Scientists have been working hard to find a way to accurately diagnose mood disorders like depression and bipolar disorder. One out of every four people suffer from depression worldwide, but the current methods of treatment and cure are based on a trial and error approach.
Discover our latest podcast
Alternate approach
Researchers at the Indiana University School of Medicine have found an alternate approach to diagnose mood disorders, by developing a blood test that could be useful in giving patients the precise treatments they require. They have been working on this study over four years, observing more than 300 participants. Their findings were published earlier this month, and the results are looking promising. They stated:
The development of blood tests, as well as matching of patients with existing and new treatments, in a precise, personalized and preventive fashion, would make a significant difference at an individual and societal level.
The blood test is composed of RNA biomarkers that can correctly identify the severity of one’s depression. It can alsoassess how severe it can get in the future, and whether there is a risk of developing bipolar disorder as well. Furthermore, the test will be able to determine the best medication for each individual patient.
Alexander B. Niculescu, Professor of Psychiatry at IU School of Medicine, and lead scientist of the study said:
Through this work, we wanted to develop blood tests for depression and for bipolar disorder, to distinguish between the two, and to match people to the right treatments.
Blood biomarkers are emerging as important tools in disorders where subjective self-report by an individual, or a clinical impression of a healthcare professional, is not always reliable.
Life-changing study
Niculescu has explained that this work is crucial in improving countless lives, especially considering that 25% of the global population ‘will have a clinical mood disorder episode in their lifetime.’ He added:
The need for and importance of efforts such as ours cannot be overstated.