Melancholy in the Microbiome: Decoding the Gut-Brain Axis in Major Depression – A Systematic Review
DOI:
https://doi.org/10.12775/JEHS.2026.95.74083Keywords
Major Depressive Disorder, Microbiota-Gut-Brain Axis, Tryptophan-Kynurenine Pathway, Dysbiosis, NeuroinflammationAbstract
Background. Major Depressive Disorder (MDD) is a debilitating affective condition that is increasingly recognized as a systemic disease driven by the Microbiota-Gut-Brain Axis (MGBA).
Aim. This systematic review aimed to summarize and identify common ground among current findings regarding the correlation between pathological microbiome growth and the development of MDD.
Material and methods. A systematic literature search was conducted in the PubMed and Scopus electronic databases to identify clinical studies evaluating gut microbiota composition in adult patients (aged 18–65) with Major Depressive Disorder (MDD). Applying strict PICOS eligibility criteria, a three-person independent screening process was performed following PRISMA guidelines. From the initial 915 identified records, a final cohort of 66 unique publications was selected for multi-omics synthesis. Extracted data parameters included clinical psychometric scores (BDI, HAM-D), microbial metabolite profiles, and specific alpha- and beta-diversity metrics.
Results. The synthesis of the 66 studies highlighted a clear pathobiological cascade, where the depletion of protective strains (Faecalibacterium and Roseburia) and overgrowth of pathobionts (Klebsiella and Ruminococcus) compromised intestinal barrier integrity. This "leaky gut" triggers a systemic influx of LPS and cytokines (IL-1β, IL-6, TNF-α), which cross the blood-brain barrier and enter the brain. This molecular synergy exacerbates both depressive severity and gastrointestinal comorbidities (IBS/IBD).
Conclusions. MDD-associated dysbiosis drives neuroinflammation via the gut–brain axis, as conceptualized in the NIMETOX framework. Translating these findings into personalized microbiome-targeted therapies requires standardization of methodologies, accounting for antidepressant confounders, and integration of multi-omic research.
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