Maternal psychosocial risk factors and offspring gestational epigenetic age acceleration in a South African birth cohort study
Bibliographic record
Abstract
Epigenetic age (EA) acceleration is associated with higher risk of chronic disease and mortality in adults. However, little is known about whether and how in utero exposures might shape gestational EA acceleration at birth. We aimed to explore associations between maternal psychosocial risk factors and offspring gestational EA acceleration at birth in a South African birth cohort study - the Drakenstein Child Health Study. Maternal psychosocial risk factors included trauma/stressor exposure; posttraumatic stress disorder (PTSD); depression, psychological distress; and alcohol/tobacco use. Offspring gestational EA acceleration at birth was calculated using an epigenetic clock previously devised for neonates. Bivariate linear regression was used to explore unadjusted associations between maternal risk factors and offspring gestational EA acceleration at birth. A stepwise regression method was then used to determine the best multivariable model for adjusted associations. Data from 272 maternal-offspring dyads were included in the current analysis. In the stepwise regression model, maternal trauma exposure (β = 7.92; p<0.01) or PTSD (β = 7.46; p<0.01) were significantly associated with offspring gestational EA acceleration at birth, controlling for ethnicity, offspring sex, head circumference at birth, maternal HIV status, and prenatal tobacco or alcohol use. In site-stratified models, these associations retained statistical significance and direction of effect. Maternal trauma exposure or PTSD may thus be associated with offspring gestational EA acceleration at birth. Given the novelty of this preliminary finding, and its potential translational relevance, further studies to delineate underlying biological pathways and to explore clinical implications of EA acceleration are warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".