Biological embedding in mental health: An epigenomic perspective
Bibliographic record
Abstract
Human epidemiological studies and studies of animal models provide many examples by which early life experiences influence health in a long-term manner, a concept known as biological embedding. Such experiences can have profound impacts during periods of high plasticity in prenatal and early postnatal life. Epigenetic mechanisms influence gene function in the absence of changes in gene sequence. In contrast to the relative stability of gene sequences, epigenetic mechanisms appear, at least to some extent, responsive to environmental signals. To date, a few examples appear to clearly link early social experiences to epigenetic changes in pathways relevant for mental health in adulthood. Our recent work using high-throughput epigenomic techniques points to large-scale changes in gene pathways in addition to candidate genes involved in the response to psychosocial stress and neuroplasticity. Elucidation of which pathways are epigenetically labile under what conditions will enable a more complete understanding of how the epigenome can mediate environmental interactions with the genome that are relevant for mental health. In this mini-review, we provide examples of nascent research into the influence of early life experience on mental health outcomes, discuss evidence of epigenetic mechanisms that may underlie these effects, and describe challenges for research in this area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".