COMMITMENT

COMMITMENT - COMorbidity Modeling via Integrative Transfer machine-learning in MENTal illness

Psychotic disorders, including schizophrenia and bipolar disorders, comprise some of the most severe mental illnesses that cause an enormous clinical and healthcare burden, costing close to €100 billion annually in Europe alone. The diagnostic delineation of these conditions is clinically defined and does not index appropriately the underlying biology. Patients are treated largely by a “one-fits-all” approach, despite substantial clinical heterogeneity in course, treatment response and presence of somatic comorbidities that include type 2 diabetes, cardiovascular diseases and neurodegenerative processes. There is a strong need to identify biological means to stratify patients with psychotic disorders and identify the biological basis of somatic comorbidity. This will allow improved clinical delineation of psychotic illnesses and facilitate novel intervention strategies targeted at the minimization of comorbidity risk, reducing mortality and morbidity. To address this, COMMITMENT will develop an innovative computational framework for stratification of psychotic disorders and identification of biological domains shared with comorbidities. COMMITMENT builds on distributed machine learning that integrates mechanistic information mined from the literature. It extends approaches successfully used for oncological systems-medicine investigations, to optimally extract disease signatures from partially overlapping multi-OMICs data. We will use massive-scale genetic and neuroimaging data to explore the lifespan trajectories of stratification and comorbidity profiles, to identify age periods with pronounced comorbidity risk and to disentangle state- from trait-effects. We will explore the predictivity of comorbidity profiles for illness course, treatment response and occurrence of comorbidity during early illness phases. With this, COMMITMENT will provide the basis for biologically-informed clinical tools for improved personalized care of patients with psychotic disorders.

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