Gut microbiome interactions with genetic risk and immuno-metabolic pathways in depression

Objective

Depression is a leading cause of disability worldwide, yet its pathophysiology remains poorly understood and predictive biomarkers are lacking. The gut microbiome—a key modulator of metabolic and immune functions—has emerged as an important contributor to brain function and mental health. While several studies report gut microbiome alterations in depression, most are cross-sectional, leaving uncertainty about its temporal relationship with depression onset. Moreover, it remains unknown whether the microbiome interacts with genetic predisposition to influence depression risk, or contributes to depression-related immuno-metabolic dysfunction. BIOME-GRID aims to identify gut microbiome features predictive of future depression and assess their interplay with host genetic liability and immuno-metabolic processes. This will be achieved through computational analysis of metagenomic, genetic, electronic health record, questionnaire and multiomic data from the prospective, population-based Estonian Microbiome Cohort, which uniquely enables the study of the microbiome before depression diagnosis and treatment. Time-to-event prediction modelling will be used to identify microbiome signatures linked to depression onset and assess their additive and interactive effects with genetic risk. Associations with host immuno-metabolic markers and risk factors, and depression symptom clusters will be examined. Findings will be validated in two Swedish cohorts. This fellowship will enhance expertise in metagenomic and multiomic data analysis, while fostering collaboration between Estonian and Swedish groups. Directly relevant to the Horizon Europe Health priority Tackling diseases and reducing disease burden, by providing insights into gut microbiome’s role in depression pathophysiology, BIOME-GRID contributes to improved understanding of mental health disorders and ultimately supports the development of novel, microbiome-informed early detection, prevention, and intervention strategies.