Brain; Mental Health
Breakthroughs in depression research lead to more effective treatments
December 9, 2020
Even before the COVID-19 public health crisis, depression was on the rise in America. But since March, rates have tripled and the pandemic now threatens to leave a long-term mental health crisis in its wake.
At UT Southwestern’s Peter O’Donnell Jr. Brain Institute, our research over the last decade has been preparing us for this critical moment.
We’ve led some of the world’s largest studies on depression and mood disorders, and the findings have enabled us to develop a new set of tools that can help zero in on the most effective treatments for depression and potentially revolutionize the field of psychiatry.
For many years, patients have had to endure an inefficient process that relied heavily on questionnaires and a painful trial-and-error process that often mismatched them with antidepressants.
But now, through the combination of a blood test, brain imaging, and artificial intelligence, we’re able to assess patients’ biological signatures and move past the guessing game of choosing depression treatments.
We also hope our research will make it possible to alter the old mindset of how the disease of depression should be diagnosed and treated.
Finding biomarkers in a simple blood test
UT Southwestern initiated a series of national clinical trials in 2011 to better understand mood disorders, and over the years they have produced several breakthroughs.
In 2017, one of our studies demonstrated that measuring a patient’s C-reactive protein (CRP) levels through a simple finger-prick blood test can help doctors prescribe a medication that is more likely to work. Patients with higher CRP levels in their blood were more likely to respond to a combination of medications than those with lower levels.
This was significant because earlier studies had shown that up to a third of depressed patients didn’t improve on their first medication, and about 40 percent of people who started taking antidepressants stopped taking them within three months. Giving up hope is a central symptom of the disease.
But if treatment selection is tied to a blood test and improves outcomes, patients are more likely to continue the treatment and achieve the benefit.
For many years, patients have had to endure a painful trial-and-error process that often mismatched them with antidepressants. Now, we’re able to assess patients’ biological signatures and move past the guessing game.
The research published in Psychoneuroendocrinology measured remission rates of more than 100 depressed patients prescribed either escitalopram alone or escitalopram plus bupropion. Researchers found a strong correlation between CRP levels and which drug regimen improved their symptoms:
- For patients whose CRP levels were less than 1 milligram per liter, escitalopram alone was more effective: 57 percent remission rate compared to less than 30 percent on the other drug.
- For patients with higher CRP levels, escitalopram plus bupropion was more likely to work: 51 percent remission rate compared to 33 percent on escitalopram alone.
These results can be readily applied to other commonly used antidepressants and integrated into clinical practice.
While previous research to establish CRP as an antidepressant marker used levels three to five times higher than our study, our theory was that you don’t need that high of an inflammation to experience the sickness of depression. Even a little inflammation may be sufficient for patients to experience some symptoms.
Utilizing this test in clinical visits could lead to a significant boost in the success rate of depressed patients who commonly struggle to find effective treatments.
EEG brain tests help patients overcome depression
Up to two-thirds of depression patients do not respond to their first treatment. Researchers from UT Southwestern's Peter O'Donnell Jr. Brain Institute are working to eliminate antidepressant trial and error by incorporating brain scans and artificial intelligence algorithms to to eliminate the guess work.
A powerful algorithm points to change
In early 2020, our research showed that a brain scan can provide even more fine-tuned medication recommendations. A simple electroencephalogram (EEG) can show whether a patient is more likely to respond to an antidepressant medication, talk therapy, or even brain stimulation.
The study published in Nature Biotechnology included more than 300 participants with depression who were randomly chosen to receive either a placebo or an SSRI (selective serotonin reuptake inhibitor), the most common class of antidepressant. Our researchers used an EEG to measure electrical activity in the participants' cortex before they began treatment. The team then developed a machine-learning algorithm to analyze and use the EEG data to predict which patients would benefit from the medication within two months.
Not only did the artificial intelligence (AI) accurately predict outcomes, further research suggested that patients who were doubtful to respond to an antidepressant were likely to improve with other interventions such as psychotherapy or brain stimulation.
The findings were validated in three additional patient groups, proving that we can accurately predict who might benefit from an antidepressant and actually bring those learnings to the point of practical use for patients.
Among the next steps is developing an AI interface that can be integrated with EEGs across the country, as well as seeking approval from the U.S. Food and Drug Administration. UT Southwestern and Stanford, one of our partners in the research, have filed for a patent for the algorithm described in the Nature Biotechnology study.
Dr. Madhukar Trivedi: The Depression Initiative
Revolutionizing treatment and detection procedures for mood disorders.
Developing signatures of depression
Data from the study derive from the 16-week EMBARC trial, which we initiated at four U.S. sites to establish biology-based, objective strategies to remedy mood disorders.
The EMBARC project evaluated patients with major depressive disorder through brain imaging and various DNA, blood, and other tests. Our goal was to address a troubling finding from another study we led (STAR*D) that found up to two-thirds of patients do not adequately respond to their first antidepressant.
Previous EMBARC studies identified various predictive tests, including the use of magnetic resonance imaging (MRI) to examine brain activity in both a resting state and during the processing of emotions. EEG will likely be the most commonly used tool because it’s less expensive and – in most cases – will be equally or more effective.
However, a blood test or MRI may be needed for some patients if the depression is manifesting itself in a different way.
There are many signatures of depression in the body. Having all these tests available will improve the chances of choosing the right treatment the first time.
Addressing a growing problem
According to data from the National Health and Nutrition Examination Survey, antidepressant use in the U.S. has increased nearly 65 percent over a decade and a half – from 7.7 percent in 1999-2002 to 12.7 percent in 2011-2014. The pandemic and its aftermath are almost certain to add to those numbers.
The expanded use of medications makes it more critical to further understand the underpinnings of depression and ensure patients are prescribed an effective therapy.
While our team continues to evaluate data from the EMBARC trial, we have initiated other large research projects to help improve the remission rate of depression. Among them is D2K, a study that will enroll 2,500 patients with depression and bipolar disorders and follow them for 20 years. In addition, RAD is a 10-year study of 2,500 participants (ages 10-24) that we hope will uncover factors for reducing the risk of developing mood or anxiety disorders.
Utilizing some of these enrollees, UT Southwestern’s research team will study the results from several other tests to assess patients’ biological signatures to determine the most effective treatment.
We hope that by understanding the biology of depression, and pursuing advances in technology and treatments, we can stem the tide of this mental health crisis and provide more immediate relief to patients.