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UCSD Scientists Find Certain Metabolisms May Be Linked To Recurring Depression

UC San Diego's campus is shown in this undated aerial photo.

Credit: UC San Diego

Above: UC San Diego's campus is shown in this undated aerial photo.

Researchers at the UC San Diego School of Medicine found that certain metabolites — small molecules produced by the process of metabolism — may be predictive indicators for people at risk for clinical depression, it was announced Tuesday.

"This is evidence for a mitochondrial nexus at the heart of depression," said senior author Dr. Robert Naviaux, a professor of medicine, pediatrics and pathology at the UCSD School of Medicine. "It's a small study, but it is the first to show the potential of using metabolic markers as predictive clinical indicators of patients at greatest risk — and lower risk — for recurring bouts of major depressive symptoms."

The findings of the UCSD researchers, who worked in collaboration with Dutch scientists, were published in the online issue of Translational Psychiatry.

Clinical depression — formally known as recurrent major depressive disorder — is a mood disorder characterized by multiple symptoms such as feelings of sadness or hopelessness, anger or frustration, loss of interest, sleep disturbances, anxiety, slowed or difficulty thinking, suicidal thoughts and unexplained physical problems, such as back pain or headaches.

Major depressive disorder is among the most common mental illnesses in the United States, with an estimated lifetime prevalence of 20.6%, meaning one in five Americans will suffer at least one episode during their lives. For patients who have recurrent MDD, the five-year recurrence risk is up to 80%.

For the study, Naviaux and colleagues in the Netherlands recruited 68 subjects — comprised of 45 women and 23 men — with recurring MDD who were in antidepressant-free remission and 59 age- and gender-matched controls. After collecting blood from patients who were in remission, the patients were followed prospectively for 2 1/2 years.

Results showed that a metabolic signature found when patients were well could predict which patients were most likely to relapse up to 2 1/2 years in the future. The accuracy of this prediction was more than 90%.

The researchers found that in subjects with recurring MDD, changes in specific metabolites in six identified metabolic pathways resulted in fundamental alterations of important cellular activities.

"The findings revealed an underlying biochemical signature in remitted rMDD that set diagnosed patients apart from healthy controls," Naviaux said. "These differences are not visible through ordinary clinical assessment, but suggest that the use of metabolomics — the biological study of metabolites — could be a new tool for predicting which patients are most vulnerable to a recurrence of depressive symptoms."

The authors noted that their initial findings require validation in a larger study of at least 198 women and 198 men with 99 cases and 99 controls each.


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