Prenatal Exposure to Per- and Polyfluoroalkyl Substances and ASD-Related Symptoms in Early Childhood: Mediation Role of Steroids
Bibliographic record
Abstract
Previous studies regarding the associations between perfluoroalkyl and polyfluoroalkyl substances (PFAS) and autism spectrum disorder (ASD) have yielded inconsistent results, with the underlying mechanisms remaining unknown. In this study, we quantified 13 PFAS in cord serum samples from 396 neonates and followed the children at age 4 to assess ASD-related symptoms. Our findings revealed associations between certain PFAS and ASD-related symptoms, with a doubling of perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), and perfluoroundecanoic acid (PFUnDA) concentrations associated with respective increases of 1.79, 1.62, and 1.45 units in language-related symptoms and PFDA exhibiting an association with higher score of sensory stimuli. Nonlinear associations were observed in the associations of 6:2 chlorinated polyfluorinated ether sulfonate (Cl-PFAES) and 8:2 Cl-PFAES with ASD-related symptoms. Employing weighted quantile sum (WQS) regression, we observed significant mixture effects of multiple PFAS on all domains of ASD-related symptoms, with PFNA emerging as the most substantial contributor. Assuming causality, we found that 39-40% of the estimated effect of long-chain PFAS (PFUnDA and PFDoDA) exposure on sensory stimuli was mediated by androstenedione. This study provides novel epidemiological data about prenatal PFAS mixture exposure and ASD-related symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".