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Record W3193284955 · doi:10.1289/isee.2021.o-to-141

Longitudinal analysis of DNA methylation in relation to gestational perfluoroalkyl substance exposure: An epigenome-wide association study

2021· article· en· W3193284955 on OpenAlexaff
Yun Liu, Melissa Eliot, George D. Papandonatos, Karl T. Kelsey, Scott M. Langevin, Jessie P. Buckley, Aimin Chen, Bruce P. Lanphear, Kim M. Cecil, Kimberly Yolton, Joseph M. Braun

Bibliographic record

VenueISEE Conference Abstracts · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPerfluorooctaneDNA methylationMethylationOffspringEpigenomeDifferentially methylated regionsCord bloodGestational ageGestationInternal medicineMedicineBiologyPhysiologyPregnancyAndrologyGeneticsEndocrinologyGeneChemistryGene expression

Abstract

fetched live from OpenAlex

BACKGROUND AND AIM: Alterations to DNA methylation may underlie the association between gestational exposure to perfluoroalkyl substances (PFAS) and adverse health outcomes in children. However, few studies have examined the association between gestational PFAS exposure and DNA methylation, and no studies have repeated DNA methylation data across childhood. We examined associations between gestational PFAS exposure and repeated measures of offspring peripheral leukocyte DNA methylation among 266 mother-child pairs enrolled in the HOME Study (Cincinnati, OH). METHODS: We quantified serum concentrations of perfluorooctanoate (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoate (PFNA), and perfluorohexane sulfonate (PFHxS) in mothers at ~16 weeks gestation. We measured DNA methylation at delivery (cord blood) and age 12 years using the Illumina HumanMethylation EPIC BeadChip. We analyzed the associations between log2-transformed PFAS concentrations and repeated DNA methylation measures using generalized estimating equations. Visit by PFAS interaction terms were used to test the stability of these differences across time. We performed Gene Ontology (GO) enrichment analysis to identify significant biological pathways. RESULTS:A total of 35 loci were significantly associated with PFAS (false discovery rate, q 0.05). Among the 5 loci for PFOS, 10 for PFOA, 7 for PFHxS, and 13 for PFNA (q 0.01), none overlapped. These loci mapped to genes (e.g., AGAP1, HPSE2, HABP2, RNF13, RADIL, and TMEM56) that are associated with cancers, cardiovascular disease, and cognitive function. We found little evidence that associations between PFAS and DNA methylation changed over time. The most significant GO pathways included homophilic cell adhesion, cell-cell adhesion and integral component of plasma membrane. CONCLUSIONS:Using longitudinal data, we identified loci associated with PFAS that have not been reported. Several loci were in genes linked to PFAS-associated health outcomes. Future studies are needed to confirm our findings and examine whether DNA methylation mediates associations between gestational PFAS exposure and offspring health. KEYWORDS: PFAS, Epigenomics, Biomarkers of exposure, Environmental epidemiology

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.311
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 source (direct Gemma or distilled Codex), 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

Citations1
Published2021
Admission routes1
Has abstractyes

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