Expression of ANK3 moderates the association between childhood trauma and affective traits in severe mental disorders
Bibliographic record
Abstract
Exposure to early life trauma increases the risk of psychopathology later in life. Here we investigated if ANK3 mRNA levels influence the relationship between childhood trauma experiences and clinical characteristics in mental disorders. A sample of 174 patients with bipolar disorder and 291 patients with schizophrenia spectrum disorder were included. Patients were diagnosed using the Structured Clinical Interview for DSM-IV, and childhood trauma was assessed using the childhood trauma questionnaire. Age at illness onset and number of psychotic and affective episodes were assessed from interview and medical records. Current depressive symptoms were measured using the calgary depression scale for schizophrenia and the inventory for depressive symptomatology. ANK3 expression was analyzed in whole blood using the Illumina HumanHT-12 v4 Expression BeadChip. Analyses were carried out with the Process adjusted for confounders. Within the total sample, patients with both high ANK3 expression and with the most severe childhood sexual abuse had more manic/hypomanic episodes and an earlier age at onset of the first episode. ANK3 mRNA levels also moderated the relationship between emotional neglect and manic/hypomanic episodes. Our results suggest that ANK3 expression levels moderate the association between specific types of childhood trauma and affective traits in mental disorders.
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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) | 0.002 | 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".