Lifetime Postnatal Exposure to Perfluoroalkyl Substance Mixture and DNA Methylation at Twelve Years of Age
Bibliographic record
Abstract
Per- and polyfluoroalkyl substance (PFAS) exposure has been linked to DNA methylation changes in neonates and adults. We previously reported that prenatal PFAS exposure may have a durable impact on DNA methylation from birth to adolescence. However, few studies have examined the association of postnatal PFAS exposure with alterations in DNA methylation. We examined the associations of lifetime postnatal PFAS mixture exposure with leukocyte DNA methylation in 154 adolescents from the HOME Study (2003-2006; Cincinnati, Ohio). Lifetime postnatal PFAS mixture exposure was estimated using latent profile analysis of four PFAS concentrations measured at birth, and ages 3, 8, and 12 years. We measured DNA methylation in peripheral leukocytes at 12 years using the Illumina HumanMethylation EPIC BeadChip. We estimated covariate-adjusted associations between postnatal PFAS mixture concentrations and DNA methylation measures using linear regression, and used KEGG enrichment analysis to identify molecular pathways. Four significant differentially methylated positions were observed in the higher vs. lower PFAS profile (FDR p-value <0.05). These PFAS-associated CpG sites annotated to gene regions related to various cancers, cognition, and cardiometabolic health. We identified 17 pathways (FDR p-value <0.05), which indicates possible mechanism linking PFAS exposure to several health effects.
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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 | 0.000 | 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) | 0.002 | 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".