Genome-wide epigenetic signatures of childhood adversity in early life: Opportunities and challenges
Bibliographic record
Abstract
Maternal adversity and fetal glucocorticoid exposure has long-term effects on cardiovascular, metabolic and behavioral systems in offspring that can persist throughout the lifespan. These data, along with other environmental exposure data, implicate epigenetic modifications as potential mechanisms for long-term effects of maternal exposures on adverse health outcomes in offspring. Advances in microarray, sequencing and bioinformatic approaches have enabled recent studies to examine the genome-wide epigenetic response to maternal adversity. Studies of maternal exposures to xenobiotics such as arsenic and smoking have been performed at birth to examine fetal epigenomic signatures in cord blood relating to adult health outcomes. However, there have been no epigenomic studies examining these effects in animal models. On the other hand, to date, only a few studies of the effects of maternal psychosocial stress have been performed in human infants, and the majority of animal studies have examined epigenomic outcomes in adulthood. In terms of maternal exposure to excess glucocorticoids by synthetic glucocorticoid treatment, there has been no epigenetic study performed in humans and only a few studies undertaken in animal models. This review emphasizes the importance of examining biomarkers of exposure to adversity throughout development to identify individuals at risk and to target interventions. Thus, research performed at birth will be reviewed. In addition, potential subject characteristics associated with epigenetic modifications, technical considerations, the selection of target tissues and combining human studies with animal models will be discussed in relation to the design of experiments in this field of study.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".