Pharmacogenomic predictors of citalopram treatment outcome in major depressive disorder
Bibliographic record
Abstract
OBJECTIVES: A significant proportion of patients with major depressive disorder (MDD) do not improve following treatment with first-line antidepressants and, currently, there are no objective indicators of predictors of antidepressant response. The aim of this study was to investigate pre-treatment peripheral gene expression differences between future remitters and non-responders to citalopram treatment and identify potential pharmacogenomic predictors of response. METHODS: We conducted a gene expression study using Affymetrix HG-U133 Plus2 microarrays in peripheral blood samples from untreated individuals with MDD (N = 77), ascertained at a community outpatient clinic, prior to an 8-week treatment with citalopram. Gene expression differences were assessed between remitters and non-responders to treatment. Technical validation of significant probesets was carried out by qRT-PCR. RESULTS: A total of 434 probesets displayed significant correlation to change in score and 33 probesests were differentially expressed between eventual remitters and non-responders. Probesets for SMAD 7 (SMA- and MAD-related protein 7) and SIGLECP3 (sialic acid-binding immunoglobulin-like lectin, pseudogene 3) were the most significant differentially expressed genes following FDR correction, and both were down-regulated in individuals who responded to treatment. CONCLUSIONS: These findings point to SMAD7 and SIGLECP3 as candidate predictive biomarkers of antidepressant response.
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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) | 0.001 | 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".