Legacy, alternative, and precursor PFAS and associations with lipids and liver function biomarkers: results from a cross-sectional analysis of adult females in the MIREC-ENDO study
Bibliographic record
Abstract
BACKGROUND: Legacy per- and polyfluoroalkyl substances (PFAS) can promote dyslipidemia; however, evidence is lacking for alternative and precursor PFAS. We investigated associations between serum concentrations of 31 PFAS and concurrently measured lipids and liver function biomarkers. METHODS: PFAS, lipids, and liver function biomarkers were analyzed in serum samples provided by 282 adult females participating in a 2018-2021 follow-up study of a Canadian pregnancy cohort. We examined percent differences in outcomes continuously for 17 PFAS with >50% detection and as detected vs. not detected for 14 PFAS with 10-50% detection. We also examined associations with the sum of 7 PFAS recommended by the National Academies of Sciences, Engineering, and Medicine guidance report on PFAS testing and 17 PFAS. We used weighted quantile sum (WQS) and quantile g-computation models to estimate joint associations. RESULTS: Each two-fold increase in concentrations of PFHxS, PFOS, PFNA, PFDA, PFHpS, and Σ7PFAS were associated with up to 7% higher total and LDL cholesterol and the TC:HDL ratio. Individuals with detectable concentrations of N-EtFOSA, N-MeFOSA, PFBS, and 9Cl-PF3ONS had up to 17% higher total and LDL cholesterol and TC:HDL. Each one-quartile increase in the mixture of 7 PFAS was associated with up to 10% higher total and LDL cholesterol. Adding additional PFAS to the mixture (17 PFAS) made estimates less precise in WQS models and attenuated associations to the null in quantile g-computation models. CONCLUSION: Alternative and precursor PFAS, including replacements for legacy PFAS, are associated with higher cholesterol levels; prospective studies are required to confirm these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".