Global DNA methylation in suicidal ideation and suicide attempt in schizophrenia
Bibliographic record
Abstract
Suicidal behavior is influenced by many risk factors such as childhood trauma, stressful life events, genetic factors, and severe mental illnesses. Suicidal ideation is present in 50% of schizophrenia patients and is associated with an elevated risk of suicide attempt. Studies have shown that epigenetic mechanisms are associated with suicidal behavior in schizophrenia. Although several studies have suggested the importance of epigenetic factors in suicidal ideation and behavior, no studies have investigated global methylation in association with these two phenotypes. This study investigated global methylation level/change in association with current and emergent suicidal ideation and also with suicide attempt. Forty-seven schizophrenia patients were assessed for the association between global methylation and suicide attempt, and a subsample of these patients (n = 27) was assessed for current suicidal ideation. Afterwards, we performed a longitudinal analysis in which global methylation changes during a 3-month follow-up were compared between patients with and without emergent suicidal ideation. This methylation analysis did not find evidence for a significant association between global methylation and suicidal ideation or suicide attempt. To date, there are no robust biomarkers predicting suicidal ideation or behavior in psychotic patients. This study is the first to investigate global methylation in predicting suicidal ideation and behavior. Although we did not find evidence for an association between global methylation and these phenotypes, our findings may offer novel insights into the molecular mechanisms linked to suicide. Future investigation may measure global methylation in association with suicidal ideation or behavior in larger samples.
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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) | 0.001 | 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".