Genetic variation in glutamatergic genes moderates the effects of childhood adversity on brain volume and IQ in treatment-resistant schizophrenia
Bibliographic record
Abstract
Childhood adversity describes a range of adverse experiences, including sexual, physical, and emotional abuse, neglect, and other adverse life events that have occurred during childhood 1 . It is associated with an elevated risk of schizophrenia 1 , 2 . Studies of individuals with childhood adversity and schizophrenia report lower intelligence quotient (IQ) scores 3 , 4 . A population twins study found that children exposed to domestic violence had lower IQs than unexposed children, regardless of underlying genetic factors 3 . Meanwhile, another study on 216 twins including monozygotic (MZ) and dizygotic (DZ) probands pairs and MZ/DZ healthy controls showed that schizophrenia polygenic risk score (PRS) and childhood trauma predict schizophrenia vulnerability 2 . Given that schizophrenia has also continuously been linked to lower IQ levels as two meta-analysis studies reported that the mean IQ scores of individuals who subsequently develop schizophrenia are lower than those of healthy comparison individuals years before the beginning of psychotic symptoms 5 , 6 , the impact of childhood abuse on cognition may constitute a step in the developmental process leading to psychosis 7 . Low IQ in patients with schizophrenia has been identified as a predictor of poor social and clinical outcomes 8 . Fundamental questions that are highly relevant to our understanding of schizophrenia, and as yet unresolved, are whether the effect of childhood adversity on IQ is influenced by brain volume or genetic variation and if so, does the effect of these factors differ between patients with and without treatment-resistant schizophrenia (TRS)?
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".