Epigenetics in bipolar disorder: a critical review of the literature
Bibliographic record
Abstract
INTRODUCTION: Bipolar disorder (BD) is a chronic, disabling disease characterised by alternate mood episodes, switching through depressive and manic/hypomanic phases. Mood stabilizers, in particular lithium salts, constitute the cornerstone of the treatment in the acute phase as well as for the prevention of recurrences. The pathophysiology of BD and the mechanisms of action of mood stabilizers remain largely unknown but several pieces of evidence point to gene x environment interactions. Epigenetics, defined as the regulation of gene expression without genetic changes, could be the molecular substrate of these interactions. In this literature review, we summarize the main epigenetic findings associated with BD and response to mood stabilizers. METHODS: We searched PubMed, and Embase databases and classified the articles depending on the epigenetic mechanisms (DNA methylation, histone modifications and non-coding RNAs). RESULTS: We present the different epigenetic modifications associated with BD or with mood-stabilizers. The major reported mechanisms were DNA methylation, histone methylation and acetylation, and non-coding RNAs. Overall, the assessments are poorly harmonized and the results are more limited than in other psychiatric disorders (e.g. schizophrenia). However, the nature of BD and its treatment offer excellent opportunities for epigenetic research: clear impact of environmental factors, clinical variation between manic or depressive episodes resulting in possible identification of state and traits biomarkers, documented impact of mood-stabilizers on the epigenome. CONCLUSION: Epigenetic is a growing and promising field in BD that may shed light on its pathophysiology or be useful as biomarkers of response to mood-stabilizer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".