Intergenerational Transmission of Well Being–Genetic and Epigenetic Mechanisms
Bibliographic record
Abstract
Introduction Maternal mental well being influences offspring development. Research suggests that an interplay between genetic and environmental factors underlies this familial transmission of mental disorders. Objectives To explore an interaction between genetic and environmental factors to predict trajectories of maternal mental well being, and to examine whether these trajectories are associated with epigenetic modifications in mothers and their offspring. Method We assessed maternal childhood trauma and rearing experiences, prenatal and postnatal symptoms of depression and stress experience from 6 to 72 months postpartum, and genetic and epigenetic variation in a longitudinal birth-cohort study (n = 262) (Maternal adversity, vulnerability and neurodevelopment project). We used latent class modeling to describe trajectories in maternal depressive symptoms, parenting stress, marital stress and general stress, taking polygenetic risk for major depressive disorder (MDD), a composite score for maternal early life adversities, and prenatal depressive symptoms into account. Results Genetic risk for MDD associated with trajectories of maternal well being in the postpartum, conditional on the experience of early life adversities and prenatal symptoms of depression. We will explore whether these trajectories are also linked to DNA methylation patterns in mothers and their offspring. Preliminary analyses suggest that maternal early life adversities associate with offspring DNA methylation age estimates, which is mediated through maternal mental well being and maternal DNA methylation age estimates. Conclusion We found relevant gene-environment interactions associated with trajectories of maternal well being. Our findings inform research on mechanisms underlying familial transmission of vulnerability for psychopathology and might thus be relevant to prevention and early intervention programs. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".