Early Life Lead Exposure and DNA Methylation: Birth through Adolescence
Bibliographic record
Abstract
Epigenetic modification is a plausible mechanism linking early life exposures to adult disease susceptibility. Evidence suggests lead (Pb) exposure during sensitive periods in child development may modify the epigenome. We assessed whether early life Pb exposures altered DNA methylation levels (globally at repetitive elements and at candidate genes) in blood samples at birth and peripuberty and evaluated epigenetic drift between these two life stages. Mothers were recruited during pregnancy and children followed for 7-15 years. Exposures were estimated at three time points: 1) prenatal (average of trimester-specific maternal blood Pb levels), 2) birth (cord blood Pb), and 3) early childhood (average of multiple child blood Pb, sampled 3-48 months). DNA was isolated and bisulfite converted from peripubertal blood leukocytes and cord blood samples (n=79). Percent methylation was quantified via bisulfite sequencing at long interspersed nuclear elements (LINE-1) and growth-related genes (HSD11B2, IGF2, H19). Multivariable linear regression was used to evaluate the influence of Pb exposures on DNA methylation and epigenetic drift (peripubertal minus cord blood DNA methylation) adjusting for sex and age (when applicable). Prenatal and/or cord blood Pb were associated with higher LINE-1 and HSD11B2 methylation at birth (p<0.05). Each µg/dL of mean blood Pb 3-48 months was associated with a 0.2% decrease in peripubertal DNA methylation of HSD11B2 (p=0.01). Hypomethylation of H19 and hypermethylation of IGF2 occurred between birth and peri-puberty over time regardless of Pb exposure (p<0.05). Epigenetic drift was significantly altered by Pb exposure at LINE-1 and HSD11B2. Results suggest early life Pb exposure modifies DNA methylation at birth and impacts epigenetic drift from birth to peri-puberty. Future work will examine DNA methylation change as a mediator of Pb-induced impacts on childhood growth.
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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".