Contribution of genes and environment to the longitudinal association between childhood impulsive‐aggression and suicidality in adolescence
Bibliographic record
Abstract
BACKGROUND: Population-based and family studies showed that impulsive-aggression predicts suicidality; however, the underlying etiological nature of this association is poorly understood. The objective was to determine the contribution of genes and environment to the association between childhood impulsive-aggression and serious suicidal ideation/attempt in young adulthood. METHODS: N = 862 twins (435 families) from the Quebec Newborn Twin Study were followed up from birth to 20 years. Repeated measures of teacher-assessed impulsive-aggression were modeled using a genetically informed latent growth model including intercept and slope parameters reflecting individual differences in the baseline level (age 6 years) and in the change (increase/decrease) of impulsive-aggression during childhood (6 to 12 years), respectively. Lifetime suicidality (serious suicidal ideation/attempt) was self-reported at 20 years. Associations of impulsive-aggression intercept and slope with suicidality were decomposed into additive genetic (A) and unique environmental (E) components. RESULTS: Additive genetic factors accounted for an important part of individual differences in impulsive-aggression intercept (A = 90%, E = 10%) and slope (A = 65%, E = 35%). Genetic (50%) and unique environmental (50%) factors equally contributed to suicidality. We found that 38% of the genetic factors accounting for suicidality were shared with those underlying impulsive-aggression slope, whereas 40% of the environmental factors accounting for suicidality were shared with those associated with impulsive-aggression intercept. The genetic correlation between impulsive-aggression slope and suicidality was 0.60, p = .027. CONCLUSIONS: Genetic and unique environmental factors underlying suicidality significantly overlap with those underlying childhood impulsive-aggression. Future studies should identify putative genetic and environmental factors to inform prevention.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".