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Prenatal Exposure to Fine Particulate Matter Components and Autism Risk in Childhood

2025· article· en· W4415478385 on OpenAlexafffundabout
Chengchun Yu, Robert Talarico, Steven Hawken, Hong Chen, Scott Weichenthal, Sabit Cakmak, Chris Hebbern, Anna Gunz, Aaron van Donkelaar, Randall V. Martin, Jean-Nicolas Côté, Éric Lavigne

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversité de SherbrookeChildren’s Health Research InstituteWestern UniversityMcGill UniversityPublic Health OntarioUniversity of TorontoHealth CanadaOttawa HospitalUniversity of Ottawa
FundersUniversity of TorontoSchulich School of Medicine and Dentistry, Western UniversityUniversité de SherbrookeOttawa Hospital Research InstituteHealth CanadaChildren's Health Research InstituteU.S. Department of EnergyMcGill UniversitySchulich School of Medicine and DentistryUniversity of Ottawa
KeywordsPrenatal exposureAutismPregnancyParticulatesCohort studyCohortPrenatal alcohol exposureRisk assessmentAir pollutants

Abstract

fetched live from OpenAlex

Importance: Prenatal and early-life exposure to fine particulate matter (PM2.5) has been associated with autism spectrum disorder (ASD), but the role of individual components and timing of exposure remains unclear. Objective: To examine associations between prenatal and first-year-of-life exposure to PM2.5 components and ASD diagnosis, and identify potentially sensitive periods during pregnancy. Design, Setting, and Participants: This cohort study conducted in Ontario, Canada, used administrative health data covering approximately 98% of births in the province. The cohort included singleton live births from Ontario hospitals between April 1, 2002, and December 31, 2022, with gestational age 36 to 42 weeks, birth weight 500 to 6800 g, maternal age 15 to 55 years, complete residential history, and provincial health insurance coverage. At least 18 months of follow-up was required for postnatal exposure analyses. Exposures: Biweekly concentrations of PM2.5 components (black carbon, dust, ammonium, nitrate, organic matter, sulfate, sea salt) and weekly NO2 and O3 from conception to age 36 weeks. Prenatal models were adjusted for postnatal exposure using annual averages. Pollutant levels were estimated using satellite data, chemical transport models, and ground-based measurements. Main Outcomes and Measures: ASD diagnosis by age 5 years. Cox proportional hazards models were used to assess associations, and distributed lag nonlinear models identified sensitive exposure windows. Results: Among 2 183 324 births (mean [SD] maternal age, 30.5 [5.4] years; mean [SD] gestational age at birth, 39.2 [1.1] weeks; 1 152 040 female infants [48.9%]), prenatal exposure to PM2.5, sulfate (SO42-), and ammonium (NH4+), and postnatal exposure to ozone (O3), were associated with ASD. Hazard ratios (HRs) per 1-IQR increase for SO42- were 1.15 (95% CI, 1.06-1.25) and for NH4+ was 1.12 (95% CI, 1.01-1.23). PM2.5 mass excluding SO42- and NH4+ during their respective critical windows was not associated with ASD (HR, 1.04; 95% CI, 0.92-1.19). O3 exposure during weeks 26 to 30 (HR, 1.03; 95% CI, 1.00-1.05) and over the first year (HR, 1.09; 95% CI, 1.01-1.17) was also associated with ASD. Conclusions and Relevance: In this large cohort study, prenatal exposure to specific PM2.5 components and postnatal O3 exposure were associated with ASD risk. The second and third trimesters may represent sensitive exposure windows. These findings support further research on air pollution's role in ASD etiology.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.281
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.

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

Citations3
Published2025
Admission routes3
Has abstractyes

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