Depression is a highly prevalent and debilitating condition, yet we still lack both in-depth knowledge concerning its etiopathology and sufficiently efficacious treatment options. With approximately one third of patients resistant to currently available antidepressants there is a pressing need for a better understanding of depression, identifying subgroups within the highly heterogeneous illness category and to understand the divergent underlying biology of such subtypes, to help develop and personalise treatments. The TRAJECTOME project aims to address such challenges by (1) identifying depression-related multimorbidity subgroups and shared molecular pathways based on temporal disease profiles from healthcare systems and biobank data using machine learning approaches, and by (2) characterising these subgroups from multiple aspects including genetic variants, metabolic processes, lifestyle and environmental factors. Following the identification of multimorbidity trajectories, a disease burden score related to depression and adjusted for multimorbidity was established summarising the current state of the patient to weigh the molecular mechanisms associated with depression. In addition, the role of genetic and environmental factors, and also their interactions were identified for all subgroups. The project also attempted to identify potential metabolomic markers for the early diagnostics of these multimorbidity conditions. Finally, we prioritized molecular drug candidates matching the multimorbidity pathways indicated for the individual subgroups which would potentially offer personalised treatment simultaneously for the observable multimorbid conditions yet minimising polypharmacy and related side effects. The present paper overviews the TRAJECTOME project including its aims, tasks, procedures and accomplishments. (Neuropsychopharmacol Hung 2023; 25(4): 183-193)
Keywords: depression; multimorbidity; metabolomics; genetics; machine learning..