Maternal Adverse Childhood Experiences and Infant DNA Methylation: Examining an Epigenetic Biomarker of Intergenerational Risk
Bibliographic record
Abstract
While “nature" and "nurture" are often viewed as opposing influences on human development, epigenetics is one area of study investigating how these influences work together. Until recently, transmission of intergenerational risk to human development has centred on claims of genetic inheritance, or the persistence of poor social environments such as adverse childhood experiences (ACEs), across generations. Stress occurring during gestation, that affects both the fetus and mother, has also been proposed as a method of transmitting intergenerational risk to offspring. New evidence in animal models suggests that “preconception stress” may also predict DNA methylation (DNAm; one component of epigenetics) in offspring, potentially impacting developmental health outcomes. Thus, the purpose of this study was to investigate the association between human mothers’ preconception stress and differential DNAm patterns in their biological infants. A secondary analysis was conducted, utilizing data obtained from the Fetal Programming (FetalPro) cohort study, a sub-set of participants in the Alberta Pregnancy Outcomes and Nutrition (APrON) study. APrON study participants were voluntary, and all pregnant women were over 16-years-old and before 22 weeks of gestation at enrolment. Measures included mothers’ scores on the Adverse Childhood Experiences (ACEs) questionnaire, mental health during pregnancy including the Edinburgh Postnatal Depression Scale and the Symptom Checklist 90 Revised, as well as demographics. Epigenetic data were obtained from buccal epithelial cell (BEC) samples collected from mothers’ 3-month-old infants. Cellular DNA were processed with the Illumina Infinium HumanMethylation450 Bead Chip to investigate DNAm. Relationships were investigated using regression modelling with the Limma function in R-package. Results showed a strong relationship between mothers’ total ACE score and differential DNAm patterning in their infants at eight epigenetic sites out of over 450,000 sites investigated. These findings have implications for the study of DNAm patterning as a biomarker for the transfer of preconception stress in humans and suggest a role for epigenetics in the transfer of intergenerational trauma.
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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 | 0.001 | 0.001 |
| 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".