Association Between Side Effects and Blood microRNA Expression Levels and Their Targeted Pathways in Patients With Major Depressive Disorder Treated by a Selective Serotonin Reuptake Inhibitor, Escitalopram: A CAN-BIND-1 Report
Bibliographic record
Abstract
INTRODUCTION: Antidepressant drugs are effective therapies for major depressive disorder; however, they are frequently associated with side effects. Although there is some evidence for a relationship between genetic variation and side effects, little is known regarding the role of dynamic molecular factors as moderators of side effects. The aim of this study was to assess microRNA (miRNA) changes associated with side effects during escitalopram treatment and their downstream effects on target gene expression. METHODS: A total 160 patients with major depressive disorder from the CAN-BIND-1 cohort were included. Side effects were assessed with the Toronto Side Effect Scale after 2 weeks of treatment with escitalopram. We assessed the relationship between side effects and changes in peripheral expression of miRNAs between baseline and week 2. For miRNA whose expression changed, we used target prediction algorithms to identify putative messenger RNA (mRNA) targets and assessed their expression. RESULTS: Nausea was experienced by 42.5% of patients. We identified 45 miRNAs whose expression changed on initiation of escitalopram treatment, of which 10 displayed a negative association with intensity of nausea (miR15b-5p, miR17-5p, miR20a-5p, miR20b-5p, miR103a-3p, miR103b, miR106a-5p, miR182-5p, miR185-5p, and miR660-5p). Additionally, we found negative associations between 4 microRNAs (miR20a-5p, miR106a-5p, miR185-5p, miR660-5p) and mRNA targets. The expression of the miR185-5p target, CAMK2δ was significantly decreased [log 2 mean = -0.048 (0.233)] between weeks 0 and 2 (P = .01)]. CONCLUSIONS: We identified an overexpression of miR185-5p during escitalopram treatment of major depressive disorder, which was negatively associated with intensity of nausea, and identified a potential mRNA target that may mediate this effect.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".