Adolescent epigenetic profiles and environmental exposures from early life through peri-adolescence
Bibliographic record
Abstract
Epigenetic perturbations induced by environmental exposures at susceptible lifestages contribute to disease development. Even so, the influence of early life and ongoing exposures on the adolescent epigenome is rarely examined. We examined the association of exposure biomarkers for lead (Pb), bisphenol A (BPA), and nine phthalates metabolites with blood leukocyte DNA methylation at LINE-1 repetitive elements and environmentally responsive genes ( IGF2 , H19 , and HSD11B2 ) in peri-adolescents. Participants ( n = 247) from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) birth cohorts were followed-up once between the ages of 8 and 14 years, and concurrent exposures were measured in biospecimen collected at that time (blood Pb, urinary BPA, and phthalate metabolites). Prenatal and childhood exposures to Pb were previously approximated using maternal and child samples. BPA and phthalate metabolites were measured in third trimester maternal urine samples. Significant associations ( P < 0.05) were observed between DNA methylation and exposure biomarkers that were gene and biomarker specific. For example, Pb was only associated with LINE-1 hypomethylation during pregnancy ( P = 0.04), while early childhood Pb was instead associated with H19 hypermethylation ( P = 0.04). Concurrent urinary mono (2-ethylhexyl) phthalate (MEHP) was associated with HSD11B2 hypermethylation ( P = 0.005). Sex-specific associations, particularly among males, were also observed. In addition to single exposure models, principal component analysis was employed to examine exposure mixtures. This method largely corroborated the findings of the single exposure models. This study along with others in the field suggests that environment-epigenetic relationships vary by chemical, exposure timing, and sex.
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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) | 0.001 | 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".