Maternal exposure to trace elements and perfluoroalkyl acids and their associations with alterations of the hypothalamic-pituitary-adrenal axis
Bibliographic record
Abstract
Environmental exposures to trace elements and perfluoroalkyl acids (PFAAs) could alter hypothalamic-pituitary-adrenal (HPA) function during pregnancy. Concentrations of trace elements measured in maternal red blood cells and PFAAs measured in maternal plasma, as well as maternal salivary cortisol were quantified from second trimester samples in 243 women from the Alberta Pregnancy Outcomes and Nutrition (APrON) cohort. Regression analyses examine associations between trace elements and PFAAs, and cortisol awakening response (CAR), daytime cortisol and diurnal cortisol slope. Bayesian kernel machine regression (BKMR) analyses investigated mixture effects. In single exposure models, molybdenum (β = -0.99, 95 % CI = -1.74, -0.24) was negatively associated with CAR and lead was negatively associated with daytime cortisol (β = -13.3, 95 % CI = -25.65, -1.10); perfluoroheptanoic acid (PFHpA) was positively associated (β = 4.29, 95 % CI = 0.68, 7.90) with daytime cortisol. In sex-specific analyses molybdenum was negatively associated with CAR and lead was negatively associated with daytime cortisol in women who gave birth to female infants. PFHpA and perfluorohexanesulfonic acid (PFHxS), were positively associated with daytime cortisol in women who gave birth to male infants. Arsenic, mercury, zinc, PFHxS and perfluorooctanesulfonic acid (PFOS) were positively associated with diurnal cortisol slope in the women who gave birth to female infants. No associations survived correction for false discovery rate. In mixture models, molybdenum was the main contributor to CAR in women who gave birth to female infants; PFHpA contributed the most to daytime cortisol in women who gave birth to male infants. Exposure to trace elements and PFAAs in pregnancy may be associated with alterations in maternal HPA axis function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".