Association Between Polygenic Risk and Symptom Severity Change After Cognitive Behavioral Therapy for Obsessive‐Compulsive Disorder
Bibliographic record
Abstract
ABSTRACT A large proportion of patients undergoing cognitive behavior therapy (CBT) for obsessive‐compulsive disorder (OCD) do not respond sufficiently to treatment. Identifying predictors for change in symptom severity after treatment could inform clinical decision‐making, allow for better‐tailored interventions, and avoid treatment failure. Prior research on predictors for treatment response has, however, yielded inconsistent findings with limited clinical utility. Here, we investigated the predictive power of nine polygenic risk scores (PRSs) for psychiatric and cognitive traits in 1598 OCD patients (1167 adults and 431 children/adolescents) treated with CBT in Sweden and Norway. We fitted linear mixed models adjusted for age, sex, genotyping batch, and the first five ancestry PCs to estimate associations between PRS and symptom severity change from pre‐ to post‐treatment. The PRS for schizophrenia showed a modestly significant association with symptom change (β = 0.013, p = 0.04, R 2 = 0.10), indicating that a higher PRS for schizophrenia was associated with a smaller decrease in symptom severity. No other PRS were significantly associated with the outcome. While these results await replication and expansion, current PRS for psychiatric and cognitive phenotypes do not seem to contribute meaningfully to symptom severity change in CBT for OCD.
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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 | 0.000 | 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".