Researchers at the University of Illinois Chicago have identified a novel biomarker for depression that predicts the efficacy of antidepressant treatment. The corresponding research was published in Molecular Psychiatry.
Major Depressive Disorder (MDD) is a leading cause of disability worldwide. Selective Serotonin Reuptake Inhibitors (SSRIs) are one of the most commonly used antidepressant treatments for the disorder however can take months to work and are not effective for all patients.
"When you are depressed, adenylyl cyclase is low, " said Mark Rasenick, lead author of the study, "The reason adenylyl cyclase is attenuated is that the intermediary protein that allows the neurotransmitter to make the adenylyl cyclase, Gs alpha, is stuck in a cholesterol-rich matrix of the membrane -- a lipid raft -- where they don't work very well."
For the present study, the researchers included 49 participants aged 30–65 who met the diagnostic criteria for MDD, as well as 59 healthy controls. The participants gave two blood samples, one week apart, to check for the presence of Gs alpha, with a third being optional for those who chose to take medication.
In the end, the researchers found that MDD subjects had lower adenyl cyclase levels than the control samples. In medicated patients who showed improvement, however, levels of adenyl cyclase were elevated by at least 30%, and Gs Alpha was shown to be out of the membrane.
These findings, say the researchers, indicate that GS Alpha could be used as a biomarker to identify individual responses to antidepressant medication. Within a week of treatment, blood tests would be able to show whether or not Gs alpha is inside lipid rafts, and thus indicate treatment efficacy.
The researchers noted several limitations to their findings. The small sample of participants may not be representative of the wider population. The study also lacked a placebo group, so the researchers were unable to establish whether changes arose from antidepressant usage or other means.
In future studies, they hope to determine the full utility of the biomarker and whether it can predict antidepressant response before clinical metrics.