Biomarkers May Predict Suicide Risk During Antidepressant Treatment

Messenger RNA and MicroRNA can be used as viable predictors of suicidal behavior.

A combination of clinical and molecular biomarkers may be used to predict which patients are more likely to experience increased suicidal ideation during antidepressant treatment, according to study results published in the Journal of Clinical Psychiatry.

The study included data collected between 2007 and 2011 from 237 patients (age 18 to 75 years; 69.6% women) with major depressive disorder (MDD) who received either 60 mg duloxetine (n=112) or placebo (n=125). At baseline (before treatment), investigators examined the relationship between treatment-worsening suicidal ideation (TWSI) and clinical and biologic variables. TWSI was defined as an increase of at least 1 point on the Montgomery-Asberg Depression Rating Scale (MADRS) item 10 at any time during follow-up. Item 10 addresses suicidal thoughts, with 0 indicating enjoyment of life and 6 indicating explicit plans or active preparation for suicide.

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Investigators assessed suicidal ideation at baseline and again at 5 time points over an 8-week period using item 10 from the MADRS. They also assessed depression severity using the total score of all items on the MADRS at each visit, anxiety severity using the Hamilton Anxiety Rating Scale, and familial psychiatric history using a questionnaire.

At baseline, investigators analyzed the peripheral expression of messenger RNA (mRNA) and microRNA (miRNA) using whole blood samples. Then they created 4 predictive models for TWSI: clinical, mRNA, miRNA, and combined (the best predictive variables from clinical, mRNA, and miRNA data).

In patients who were treated with duloxetine, 9.8% presented with TWSI. After assessing all clinical variables, only baseline depression severity was predictive of TWSI. At baseline, 2 mRNAs were significantly predictive of TWSI: stathmin 1 (STMN1; P =.002) and protein phosphatase 1 regulatory subunit 9B (PPP1R9B; P =.044). This was also true for 2 miRNAs: miR-3688 (P =.004) and miR-5695 (P =.005). The best combination for predicting TWSI was baseline depression severity and expression of STMN1 and miR-5695 (area under the curve, 0.94; P <.001). In patients who received placebo, the combined model did not significantly predict TWSI.

Results should be interpreted with caution, given the small sample size and the low percentage of TWSI in the sample, which decreased the ability to detect biomarkers.

“In summary, we report a predictive tool for TWSI during antidepressant treatment that combines both biological and clinical variables,” the investigators wrote. “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.”


Belzeaux R, Fiori LM, Lopez JP, et al. Predicting worsening suicidal ideation with clinical features and peripheral expression of messenger RNA and MicroRNA during antidepressant treatment. J Clin Psychiatry. 2019;80(3).