Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach

Abstract

Worldwide, one person dies every 40 seconds by suicide, a potentially preventable tragedy. A limiting step in our ability to intervene is the lack of objective, reliable predictors. We have previously provided proof of principle for the use of blood gene expression biomarkers to predict future hospitalizations due to suicidality, in male bipolar disorder participants. We now generalize the discovery, prioritization, validation, and testing of such markers across major psychiatric disorders (bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) in male participants, to understand commonalities and differences. We used a powerful within-participant discovery approach to identify genes that change in expression between no suicidal ideation and high suicidal ideation states (n=37 participants out of a cohort of 217 psychiatric participants followed longitudinally). We then used a convergent functional genomics (CFG) approach with existing prior evidence in the field to prioritize the candidate biomarkers identified in the discovery step. Next, we validated the top biomarkers from the prioritization step for relevance to suicidal behavior, in a demographically matched cohort of suicide completers from the coroner's office (n=26). The biomarkers for suicidal ideation only are enriched for genes involved in neuronal connectivity and schizophrenia, the biomarkers also validated for suicidal behavior are enriched for genes involved in neuronal activity and mood. The 76 biomarkers that survived Bonferroni correction after validation for suicidal behavior map to biological pathways involved in immune and inflammatory response, mTOR signaling and growth factor regulation. mTOR signaling is necessary for the effects of the rapid-acting antidepressant agent ketamine, providing a novel biological rationale for its possible use in treating acute suicidality. Similarly, MAOB, a target of antidepressant inhibitors, was one of the increased biomarkers for suicidality. We also identified other potential therapeutic targets or biomarkers for drugs known to mitigate suicidality, such as omega-3 fatty acids, lithium and clozapine. Overall, 14% of the top candidate biomarkers also had evidence for involvement in psychological stress response, and 19% for involvement in programmed cell death/cellular suicide (apoptosis). It may be that in the face of adversity (stress), death mechanisms are turned on at a cellular (apoptosis) and organismal level. Finally, we tested the top increased and decreased biomarkers from the discovery for suicidal ideation (CADM1, CLIP4, DTNA, KIF2C), prioritization with CFG for prior evidence (SAT1, SKA2, SLC4A4), and validation for behavior in suicide completers (IL6, MBP, JUN, KLHDC3) steps in a completely independent test cohort of psychiatric participants for prediction of suicidal ideation (n=108), and in a future follow-up cohort of psychiatric participants (n=157) for prediction of psychiatric hospitalizations due to suicidality. The best individual biomarker across ps...

EPIGENETIC VARIATION AT SKA2 PREDICTS SUICIDE PHENOTYPES AND INTERNALIZING PSYCHOPATHOLOGY

“…This study was the first to examine relationships between SKA2 and suicidal behavior in a military cohort and, consistent with research in civilian samples, [6-8] found that variation at the SKA2 locus identified in previous research (CpG locus cg13989295) was associated with suicidal thoughts and behaviors. The specificity of the SKA2 association with current, but not lifetime, suicide phenotypes suggests methylation at this locus may index dynamic processes related to ongoing suicidal behavior and speaks to its potential utility for measuring suicide risk.…”

“…In two subsequent studies, Niculescu and colleagues found decreased SKA2 expression levels in blood in suicide decedents compared to controls [7] and SKA2 expression levels prospectively predicted suicidal ideation in psychiatric patients. [8] …”

Discovery and replication of a peripheral tissue DNA methylation biosignature to augment a suicide prediction model

“…Notably, some studies demonstrate that state level stress can be influenced by trait level anxiety [10]. Model predictive accuracies vary between ~70 and 85 % in various cohorts and are consistent with SKA2 gene expression-based prediction accuracies reported by other groups [8, 11]. The statistical interaction with stress is likely related to the physiological role SKA2 plays in mediating HPA axis activity.…”

Genomewide association studies of suicide attempts in US soldiers

“…This attention to risk factors has extended to genetic and other biological factors [Oquendo et al 2016; Sudol and Mann 2017; Turecki 2014], with the goal of developing a bio-signature for suicide [Oquendo et al 2014]. Recent reports of a combined genetic-epigenetic risk marker for suicidality in SKA2 [Guintivano et al 2014; Kaminsky et al 2015] are of considerable interest, and await further replication in population-based samples, as do other multivariate approaches that incorporate genomic and clinical risk factors [Niculescu et al 2015b]. Although twin studies have documented genetic influences on suicide-related phenotypes (heritability ~30% – 55%) [Tidemalm et al 2011], it is clear that suicidality is a multi-determined, genetically complex trait, as is the case for virtually all mental and behavioral disorders [Gelernter 2015].…”