Paternal adverse childhood experiences: Associations with infant DNA methylation
Bibliographic record
Abstract
Adverse childhood experiences (ACEs), or cumulative childhood stress exposures, such as abuse, neglect, and household dysfunction, predict later health problems in both the exposed individuals and their offspring. One potential explanation suggests exposure to early adversity predicts epigenetic modification, especially DNA methylation (DNAm), linked to later health. Stress experienced preconception by mothers may associate with DNAm in the next generation. We hypothesized that fathers' exposure to ACEs also associates with their offspring DNAm, which, to our knowledge, has not been previously explored. An epigenome-wide association study (EWAS) of blood DNAm (n = 45) from 3-month-old infants was regressed onto fathers' retrospective ACEs at multiple Cytosine-phosphate-Guanosine (CpG) sites to discover associations. This accounted for infants' sex, age, ethnicity, cell type proportion, and genetic variability. Higher ACE scores associated with methylation values at eight CpGs. Post-hoc analysis found no contribution of paternal education, income, marital status, and parental postpartum depression, but did with paternal smoking and BMI along with infant sleep latency. These same CpGs also contributed to the association between paternal ACEs and offspring attention problems at 3 years. Collectively, these findings suggested there were biological associations with paternal early life adversity and offspring DNAm in infancy, potentially affecting offspring later childhood outcomes.
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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".