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Record W4408962752 · doi:10.1177/07067437251329073

Polygenic Risk Score Analysis of Antidepressant Treatment Outcomes: A CAN-BIND-1 Study Report: Analyse des résultats du traitement antidépresseur à l’aide des scores de risque polygéniques : Rapport sur l’étude CAN-BIND-1

2025· article· en· W4408962752 on OpenAlexafffundvenueabout
Leen Magarbeh, Samar S. M. Elsheikh, Farhana Islam, Victoria Marshe, Xiaoyu Men, Emytis Tavakoli, Martin Kronenbuerger, Stefan Kloiber, Benício N. Frey, Roumen Milev, Claudio N. Soares, Sagar V. Parikh, Franca Placenza, Stefanie Hassel, Valerie H. Taylor, Francesco Leri, Pierre Blier, Rudolf Uher, Faranak Farzan, Raymond W. Lam, Gustavo Turecki, Jane A. Foster, Susan Rotzinger, Sidney H. Kennedy, Daniel J. Müller

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsSt. Michael's HospitalDouglas Mental Health University InstituteUniversity of British ColumbiaMcGill UniversitySimon Fraser UniversitySt. Joseph’s Healthcare HamiltonRoyal Ottawa Mental Health CentreUniversity of CalgaryMcMaster UniversityQueen's UniversityCentre for Addiction and Mental HealthUniversity of GuelphDalhousie UniversityUniversity Health NetworkUniversity of Toronto
FundersCanadian Institutes of Health ResearchLundbeck CanadaOntario Brain InstituteServier Canada
KeywordsPolygenic risk scorePsychologyGynecologyAntidepressantMedicinePsychiatryBiologyGeneticsAnxiety

Abstract

fetched live from OpenAlex

Objective The genetic architecture of antidepressant response is poorly understood. This study investigated whether polygenic risk scores (PRSs) for major psychiatric disorders and a personality trait (neuroticism) are associated with antidepressant treatment outcomes. Methods We analysed 148 participants with major depressive disorder (MDD) from the Canadian Biomarker Integration Network for Depression-1 (CAN-BIND-1) cohort. Participants initially received escitalopram (ESC) monotherapy for 8 weeks. Nonresponders at week 8 received augmentation with aripiprazole (ARI), while responders continued ESC until week 16. Primary outcomes were remission status and symptom improvement measured at weeks 8 and 16. At week 16, post-hoc stratified analyses were performed by treatment arm (ESC-only vs. ESC + ARI). Eleven PRSs derived from genome-wide association studies of psychiatric disorders (e.g., MDD and post-traumatic stress syndrome (PTSD)) and neuroticism, were analysed for associations with these outcomes using logistic and linear regression models. Results At week 8, a higher PRS for PTSD was nominally associated with a lower probability of remission (odds ratio (OR) = 0.08 [0.014–0.42], empirical p -value = 0.017) and reduced symptom improvement (beta (standard error) = −29.15 (9.76), empirical p -value = 0.019). Similarly, a higher PRS for MDD was nominally associated with decreased remission probability (OR = 0.38 [0.18–0.78], empirical p -value = 0.044). However, none of the results survived multiple testing corrections. At week 16, the stratified analysis for the ESC-only group revealed that a higher PRS for MDD was associated with increased remission probability (empirical p- value = 0.034) and greater symptom improvement (empirical p -value = 0.02). In contrast, higher PRSs for schizophrenia (empirical p -value = 0.013) and attention-deficit hyperactivity disorder (empirical p -value = 0.032) were associated with lower symptom improvement. No significant associations were observed in the ESC + ARI group. Conclusions These findings suggest that PRSs may influence treatment outcomes, particularly in ESC monotherapy. Replication in larger studies is needed to validate these observations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.037
GPT teacher head0.306
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes4
Has abstractyes

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