One major stumbling block in the treatment of depression is the fact that while antidepressants are an effective option, they do not work in all people with the condition. Scientists report a discovery that may explain why this is the case.
Marianne Müller and her colleagues from the University Medical Center Mainz and the Max Planck Institute of Psychiatry, both in Germany, conducted the new research.
Their findings were published in the journal PLOS Biology.
In most cases of depression, psychotherapy, antidepressant medications (such as selective serotonin reuptake inhibitors), or a combination of both are prescribed to help treat symptoms. However, only one third of people with depression benefit from the antidepressant that they are prescribed.
There is no “one-size-fits-all” approach to treating depression and no way to predict whether or not a specific treatment will work for a person. Therefore, the most effective treatment is currently identified through a process of trial and error until the right fit is found.
One way to develop tailored treatments would be to distinguish biomarkers that determine whether a person would respond to a certain medication or not. Although research in this area has been promising, no significant predictors have yet been identified due, in part, to three issues.
- Firstly, those with depression likely have different functional changes that result from their condition.
- Secondly, environmental factors such as childhood maltreatment, disease episodes, previous life events, and different treatment schedules may remain unidentified and thus reduce the likelihood of detecting response biomarkers.
- Lastly, age, sex, and genetic background all impact transcription profiles, measurements, and treatment outcomes.
Müller and colleagues developed a novel approach to overcome the hurdles of previous research that enabled extreme phenotypes in response to antidepressant treatment to be selected in a mouse model of depression.
The mouse model simulated the situation in humans by identifying mice that were responsive and unresponsive to antidepressant treatment.
The researchers hypothesized that the mouse model would help to identify peripheral biomarkers associated with a positive treatment response, and that these could potentially be applied to humans.
“We were able to identify,” explains Tania Carrillo-Roa, who works in the Max Planck Institute of Psychiatry, “a cluster of antidepressant response-associated genes in the mouse model that we then validated in a cohort of depressed patients from our collaborators from Emory University, Atlanta.”
The researchers discovered that molecular signatures that are connected with treatment response in mice could predict the outcome in a human cohort with an accuracy of 76 percent.
Furthermore, they pinpointed the glucocorticoid receptor (GR) — and GR sensitivity in particular — as playing a key role in shaping a person’s response to treatment with antidepressants. The GR helps to fine-tune the stress hormone system. The study authors write:
“Intriguingly, we finally show that GR-regulated genes are significantly enriched in this cluster of antidepressant-response genes, pointing to the involvement of GR sensitivity as a potential key mechanism in shaping transcriptional changes and clinical response to antidepressant treatment.”
Identifying biomarkers that predict a person’s treatment response outcome would eliminate the cost and consequences of the trial and error approach to prescribing antidepressants, and, ultimately, improve patient care.
The experimental cross-species approach that was used by the study investigators could serve as a template for developing tailored treatments in the future.