DNA methylation mediates the impact of exposure to prenatal maternal stress on BMI and central adiposity in children at age 13½ years: Project Ice Storm
Bibliographic record
Abstract
Prenatal maternal stress (PNMS) in animals and humans predicts obesity and metabolic dysfunction in the offspring. Epigenetic modification of gene function is considered one possible mechanism by which PNMS results in poor outcomes in offspring. Our goal was to determine the role of maternal objective exposure and subjective distress on child BMI and central adiposity at 13½ years of age, and to test the hypothesis that DNA methylation mediates the effect of PNMS on growth. Mothers were pregnant during the January 1998 Quebec ice storm. We assessed their objective exposure and subjective distress in June 1998. At age 13½ their children were weighed and measured (n = 66); a subsample provided blood samples for epigenetic studies (n = 31). Objective and subjective PNMS correlated with central adiposity (waist-to-height ratio); only objective PNMS predicted body mass index (BMI). Bootstrapping analyses showed that the methylation level of genes from established Type-1 and -2 diabetes mellitus pathways showed significant mediation of the effect of objective PNMS on both central adiposity and BMI. However, the negative mediating effects indicate that, although greater objective PNMS predicts greater BMI and adiposity, this effect is dampened by the effects of objective PNMS on DNA methylation, suggesting a protective role of the selected genes from Type-1 and -2 diabetes mellitus pathways. We provide data supporting that DNA methylation is a potential mechanism involved in the long-term adaptation and programming of the genome in response to early adverse environmental factors.
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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".