Metabolic and genetic signatures of the dynamics of first-episode psychosis: associations with adverse effects of antipsychotic treatment

The project aims to investigate the early course of schizophrenia spectrum disorder (SSD), focusing on the molecular signature of first-episode psychosis (FEP) and metabolic side effects induced by antipsychotic (AP) medications. Initially, we will use machine learning methods on our previously collected data to identify biomarker profiles predicting the dynamics of clinical status. We expand our study cohorts by including new FEP patients with extended clinical status and biomarker assessment. Polygenic risk scores will be calculated for all patient groups and included in machine learning algorithms. Preclinical studies using the Negr1-deficient mouse model allow us to explore the long-term effects of AP medications (olanzapine, clozapine) and assess the potential of the incretin agonist tirzepatide or physical activity in preventing associated side effects. The project comprehensively studies SSD, aiming to provide insights for mitigating metabolic side effects of AP treatment.