Peripheral serotonin transporter DNA methylation is linked to increased salience network connectivity in females with anorexia nervosa
Bibliographic record
Abstract
Background: Epigenetic variation in the serotonin transporter gene (SLC6A4) has been shown to modulate the functioning of brain circuitry associated with the salience network and may heighten the risk for mental illness. This study is, to our knowledge, the first to test this epigenome–brain–behaviour pathway in patients with anorexia nervosa. Methods: We obtained resting-state functional connectivity (rsFC) data and blood samples from 55 acutely underweight female patients with anorexia nervosa and 55 age-matched female healthy controls. We decomposed imaging data using independent component analysis. We used bisulfite pyrosequencing to analyze blood DNA methylation within the promoter region of SLC6A4. We then explored salience network rsFC patterns in the group × methylation interaction. Results: We identified a positive relationship between SLC6A4 methylation levels and rsFC between the dorsolateral prefrontal cortex and the salience network in patients with anorexia nervosa compared to healthy controls. Increased rsFC in the salience network mediated the link between SLC6A4 methylation and eating disorder symptoms in patients with anorexia nervosa. We confirmed findings of rsFC alterations for CpG-specific methylation at a locus with evidence of methylation correspondence between brain and blood tissue. Limitations: This study was cross-sectional in nature, the sample size was modest for the method and methylation levels were measured peripherally, so findings cannot be fully generalized to brain tissue. Conclusion: This study sheds light on the neurobiological process of how epigenetic variation in the SLC6A4 gene may relate to rsFC in the salience network that is linked to psychopathology in anorexia nervosa.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".