Maternal concentrations of perfluoroalkyl sulfonates and alterations in white matter microstructure in the developing brains of young children
Bibliographic record
Abstract
BACKGROUND: Maternal exposure to per- and polyfluoroalkyl substances (PFAS) has been linked to child neurodevelopmental difficulties. Neuroimaging research has linked these neurodevelopmental difficulties to white matter microstructure alterations, but the effects of PFAS on children's white matter microstructure remains unclear. We investigated associations between maternal blood concentrations of six common perfluoroalkyl sulfonates and white matter alterations in young children using longitudinal neuroimaging data. METHODS: This study included 84 maternal-child pairs from a Canadian pregnancy cohort. Maternal second trimester blood concentrations of perfluorohexanesulfonate (PFHxS) and five perfluorooctane sulfonate (PFOS) isomers were quantified. Children underwent magnetic resonance imaging scans between ages two and six (279 scans total). Adjusted linear mixed models investigated associations between each exposure and white matter fractional anisotropy (FA) and mean diffusivity (MD). RESULTS: Higher maternal concentrations of perfluoroalkyl sulfonates were associated with higher MD and lower FA in the body and splenium of the corpus callosum of young children. Multiple sex-specific associations were found. In males, PFHxS was negatively associated with FA in the superior longitudinal fasciculus, while PFOS isomers were positively associated with MD in the inferior longitudinal fasciculus (ILF). In females, PFOS isomers were positively associated with FA in the pyramidal fibers and MD in the fornix, but negatively associated with MD in the ILF. CONCLUSION: Maternal exposure to perfluoroalkyl sulfonates may alter sex-specific white matter development in young children, potentially contributing to neurodevelopmental difficulties. Larger studies are needed to replicate these findings and examine the neurotoxicity of these chemicals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".