November 01, 2016
NIH Warns Against Use of 'One-Size-Fits-All' Treatments for Depression
The National Institutes of Health (NIH) recently published an article that highlights the necessity of unique treatment approaches for individuals battling depression. Depression, one of the most common mental disorders in the U.S., is not easily treated with “one-size-fits-all” therapy, the article explains, and is usually treated using antidepressant medications, therapy, or a combination. "It’s often a trial-and-error process to figure out which approaches will work best for each individual," the author notes. Researchers used information about early-life stressors and amygdala activity to predict the ability of certain antidepressants to treat symptoms of depression. Some correlation between physical differences in the amygdala and responsiveness to specific antidepressants emphasize the necessity of access to a range of antidepressants as part of individualized treatments.
Predicting the usefulness of antidepressants
Depression is one of the most common mental disorders in the U.S. It can bring a persistent sad, anxious, or “empty” mood and affect your ability to function and enjoy life. There’s no “one-size-fits-all” therapy. Depression is usually treated with antidepressant medications, psychotherapy, or a combination. It’s often a trial-and-error process to figure out which approaches will work best for each individual.
Early life stressors and the brain’s processing of emotions can each contribute to depression. Stress can activate and eventually cause physical changes to the brain’s amygdala region, which processes fear. A group of scientists, led by Drs. Leanne Williams and Andrea Goldstein-Piekarski at Stanford University, investigated whether they could predict the likelihood that antidepressants would work for patients with depression based on their childhood stress exposure and amygdala activity. The research was funded in part by NIH’s National Institute of Mental Health (NIMH) and National Institute of Biomedical Imaging and Bioengineering (NIBIB). Results were published in the Proceedings of the National Academy of Sciences on October 18, 2016.
The team analyzed data from 70 patients with major depressive disorder from the International Study to Predict Optimized Treatment for Depression (iSPOT-D). Patients were asked how many life stressors they’d experienced before age 18. This included abuse, neglect, family conflict, illness or death, and natural disasters. Using functional MRI, the researchers measured brain activity in patients while they viewed pictures of emotional faces. Brain scans were taken before patients started an antidepressant treatment and 8 weeks after. Participants were randomly selected to receive 1 of 3 commonly prescribed antidepressants: sertraline (Zoloft), escitalopram (Lexapro), or venlafaxine-XR (Effexor-XR).
The team compared how well early life stressors and brain responses to positive or negative facial expressions correlated with patient recovery. A model combining all 3 factors predicted the likelihood that antidepressants would benefit patients with over 80% accuracy.
The researchers grouped patients into 3 categories based on the number of stressful events they’d experienced (low, medium, or high). Antidepressants were less likely to work for those in the high-stress category. However, these patients had a greater chance of benefiting from the medications if their brains were highly responsive to happy facial expressions. Patients with low childhood stress were most likely to benefit from antidepressant treatment. Their chances rose if their brains were less sensitive to both happy and fearful stimuli. These results suggest that, for some patients, it might help to first try therapy techniques that address the impact of trauma in a person’s life before considering medication.
“We were able to show how we can use an understanding of the whole person—their experiences and their brain function and the interaction between the 2—to help tailor treatment choices,” Williams says. More research is needed to determine whether this model could be used to predict if a specific antidepressant would benefit a patient.