Pharmacogenetic Association Between Glutamatergic Genes and Sri Treatment Response in Obsessive Compulsive Disorder
Bibliographic record
Abstract
Introduction Pharmacogenetic studies in obsessive-compulsive disorder (OCD) primarily focussing on serotonergic and dopaminergic polymorphisms, provided inconsistent findings. There is recent evidence for glutamatergic abnormalities in OCD. Aims Examine the association glutamatergic genes with serotonin reuptake inhibitor (SRI) response in OCD. Objectives To study pharmacogenetic association between SLC1A1 and GRIN2B polymorphisms with SRI response in OCD. Methods DSM-IV OCD patients were recruited from a specialty OCD clinic and evaluated using the Yale-Brown obsessive compulsive scale (YBOCS), Mini International Neuropsychiatric Interview (MINI) plus, Clinical Global Impression scale (CGI). They were subsequently reassessed with YBOCS and CGI. To study extreme phenotypes, we included only full responders (> 35% YBOCS improvement and CGI-I score of 1 or 2) to any SRI (n = 191) and non-responders (< 25% YBOCS improvement and CGI-I score ≥ 4) to adequate trial of at least two SRIs (n = 84). Partial responders were excluded. Genotyping was performed using an ABI9700 PCR machine. Results Genotype frequencies did not deviate significantly from the values predicted by the Hardy-Weinberg equation. Case-control association analyses revealed no significant association between genotype/allele frequencies with SRI response. Conclusion Our data does not show any association between polymorphisms in glutamatergic genes and SRI response in OCD though such associations have been found in other studies. More SNP's in the same gene could be responsible for the pharmacogenetic associations. More homogenous sample considering symptom dimensions and other phenotypic variables may be needed. It may be critical to go beyond “usual suspect” candidate gene research. In this regard, a novel approach to identify SRI response biomarkers is the use of cellular models. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".