Precision medicine for suicidality: from universality to subtypes and personalization

Abstract

Suicide remains a clear, present and increasing public health problem, despite being a potentially preventable tragedy. Its incidence is particularly high in people with overt or un(der)diagnosed psychiatric disorders. Objective and precise identification of individuals at risk, ways of monitoring response to treatments and novel preventive therapeutics need to be discovered, employed and widely deployed. We sought to investigate whether blood gene expression biomarkers for suicide (that is, a ‘liquid biopsy’ approach) can be identified that are more universal in nature, working across psychiatric diagnoses and genders, using larger cohorts than in previous studies. Such markers may reflect and/or be a proxy for the core biology of suicide. We were successful in this endeavor, using a comprehensive stepwise approach, leading to a wealth of findings. Steps 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 (SI) and for future hospitalizations for suicidality in independent cohorts, leading to the identification of completely novel predictive biomarkers (such as CLN5 and AK2), as well as reinforcement of ours and others previous findings in the field (such as SLC4A4 and SKA2). Additionally, we examined whether subtypes of suicidality can be identified based on mental state at the time of high SI and identified four potential subtypes: high anxiety, low mood, combined and non-affective (psychotic). Such subtypes may delineate groups of individuals that are more homogenous in terms of suicidality biology and behavior. We also studied a more personalized approach, by psychiatric diagnosis and gender, with a focus on bipolar males, the highest risk group. Such a personalized approach may be more sensitive to gender differences and to the impact of psychiatric co-morbidities and medications. We compared testing the universal biomarkers in everybody versus testing by subtypes versus personalized by gender and diagnosis, and show that the subtype and personalized approaches permit enhanced precision of predictions for different universal biomarkers. In particular, LHFP appears to be a strong predictor for suicidality in males with depression. We also directly examined whether biomarkers discovered using male bipolars only are better predictors in a male bipolar independent cohort than universal biomarkers and show evidence for a possible advantage of personalization. We identified completely novel biomarkers (such as SPTBN1 and C7orf73), and reinforced previously known biomarkers (such as PTEN and SAT1). For diagnostic ability testing purposes, we also examined as predictors phenotypic measures as apps (for suicide risk (CFI-S, Convergent Functional Information for Suici...

Trends in incidence and associated risk factors of suicide mortality among breast cancer patients

“…Among the best known is the Beck Depression Inventory, a self-report rating tool that contains 21 questions and has been used in a variety of settings. 37 More recently, personalized approaches to suicidality have been suggested with the description of relevant biomarkers, 38 which may assist in better risk stratification and assessment of the response to treatment. The results of this study demonstrate that screening for depression would especially be indicated during the first year after diagnosis, where the risk for committing suicide was the highest.…”

Blood biomarkers for memory: toward early detection of risk for Alzheimer disease, pharmacogenomics, and repurposed drugs

“…Microarray work was carried out using previously described methodology [7][8][9][10]. The dataset is available at GEO (GSE127711).…”

“…This was done in order to normalize and be able to easily combine internal (discovery) and external evidence (prioiritzation) scores, and to avoid over-fitting to the discovery cohort. We have developed in our lab R scripts to automate and conduct all these large dataset analyses in bulk, checked against human manual scoring [10].…”

“…Similar gene expression changes in brain and blood cells may also be due to similar gene promoter regulation in response to shared internal milieu and external environment changes [2][3][4][5][6]. Previous work by our group has identified blood gene expression biomarkers that track suicidal ideation using longitudinal within-subject designs, validated them in suicide completers, and tested them in independent cohorts for ability to assess state (suicidal ideation), and ability to predict trait (future hospitalizations for suicidality) [7][8][9][10]. Those biomarkers were also useful in pharmacogenomic and drug repurposing analyses.…”

Cytomegalovirus Seropositivity and Suicidal Behavior: A Mini-Review

“…Although suicide research has made many advances [1], suicide remains an unpredictable [2] but leading cause of death. There are many known indicators/risk factors that can alert physicians' of those at risk of suicide, including clinical (e.g., psychiatric illness, substance use, previous attempts, medical illness), demographic (e.g., male sex, older age, living alone), genetic, and psychological (e.g., unemployment, interpersonal conflict) factors.…”