Maternal exposure to bisphenols, phthalates, perfluoroalkyl acids, and trace elements and their associations with gestational diabetes mellitus in the APrON cohort
Bibliographic record
Abstract
The increasing global prevalence of gestational diabetes mellitus (GDM) has been hypothesized to be associated with maternal exposure to environmental chemicals. Here, among 420 women participating in the Alberta Pregnancy Outcomes and Nutrition (APrON) cohort study, we examined associations between GDM and second trimester blood or urine concentrations of endocrine disrupting chemicals (EDCs): bisphenol-A (BPA), bisphenol-S (BPS), twelve phthalate metabolites, eight perfluoroalkyl acids (PFAAs), and eleven trace elements. Fifteen (3.57%) of the women were diagnosed with GDM, and associations between the environmental chemical exposures and GDM diagnosis were examined using multiple logistic and LASSO regression analyses in single- and multi-chemical exposure models, respectively. In single chemical exposure models, BPA and mercury were associated with increased odds of GDM, while a significant inverse association was observed for zinc. Double-LASSO regression analysis selected mercury (AOR: 1.51, CI: 1.12-2.02), zinc (AOR: 0.017, CI: 0.0005-0.56), and perfluoroundecanoic acid (PFUnA), a PFAAs, (AOR: 0.43, CI: 0.19-0.94) as the best predictors of GDM. The combined data for this Canadian cohort suggest that second trimester blood mercury was a robust predictor of GDM diagnosis, whereas blood zinc and PFUnA were protective factors. Research into mechanisms that underlie the associations between mercury, zinc, PFUnA, and the development of GDM is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".