Abstract
This study aims to explore the mechanism by which gut microbiota influences the antidepressant effects of serotonin reuptake inhibitors (SSRIs) through metabolic pathways. A total of 126 patients were analyzed for their gut microbiota and metabolomics. Patients received SSRI treatment and were categorized into responder and non-responder groups based on changes in their Hamilton Depression Rating Scale (HAMD-17) scores before and after treatment. The association between gut microbiota composition and the efficacy of SSRIs was investigated through 16S rRNA gene sequencing and metabolomic analysis, and a predictive model was developed. As a result, the study found significant differences in gut microbiota composition between the responder and resistant groups. Specific taxa, such as Ruminococcus, Bifidobacterium, and Faecalibacterium, were more abundant in the responder group. Functional analysis revealed upregulation of acetate degradation and neurotransmitter synthesis pathways in the responder group. The machine learning model indicated that gut microbiota and metabolites are potential biomarkers for predicting SSRIs efficacy. In conclusion, gut microbiota influences the antidepressant effects of SSRIs through metabolic pathways. The diversity and function of gut microbiota can serve as biomarkers for predicting the treatment response, providing new insights for personalized treatment.