August 31, 2015

Biomarkers and Questionnaires Predict Suicide Risk

At a Glance

  • Researchers identified several genes in blood whose activity is related to suicidal thoughts and actions in men with psychiatric disorders.
  • The genetic findings, combined with app-based questionnaires, may help clinicians predict which patients are likely to attempt suicide.
Man looking out a window. Researchers have been seeking a way to objectively measure a person’s risk for suicide. KatarzynaBialasiewicz/iStock/Thinkstock.

More than 41,000 Americans commit suicide each year. That’s more than twice the number killed annually by homicide. Most people who end their own lives have a mental disorder such as depression, schizophrenia, or bipolar disorder.

Efforts to reduce suicides have focused on identifying and treating those at risk. However, asking people if they’re suicidal isn’t always a reliable approach. Finding a way to objectively measure a person’s risk for suicide is thus an important area of research. Some researchers are developing questionnaires that measure the likelihood of someone committing suicide. Others are looking for biological markers of people who are suicidal.

A study led by researchers at Indiana University School of Medicine combined these approaches. Their work was funded by an NIH Director’s New Innovator Award and the U.S. Department of Veterans Affairs. Results appeared online in Molecular Psychiatry on August 18, 2015.

The researchers studied 217 male psychiatric patients at the Indianapolis VA Medical Center during multiple visits several months apart. The scientists measured the men’s thoughts of suicide through extensive interviews and took blood samples. They identified 37 patients whose thoughts of suicide increased between visits. Those patients’ blood samples were analyzed to find genes with changes in activity, or expression, between visits. Those genes were ranked based on prior research linking them to suicide risk. The researchers then measured the expression of these top-ranked genes in blood samples from 26 men who had committed suicide.

The team also developed 2 apps that use questionnaires to measure risk factors for suicide. The first collects details on a patients’ emotional state. The second asks about factors known to influence suicide risk, such as life events, stress, and mental health. Both could predict thoughts of suicide more than 85% of the time. These questionnaires were then combined with the most predictive gene biomarkers to create a universal predictive measure called UP-Suicide.

The team tested UP-Suicide in a separate group of 108 psychiatric patients to examine its ability to predict thoughts of suicide and a group of 157 patients to examine ability to predict future hospitalizations. The tool predicted which patients would go on to have serious suicidal thoughts with 92% accuracy. It also predicted with 71% accuracy which patients would be hospitalized for suicidal behaviors in the year following testing. The tool was even more accurate for patients with bipolar disorder, with 98% and 94% accuracy, respectively.

“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,” says lead researcher Dr. Alexander B. Niculescu.

Because the team studied only male psychiatric patients, further research will be needed to understand how well this approach can predict suicidal thoughts and behaviors in other populations, such as women and those who aren’t psychiatric patients.

— by Brandon Levy

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Reference: Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach. Niculescu AB, Levey DF, Phalen PL, Le-Niculescu H, Dainton HD, Jain N, Belanger E, James A, George S, Weber H, Graham DL, Schweitzer R, Ladd TB, Learman R, Niculescu EM, Vanipenta NP, Khan FN, Mullen J, Shankar G, Cook S, Humbert C, Ballew A, Yard M, Gelbart T, Shekhar A, Schork NJ, Kurian SM, Sandusky GE, Salomon DR. Mol Psychiatry. 2015 Aug 18. doi: 10.1038/mp.2015.112. [Epub ahead of print]. PMID: 26283638.

Funding: NIH Director's New Innovator Award and the U.S. Department of Veterans Affairs.