The authors of a previous study on the use of blood gene expression biomarkers to predict future hospitalizations due to suicidality recently published results from their research to identify genes that change in expression between no suicidal ideation (SI) and high SI states.
It’s hard to imagine an outcome worse than the suicide of a psychiatric patient. Although there are often many clinical and nonclinical signposts along the way, the reality is that most often, a suicide attempt lacks objective, reliable predictors. The authors of a study in Molecular Psychiatry, who have previously provided research on blood gene expression biomarkers to predict future hospitalizations due to suicidality, have now analyzed testing of such markers across major psychiatric disorders to understand commonalities and differences.
The researchers used a powerful within-participant discovery approach to identify genes that change in expression between no suicidal ideation (SI) and high SI states (n=37 participants out of a cohort of 217 psychiatric participants followed longitudinally). They 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.
The post-mortem cohort of the study, in which the top biomarker findings were validated, consisted of a demographically matched cohort of 26 male violent suicide completers obtained through the coroner’s office. The cases selected had completed suicide by means other than overdose, which could affect gene expression. “Markers involved in behavior may be on a continuum with some of the markers involved in ideation, varying in the degree of expression changes from less severe (ideation) to more severe (behavior),” the study authors noted. “One cannot have suicidal behavior without SI, but it may be possible to have SI without suicidal behavior.”
As part of the study, the researchers describe a novel, simple and comprehensive phenomic (clinical) risk assessment scale, the CFI-S scale, as well as a companion app to it for use by clinicians and individuals. CFI-S was developed independently of any data from this study, by integrating known risk factors for suicide from the clinical literature. They then compared the CFI-S in live participants with ideation versus suicide completers, and identified which items are most different (such as inability to cope with stress, which is consistent with biological data from the biomarker side of our study). They examined whether the biomarkers are able to predict trait (future hospitalizations for suicidal behavior) in psychiatric participants (n=157) in the short term (first year of follow-up) as well as overall (all data for future hospitalizations available for each patient).
The analysis identified a series of genes that may be involved in suicidality, including some novel candidates, such as CADM1, KIF2C, DTNA, and CLIP4, which had not previously been associated with suicidality.
“Biomarkers that survive such a rigorous stepwise discovery, prioritization, validation and testing process are likely directly relevant to the disorder studied,” the study authors note. “As such, we endeavored to study their biology, whether they are involved in other psychiatric disorders or are relatively specific for suicide, and whether they are the modulated by existing drugs in general, and drugs known to treat suicidality in particular. We have identified a series of biomarkers that seem to be changed in opposite direction in suicide versus in treatments with omega-3 fatty acids, lithium, clozapine or MAOIs. These biomarkers could potentially be used to stratify patients to different treatment approaches, and monitor their response.”
The researchers acknowledge a number of limitations, including the need to study biological factors in women and the fact that it remains to be seen how suicidality predictors apply to non-psychiatric participants. But they note that the work, which identifies key behavioral and biological mechanisms related to inflammation, mTOR signaling, growth factors, and stress response, could lead to risk prediction tests becoming part of routine or targeted healthcare assessments, allowing for earlier intervention in patients most likely to consider suicide.