Depression is a growing problem, but diagnosing it is difficult, because traditional interviews and questionnaires can be subjective. Now, researchers found that depression involves predictable changes in gut bacteria and their metabolites.

The findings, published in Cell Reports Medicine, suggest that specific gut microbes and metabolites can serve as biomarkers to help diagnosing depression and guide treatment.

While it’s known that certain microbes and metabolites can influence depression by affecting brain pathways and neurotransmitters, it’s unclear whether they could reliably help diagnose the condition.

Researchers led by Mingliang Zhao at Shanghai Jiao Tong University School of Medicine in China conducted a study analyzing gut bacteria and blood metabolites in dozens of people with depression, before and after treatment.

Metabolic patterns

By comparing blood and stool samples from non-depressed individuals with samples from people with depression, the researchers found that people with depression showed alterations in their blood metabolites. An analysis of more than 200 metabolites revealed 34 molecules that differed between depressed and non-depressed people.

Many of these changes were reversed after treatment, showing that medications can restore certain metabolic patterns. Animal experiments confirmed similar trends, supporting the connection between gut microbes, metabolism, and depression.

Using data from one group of participants, the researchers found that certain metabolites mediate the effects of specific gut microbes on depression. For example, the amino acid L-tyrosine mediated some of the effects of one bacterial species on depression, and homovanillic acid partly mediated the effects of another. 

Identifying depression

Key bacteria and metabolites, such as Bifidobacterium longum, Roseburia intestinalis, serotonin, and homovanillic acid, were linked to lower depression risk, while others, such as Blautia obeum and 2-hydroxybutyric acid, were linked to higher risk, the researchers found. 

Building on these findings, the team developed a machine-learning model using 34 metabolites that could reliably identify depressed individuals.

Although more research is needed to confirm these results, the findings “highlight metabolites as key mediators linking microbiota to depression and as valuable indicators for its identification,” the authors say.