Predicting Worsening Suicidal Ideation With Clinical Features and Peripheral Expression of Messenger RNA and MicroRNA During Antidepressant Treatment
Bibliographic record
Abstract
OBJECTIVE: To investigate how the combination of clinical and molecular biomarkers can predict worsening of suicidal ideation during antidepressant treatment. METHODS: Samples were obtained from 237 patients with major depressive disorder (DSM-IV criteria) treated with either duloxetine or placebo in an 8-week randomized controlled trial. Data were collected between 2007 and 2011. The relationship between treatment-worsening suicidal ideation (TWSI) and a number of clinical variables, as well as peripheral expression of messenger RNA (mRNA) and microRNA (miRNA), was assessed at baseline. We generated 4 predictive models for TWSI: clinical, mRNA, miRNA, and a combined model comprising the best predictive variables from clinical, mRNA, and miRNA data. RESULTS: Eleven patients (9.8%) presented with TWSI in the duloxetine group. Among the clinical variables, only baseline depressive severity was found to be mildly predictive of TWSI. Two mRNAs (stathmin 1 [STMN1] and protein phosphatase 1 regulatory subunit 9B [PPP1R9B]) and 2 miRNAs (miR-3688 and miR-5695) were identified that were significantly predictive of TWSI when mRNA and miRNA were assessed separately (P = .002, .044, .004, and .005, respectively). The best model included baseline depression severity and expression of STMN1 and miR-5695 and predicted TWSI with area under the curve = 0.94 (P < .001). Additionally, the combined model did not significantly predict TWSI in the placebo group. CONCLUSIONS: This study generated a predictive tool for TWSI that combines both biological and clinical variables. These biological variables can be easily quantified in peripheral tissues, thus rendering them viable targets to be used in both clinical practice and future studies of suicidal behaviors. TRIAL REGISTRATION: ClinicalTrials.gov identifiers: NCT00635219, NCT00599911, and NCT01140906.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".