Prenatal exposure to perfluoroalkyl substances and inflammatory biomarker concentrations
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants that induce immunotoxicity in experimental studies; however, epidemiological evidence-particularly during pregnancy-is scarce. We quantified associations between first trimester plasma perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and perfluorohexane sulfonate (PFHxS) concentrations and third trimester concentrations of inflammatory biomarkers and determined if these associations were modified by fetal sex. Methods: We analyzed data from 1411 participants, recruited between 2008 and 2011, in the Maternal-Infant Research on Environmental Chemicals study. Our primary outcome was a composite inflammatory index derived by summing the z-scores of eight proinflammatory biomarkers. Using multivariable linear regression models, we quantified associations between each PFAS and the inflammatory index and individual biomarkers. We quantified the effects of the PFAS mixture using weighted quantile sum regression, and evaluated effect modification using product terms and sex-stratified models. Results: Each doubling of PFOA and PFHxS was associated with a 0.38 (95% CI, 0.09, 0.67) and 0.21 (95% CI, 0.01, 0.41) SD increase in the proinflammatory index, respectively. A one-quartile increase in the PFAS mixture was associated with a 0.40 (95% CI, 0.09, 0.71) SD increase in the proinflammatory index. In individual models, we observed positive associations between PFAS and concentrations of monocyte chemoattractant protein-1, macrophage inflammatory protein-1β, and matrix metalloproteinases-9; however, the magnitude and precision varied according to the specific PFAS. Sex-specific findings were identified in few PFAS-biomarker associations. Conclusions: PFOA, PFOS, and PFHxS, individually and as a mixture, were positively associated with proinflammatory biomarkers 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 | 0.005 |
| 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".