showed that exposure to some phthalate metabolites, especially MBzP, may affect adrenal and reproductive hormone levels during pregnancy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".