SA66 - PRECISION MEDICINE FOR SUICIDALITY: FROM UNIVERSALITY TO SUBTYPES AND PERSONALIZATION

Abstract

Background Objective and quantitative markers would permit better and more precise assessment, tracking, and prediction of suicidal risk, which would enable preventive therapeutic interventions. Previous work by our group has identified blood biomarkers and phenotypic predictors for suicide risk in men, and separately in women, showing some gender similarities as well as differences. An essential question remained to be answered, of high relevance for developing this area of research and carrying it to full clinical applicability: would a quest for more universal predictors or a quest for more personalized predictors be more productive? We endeavored to answer this question through our current work. Methods First, we sought to investigate whether blood gene expression biomarkers can be identified that are more universal in nature, working across psychiatric diagnoses and genders, starting with a powerful longitudinal within-participant design, and using larger cohorts than in previous studies. Second, we identified subtypes of suicidality based on mental state (anxiety, mood, psychosis) at the time of high suicidal ideation. Third, we used a more personalized approach, by gender and diagnosis, with a focus on the highest clinical risk group, male bipolars. We examined the ability of the candidate biomarkers to predict suicidal ideation and future hospitalizations for suicidality, in completely independent cohorts. We also used the lists of top biomarkers we identified as a window into the biology of suicidality, by conducting biological pathways and network analyses. Additionally, we leveraged these lists for therapeutics and drug discovery purposes. Results We were successful in this endeavor, using a comprehensive stepwise approach, leading to a wealth of findings. Step 1, 2 and 3 were discovery, prioritization, and validation for tracking suicidality, resulting in a top dozen list of candidate biomarkers comprising the top biomarkers from each step, as well as a larger list of 148 candidate biomarkers that survived Bonferroni correction in the validation step. Step 4 was testing the top dozen list and Bonferroni biomarker list for predictive ability for suicidal ideation and for future hospitalizations for suicidality in independent cohorts, leading to the identification of completely novel predictive biomarkers, as well as reinforcement of ours and others previous findings in the field. Discussion The biomarkers we identified also provide a window towards understanding the biology of suicide, implicating biological pathways related to neurogenesis, programmed cell death, and insulin signaling from the universal biomarkers, as well as mTOR signaling from the male bipolar biomarkers. Finally, based on the totality of our data and of the evidence in the field to date, a convergent functional evidence score prioritizing biomarkers that have all around evidence (track suicidality, predict it, are reflective of biological predisposition, and are potential drug targets) brings to the fore genes that suggest an inflammatory/accelerated aging component, which may be a targetable common denominator.