The dynamic epigenome and its implications for behavioral interventions: a role for epigenetics to inform disorder prevention and health promotion
Bibliographic record
Abstract
The emerging field of behavioral epigenetics is producing a growing body of evidence that early life experience and social exposure can alter the way by which genes are marked with DNA methylation. We hypothesize that changes in DNA methylation as well as other epigenetic markers could generate stable phenotypes. Early life adversity appears to result in altered DNA methylation of genes in the brain and peripheral tissues, and these changes are associated with adverse phenotypic changes. Although the data are still sparse, early epigenetic studies have provided a proof of principle that experiences and the environment leave marks on genes, and thus suggest molecular and physical mechanisms for the epidemiological concept of gene-environment interaction. The main attraction of DNA methylation for type I (TI) translational prevention science is the fact that, different from genetic changes that are inherited from our ancestors, DNA methylation is potentially preventable and reversible and, therefore, there is a prospect of epigenetically targeted interventions. In addition, DNA methylation markers might provide an objective tool for assessing effects of early adverse experience on individual risks as well as providing objective measures of progress of an intervention. In spite of this great potential promise of the emerging field of social and translational epigenetics, many practical challenges remain that must be addressed before behavioral epigenetics could become translational epigenetics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".