Pregnant women’s cognitive appraisal of a natural disaster affects DNA methylation in their children 13 years later: Project Ice Storm
Bibliographic record
Abstract
Prenatal maternal stress (PNMS) can impact a variety of outcomes in the offspring throughout childhood and persisting into adulthood as shown in human and animal studies. Many of the effects of PNMS on offspring outcomes likely reflect the effects of epigenetic changes, such as DNA methylation, to the fetal genome. However, no animal or human research can determine the extent to which the effects of PNMS on DNA methylation in human offspring is the result of the objective severity of the stressor to the pregnant mother, or her negative appraisal of the stressor or her resulting degree of negative stress. We examined the genome-wide DNA methylation profile in T cells from 34 adolescents whose mothers had rated the 1998 Québec ice storm's consequences as positive or negative (that is, cognitive appraisal). The methylation levels of 2872 CGs differed significantly between adolescents in the positive and negative maternal cognitive appraisal groups. These CGs are affiliated with 1564 different genes and with 408 different biological pathways, which are prominently featured in immune function. Importantly, there was a significant overlap in the differentially methylated CGs or genes and biological pathways that are associated with cognitive appraisal and those associated with objective PNMS as we reported previously. Our study suggests that pregnant women's cognitive appraisals of an independent stressor may have widespread effects on DNA methylation across the entire genome of their unborn children, detectable during adolescence. Therefore, cognitive appraisals could be an important predictor variable to explore in PNMS research.
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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 | 0.001 | 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".