Genetic risk of suicidal behavior in bipolar spectrum disorder: analysis of 737 pedigrees
Bibliographic record
Abstract
OBJECTIVES: Suicide is a significant cause of mortality in patients with major affective disorders (MAD), and suicidal behavior and MAD co-aggregate in families. However, the transmission of suicidal behavior is partially independent from that of MAD. We analyzed the lifetime prevalence of completed and attempted suicides in a large sample of families with bipolar disorder (BD), its relation to family history of MAD and BD, and the contribution of clinical and treatment factors to the risk of suicidal behavior. METHODS: We studied 737 families of probands with MAD with 4919 first-degree relatives (818 affected, 3948 unaffected, and 153 subjects with no information available). Lifetime psychiatric diagnoses and suicidal behavior in first-degree relatives were assessed using semi-structured interviews, family history methods, and reviews of clinical records. Cox proportional hazard and logistic regression models were used to investigate the role of clinical covariates in the risk of suicidal behavior, and in the prevalence of MAD and BD. RESULTS: The estimated lifetime prevalence of suicidal behavior (attempted and completed suicides) in 737 probands was 38.4 ± 3.0%. Lithium treatment decreased suicide risk in probands (p = 0.007). In first-degree relatives, a family history of suicidal behavior contributed significantly to the joint risk of MAD and suicidal behavior (p = 0.0006). CONCLUSIONS: The liability to suicidal behavior is influenced by genetic factors (particularly family history of suicidal behavior and MAD). Even in the presence of high genetic risk for suicidal behavior, lithium treatment decreases suicide rates significantly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".