Major depressive disorder (MDD) is estimated to affect 6.7% of the adult population in the United States every year (Lepine & Briley, 2011). The number of people who suffer from major depressive disorder is expected to increase as our youth continues to age (Hidaka, 2011). According to the World Health Organization, depressive disorders have been identified as the number one cause of disability in the world (Ferrari et al., 2013). This further supports the responsibility throughout the medical community to diagnose depression accurately and provide appropriate interventions quickly.
As with other psychiatric diseases, we do not yet fully understand the neurobiological mechanisms of major depressive disorder. Perhaps the most well known theory for the neurobiological underpinnings of depression is the monoamine hypothesis. This hypothesis holds that serotonin and norepinephrine, or the lack there of, can explain the pathophysiology of major depressive disorder. The most widely used antidepressants target this deficiency. Despite such medications causing immediate increases in monoamines in neuronal synapses, it often takes ten to fourteen days before mood is therapeutically affected (Hindmarch, 2001). Similarly, studies have complicated this theory by demonstrating that differences in monoamine levels cannot effectively distinguish patients with major depressive disorder and healthy controls (Higgins & George, 2013). As such, we know that the mechanisms underlying depression are more complicated than any one group of neurotransmitters can explain.
Just as our understanding of the neurobiological mechanisms of major depressive disorder is not complete, the etiology of MDD remains complicated and relatively unclear. The interplay of an individual’s genetic makeup and the experiences he or she encounters is often the culprit for the later development of mental illness. This also holds true for depression. However, the exact genes that are responsible for major depressive disorder have eluded discovery for quite some time.
In September, researchers at Northwestern University published their research claiming to have identified a blood test that could be used to diagnose MDD based on components of an individual’s genetic material. Currently, a major depressive disorder diagnosis is based on a clinical interview. In this interview, a patient’s presentation is dissected based on symptoms and whether certain diagnostic criteria are met according to the DSM. Understandably, there is some degree of subjectivity in this process. A serum blood test would provide medical professionals with the ability to make this diagnosis objectively.
The objective diagnosis of MDD is based on the identification of specific serum transcriptomic biomarkers in patients suffering from major depressive disorder. The blood test distinguishes depression based on the levels of nine RNA markers – ADCY3, DGKA, FAM461, IGSF4A/CADM1, KIAA1539, MARCKS, PSME1, RAPH1, and TLR7.
RNA, or ribonucleic acid, is a macromolecule that serves many functions for the cell. Perhaps most notably, it copies and transfers the genetic code needed for the synthesis of proteins. These proteins, in turn, are responsible for various catalytic and structural processes that every cell depends on (Clancy, 2008). For this reason, proteins are often recognized as “the workhorses” of the cell (e.g., Lodish et al., 2000). However, it is our genetic code that determines which and how many proteins are produced.
The researchers at Northwestern were also able to utilize the RNA markers of MDD patients as a means to measure the efficacy of cognitive behavioral therapy (CBT). In the study, the researchers measured the levels of the RNA markers after 18 weeks of CBT. Using certain markers, they were able to distinguish patients for whom therapy was effective (i.e. patients that were no longer depressed) from those whose depression persisted despite cognitive behavioral therapy. Patients with depression that remitted at the conclusion of CBT displayed differences in the levels of ASAH1, ATP11C, and KIAA1539 as compared to those patients whose depression did not remit. Thus, the levels of certain RNA markers could provide objective evidence for the efficacy of this intervention. That is, levels of these markers should change if patients with MDD respond to CBT therapeutically.
Beyond following the trends of certain markers to determine if CBT is effective, specific baseline characteristics of several markers could be used to predict who would have therapeutic responses to cognitive behavioral therapy in the first place. The researchers found that the co-expression patterns of certain transcripts differed significantly at baseline between patients who were no longer depressed and patients who were still depressed following CBT. In utilizing these markers, medical professionals could identify appropriate treatment regimens for each individual and anticipate whether such interventions would be effective.
Despite certain RNA markers changing in response to CBT, some transcript level differences persisted between patients with MDD and controls irrespective of whether patients with MDD were currently depressed or not. These differences were found in the levels of RAPH1, KIAA1539, and DGKA. As such, these RNA markers were both highly specific and sensitive for patients with MDD and those who never had MDD. It would be these markers, presumably, that could be objectively relied on to aid medical professionals in their capacity to diagnose.
Because of how distressing and disabling life with MDD can be, it is vitally important for the medical community to arrive at such a diagnosis with accuracy and confidence. The researchers at Northwestern may have found a method that ensures exactly that. According to Eva Redei, the leading author on the paper, “this clearly indicates that you can have a blood-based laboratory test for depression, providing a scientific diagnosis in the same way someone is diagnosed with high blood pressure or high cholesterol” (Paul, 2014). As our ability to diagnose depression increases, our understanding of its etiology and neurobiological mechanisms will plausibly expand. Likewise, as the diagnosing of mental illness becomes more “scientific,” we can hope, from a global perspective, that it might help to combat the long-standing stigma that often surrounds it.
Clancy, S. (2008). RNA functions. Nature Education, 1(1): 102. Retrieved from http://www.nature.com/scitable/topicpage/rna-functions-352
Ferrari, A.J., Charlson, F.J., Norman, R.E., Patten, S.B., Freedman, G., Murray, C.J., Vos, T., & Whiteford, H.A. (2013). Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Medicine, 10(11). doi: 10.1371/journal.pmed.1001547
Hidaka, B.H. (2012). Depression as a disease of modernity: explanations for increasing prevalence. Journal of Affective Disorders, 140(3), 205-214. doi: http://dx.doi.org/10.1016/j.jad.2011.12.036
Higgins, E.S. & Georfe, M.S. (2013). The neuroscience of clinical psychiatry: The pathophysiology of behavior and mental illness. Philadelphia, PA: Lippincott Williams & Wilkins.
Hindmarch, I. (2001). Expanding the horizons of depression: beyond the monoamine hypothesis. Human Psychopharmacology: Clinical and Experimental, 16(3), 203-218. doi: 10.1002/hup.288
Lepine, J.P., & Briley, M. (2011). The increasing burden of depression. Neuropsychiatric Disease and Treatment, 7, 3-7. doi: 10.2147/NDT.S19617
Lodish, H., Berk, A., Zipursky, S.L., Matsudaira, P., Baltimore, D., & Darnell, J. (2000). Molecular cell biology. New York: W.H. Freeman.
Paul, M. (2014, September 16). First blood test to diagnose depression in adults. Retrieved from http://www.northwestern.edu/newscenter/stories/2014/09/first-blood-test-to-diagnose-depression-in-adults.html
Redei, E.E., Andrus, B.M., Kwasny, M.J., Seok, J., Cai, X., Ho, J., & Mohr, D.C. (2014). Blood transcriptomic biomarkers in adult primary care patients with major depressive disorder undergoing cognitive behavioral therapy. Translational Psychiatry, 4, doi: 10.1038/tp.2014.66