Pharmacogenomic predictors of citalopram treatment outcome in major depressive disorder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it