Almost one quarter of the U.S. population will experience a depressive episode over the course of their lives. Despite many recent advancements in the past century, including identification of genetic biomarkers found for the disorder earlier this year, we are still nowhere near finding a reliable means of predicting depression rates. However, where past research has focused on developing a single factor in the development of depressive symptoms, German neuroscientists have managed to create a software program that can calculate the risk of relapse in cases of depression.
The project, conducted by Ruhr University Bochum and Mercator Research Group, examined a multitude of factors that have been known to influence depression. Designed to assess the risk of relapse (versus predicting outright if someone will develop a depressive disorder), the test screened for factors such as rate of memory lapse, cognitive bias and activity levels of serotonin, one of the major neurotransmitters that affect mood.
“Often, it is not clear if a patient is going through the remission or the recovery phase when he shows depressive symptoms for a few days during the six-month period. We want to use our model to explain the occurrence and recurrence rates,” said Dr. Selver Demic, lead author of the study.
After using the software model, the researchers discovered that there were two sets of people with depression: a high risk group that is permanently prone to developing symptoms and a “temporary” group, where bouts of depression are due much in part to chance. The software itself incorporates a wide array of social factors such as income, family demographics and quality of the participants’ emotional support network. More traditional factors such as neurotransmitter levels (serotonin, norepinephrine, dopamine, etc.) were also taken into account when writing the program.
A new classification system for depression
The goal of the program was not to simply predict relapse rates for depression, but to compile a new system for classifying its individual disease states as well. Where the current classification system includes a host of various depressive orders with overlapping symptoms (major depressive disorder, chronic, persistent, psychotic or postpartum depression, etc.), the authors intend for their software program to provide a more objective system than the one offered by the current “Diagnostic and Statistical Manual for Mental Disorders” (“DSM-V”).
The aforementioned current system relies on subjective opinions from the patient for diagnosis, including lack of motivation, sadness or thoughts of not wanting to go on for a minimum of two weeks. Some symptoms may seem more pronounced than others each day, making an accurate listing of their symptoms (and thus, meeting the criteria that most appropriately fits their disorder) near impossible.
The mathematical model behind Dr. Demic’s program, referred to as his finite state machine (FSM), is fed data regarding the patient’s psychological state on a daily basis, creating a much more detailed picture of their symptoms. Based on the frequency of each symptom and the time between occurrences, the test is able to calculate the particular disease state and its risk of remission much more accurately than a traditional diagnosis.
Understanding that depression is one of the most commonly diagnosed (and misdiagnosed) mental health disorders in the world, there are a litany of cutting edge approaches to its treatment, including pharmacogenetic (PGT) testing, which uses the patient’s genetics from a sublingual swab to find the most appropriate medication for their specific physiology. If you would like more information about depression diagnosis and treatment, feel free to contact 866-858-7917.
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