Prenatal Concentrations of Perfluoroalkyl Substances and Maternal Beta Cell Function at 7 to 9 Years of Follow-Up
Bibliographic record
Abstract
CONTEXT: Epidemiological evidence regarding prenatal per- and polyfluoroalkyl substance (PFAS) exposure and long-term maternal metabolic health outcomes is lacking. OBJECTIVE: Quantify associations between prenatal PFAS concentrations and maternal metabolic biomarkers of glucose homeostasis 7 to 9 years later. METHODS: We measured second trimester plasma concentrations of 9 PFAS in participants enrolled in the Maternal-Infant Research on Environmental Chemicals (MIREC) study. We measured individual biomarkers of glucose homeostasis (fasting intact proinsulin, C-peptide, insulin, glucose, and hemoglobin A1C levels) in samples collected 7 to 9 years after the MIREC pregnancy (n = 258) and derived indicators of pancreatic beta cell function (proinsulin to insulin [PI:INS], proinsulin to C-peptide [PI:CP] ratios) and insulin resistance (homeostatic model assessment for insulin resistance [HOMA-IR], triglyceride-glucose index). Using multivariable linear regression models, we quantified the percent change in each outcome per doubling of individual PFAS concentrations. We used quantile g-computation and weighted quantile sum regression to evaluate the mixture of PFAS. RESULTS: Prenatal perfluorononanoic acid and perfluorodecanoic acid concentrations were associated with 13.9% (95% CI: 0.8, 28.8) and 10.5% (95% CI: -1.0, 23.4) higher HOMA-IR values as well as 11.9% (95% CI: 0.1, 25.1) and 8.9% (95% CI: -1.5, 20.3) higher fasting insulin concentrations, respectively. A doubling of perfluorooctanoic acid concentrations was associated with increases in intact proinsulin concentrations (12.8% [95% CI: -3.5, 31.8]) and beta cell function ratios (PI:INS: 11.5% [95% CI: -4.4, 30.1]; PI:CP: 13.5% [95% CI: -2.4, 32.0]). CONCLUSION: Prenatal exposure to PFAS may impact long-term maternal insulin resistance and beta cell function, key risk factors for type 2 diabetes. These associations differ by specific PFAS.
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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 | 0.000 |
| 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) | 0.001 | 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".