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Research identifies biomarkers of suicide across psychiatric disorders

Researchers identified biomarkers of suicidal ideation across multiple psychiatric disorders and developed an application to help clinicians accurately screen for suicide risk.

“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,” Alexander B. Niculescu III, MD, PhD, of Indiana University School of Medicine, in Indianapolis, and colleagues wrote.

Alexander B. Niculescu

Researchers used a within-participant discovery approach to identify gene expression changes between no suicidal ideation and high suicidal ideation states among 217 males with bipolar disorder, major depressive disorder, schizoaffective disorder or schizophrenia. Of these participants, 37 developed high suicidal ideation during the study period. Using blood samples taken when participants were in different suicidal states, researchers identified common RNAs among this group of participants and evaluated them using the Convergent Functional Genomics approach to prioritize suicidal ideation biomarkers. Researchers then assessed these biomarkers in blood samples from 26 demographically matched individuals who completed suicide.

SLC4A4 was the best individual biomarker to predict suicidal ideation across all psychiatric diagnoses in the study with a receiver operating characteristic area under the curve (AUC) of 72%, according to researchers. SLC4A4 predicted suicidal ideation among participants with bipolar disorder with an AUC of 93% and future hospitalizations with an AUC of 70%.

Researchers then integrated the identified biomarkers into two clinical information applications, the simplified affective state scale (SASS) and Convergent Functional Information for Suicide (CFI-S), and assessed their combined accuracy in predicting suicidal ideation among all psychiatric diagnoses.

Alone, SASS had an AUC of 85% and CFI-S had an AUC of 89%.

When including biomarkers, the combined application had an AUC of 92% for all psychiatric diagnoses, 98% for bipolar disorder and 94% for future hospitalizations.

“We believe that widespread adoption of risk prediction tests based on these findings during health care assessments will enable clinicians to intervene with lifestyle changes or treatments that can save lives,” Niculescu said in a press release. “We now have developed a better panel of biomarkers that are predictive across several psychiatric diagnoses. Combined with the apps, we have a broader spectrum predictor for suicidality. In addition to reproducing and expanding our own previous work, we reproduce and expand other groups’ results in this burgeoning field.” – by Amanda Oldt

Disclosure: The study was supported by an NIH Directors' New Innovator Award (1DP2OD007363) and a VA Merit Award (2I01CX000139). Niculescu reports being listed as inventor on a patent application being filed by Indiana University. All other authors report no relevant financial disclosures.