Gene expression biomarkers of response to citalopram treatment 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
There is significant variability in antidepressant treatment outcome, with ∼30-40% of patients with major depressive disorder (MDD) not presenting with adequate response even following several trials. To identify potential biomarkers of response, we investigated peripheral gene expression patterns of response to antidepressant treatment in MDD. We did this using Affymetrix HG-U133 Plus2 microarrays in blood samples, from untreated individuals with MDD (N=63) ascertained at a community outpatient clinic, pre and post 8-week treatment with citalopram, and used a regression model to assess the impact of gene expression differences on antidepressant response. We carried out technical validation of significant probesets by quantitative reverse transcriptase PCR and conducted central nervous system follow-up of the most significant result in post-mortem brain samples from 15 subjects who died during a current MDD episode and 11 sudden-death controls. A total of 32 probesets were differentially expressed according to response to citalopram treatment following false discovery rate correction. Interferon regulatory factor 7 (IRF7) was the most significant differentially expressed gene and its expression was upregulated by citalopram treatment in individuals who responded to treatment. We found these results to be concordant with our observation of decreased expression of IRF7 in the prefrontal cortex of MDDs with negative toxicological evidence for antidepressant treatment at the time of death. These findings point to IRF7 as a gene of interest in studies investigating genomic factors associated with 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) | 0.000 | 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) | 0.000 | 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