Applying epigenetic science to the understanding of eating disorders: a promising paradigm for research and practice
Bibliographic record
Abstract
PURPOSE OF REVIEW: Studies indicate that environmental factors, acting at various moments throughout the life cycle, can result in epigenetically mediated alterations in gene expression. In this article, we review recent findings on the role of epigenetic factors in eating disorders, address methodological issues that need to be considered when interpreting research findings, and comment on possible clinical applications. RECENT FINDINGS: Evidence suggests that eating disorders implicate alterations of methylation in genes involved in the mental status, metabolism, anthropometric features and immunity. Furthermore, some research in individuals with anorexia nervosa suggests the presence of reversible, malnutrition-induced epigenetic alterations that 'reset' as patients recover. SUMMARY: Epigenetic studies in the eating disorders corroborate the idea that eating disorder cause is multifactorial, and identify markers that could help inform our understanding of illness staging and subtyping that may explain the commonly progressive course of these disorders, and that may provide insights towards the development of novel interventions. Already, there is evidence to suggest that, in people with eating disorders, epigenetically informed interventions help reduce stigma and shame, and increase self-acceptance and hopes of recovery. Although findings are intriguing, further research is required as, to date, studies apply modest sample sizes and disparate methodologies.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".