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Record W4415018798 · doi:10.1016/j.lanplh.2025.101309

Prenatal exposure to perfluoroalkyl substances predicts multimodal brain structural and functional outcomes in children aged 5 years: a birth cohort study

2025· article· en· W4415018798 on OpenAlexaff
Aaron Barron, Alex M. Dickens, Jetro J. Tuulari, Tuulia Hyötyläinen, Susanna Kortesluoma, Harri Merisaari, Elmo P. Pulli, Eero Silver, Venla Kumpulainen, Anni Copeland, Ekaterina Saukko, John D. Lewis, Linnéa Karlsson, Matej Orešič, Hasse Karlsson

Bibliographic record

VenueThe Lancet Planetary Health · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsMental Health Research CanadaHospital for Sick Children
FundersStrategic Research CouncilHORIZON EUROPE Framework ProgrammeOrionin TutkimussäätiöEmil Aaltosen SäätiöAcademy of FinlandVarsinais-Suomen SairaanhoitopiiriJuho Vainion SäätiöFinska LäkaresällskapetSigrid Juséliuksen SäätiöSigne ja Ane Gyllenbergin SäätiöWaterloo FoundationJalmari ja Rauha Ahokkaan SäätiöYrjö Jahnssonin SäätiöAlfred Kordelinin SäätiöSuomen KulttuurirahastoSuomen Lääketieteen Säätiö
KeywordsPrenatal exposureCohort studyCohortPregnancyPrenatal alcohol exposureYoung adultMEDLINE

Abstract

fetched live from OpenAlex

Background Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are ubiquitous persistent organic pollutants associated with adverse health outcomes in humans. Although they are associated with autism spectrum disorder and behavioural outcomes, whether PFAS affect brain development is unclear. We aimed to characterise the relationship between maternal PFAS and brain structure and function in typically developing children. Methods This study was set within the FinnBrain Birth Cohort Study, a prospective observational study that enrolled mothers from three clinics in Turku, Finland, during their first trimester of pregnancy. Maternal serum samples at gestational week 24 were analysed for PFAS by mass spectrometry and, at age 5 years, children were assessed through structural, diffusion-weighted, and functional MRI. Whole-brain voxel-level and vertex-level maps of grey matter volume, white matter fractional anisotropy and mean diffusivity, and cortical thickness and surface area were combined to compute ten independent components. Data were analysed by correlation network, elastic net regression, and multivariate linear regression with multiple testing correction. Findings Pregnant mothers were enrolled into the birth cohort study between Dec 1, 2011, and April 30, 2015, and study visits at age 5 years took place between Oct 1, 2017, and March 31, 2020. This analysis involved 51 mother–child dyads for whom maternal PFAS concentrations and structural MRI data for the child were available. PFAS concentrations in maternal serum samples were mostly 0–1 ng/mL. Maternal perfluorononanoic acid (PFNA; R 2 =0·13, β=0·39 [95% CI 0·09–0·69], p adj =0·024) and linear perfluorooctanoic acid (PFOA; 0·13, 0·36 [0·09–0·63], p adj =0·022) linearly predicted a multimodal component dominated by corpus callosal integrity, whereas branched perfluorohexanesulphonic acid (PFHxS; R 2 =0·12, β=0·31, p adj =0·036) and branched PFOA ( R 2 =0·14, β=0·36, p adj =0·016) predicted a component comprising mainly occipital cortex volume and surface area. Branched perfluorooctanesulphonic acid predicted hypothalamic microstructure ( R 2 =0·10, β=0·29, p=0·026). PFNA, linear PFOA, and branched PFOA are associated with increased functional connectivity in the right precentral gyrus, whereas branched PFHxS predicts decreased connectivity in the intracalcerine cortices. Associations were not influenced by sex assigned at birth, but were related to PFAS chemical structure. Interpretation We show an association between prenatal PFAS exposure and brain developmental outcomes in children. These findings are pertinent given the ubiquitous circulation of PFAS in humans and the extreme environmental persistence of these substances. Funding The Horizon Europe programme of the EU.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.278
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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