Neurodevelopmental and genetic determinants of exposure to adversity among youth at risk for mental illness
Bibliographic record
Abstract
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) and lower cognitive ability have been linked with increased likelihood of exposure to adversity. We hypothesized that these associations may be partly due to genetic factors. METHODS: We calculated polygenic scores for ADHD and intelligence and assessed psychopathology and general cognitive ability in a sample of 297 youth aged 5-27 years enriched for offspring of parents with mood and psychotic disorders. We calculated an adversity score as a mean of 10 indicators, including socio-economic disadvantage, childhood maltreatment and bullying. We tested the effects of polygenic scores, externalizing symptoms and IQ on adversity scores using mixed-effects linear regression. RESULTS: Externalizing symptoms and general cognitive ability showed expected positive and negative relationships with adversity, respectively. Polygenic scores for intelligence were unrelated to adversity, but polygenic scores for ADHD were associated with adversity (β = 0.23, 95% CI 0.13 to 0.34, p < .0001). ADHD polygenic scores uniquely explained 4.0% of variance in adversity score. The relationship between polygenic scores for ADHD and adversity was independently significant among individuals with (β = 0.49, 95% CI 0.25 to 0.75, p < .0001) and without (β = 0.14, 95% CI 0.02 to 0.26, p = .022) ADHD. CONCLUSIONS: A genetic score indexing liability to ADHD was associated with exposure to adversity in early life. Previously observed associations between externalizing symptoms, lower cognitive ability and adversity may be partially attributed to genetic liability to ADHD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".