Project Description

People with psychotic disorders like schizophrenia die on average 15 years sooner than the rest of the population, and the mortality gap is widening. Comorbid cardiometabolic disorders are a key contributor to the excess mortality. Cardiometabolic dysfunction is detectable by psychosis onset in predominantly young individuals, many years earlier than typically observed in the rest of the population. Recent research suggests the potential for common biological underpinnings between psychosis and cardiometabolic dysfunction with accumulating evidence for the role of accelerated ageing, though the underlying pathophysiological mechanisms remain unclear.

This project will leverage cutting-edge big data genetic epidemiological approaches applied to large existing genetic datasets from multinational consortia to explore putative biological and brain structural mechanisms of accelerated ageing with risk of comorbid schizophrenia and cardiometabolic dysfunction. Approaches will include two-sample Mendelian randomization and multi-trait genetic colocalization to explore putative causal biological and structural mechanisms, and locus-level linkage disequilibrium score regression to explore genetic similarity between cardiometabolic dysfunction, brain structure, mechanisms of accelerated ageing, and schizophrenia.

These mechanisms will then be validated prospectively in a clinical population of patients receiving care in NHS psychosis early intervention services. In-depth longitudinal assessments of aging and cardiometabolic function will triangulate our biological understanding of the mechanisms underlying psychosis and its associated cardiometabolic dysfunction, toward the identification of novel treatment targets. This project will bring together experts in data science, neuroimaging, clinical psychiatry, and biophysiology, and provide a supportive environment for the development of transferable skills in epidemiology, data science, and detailed physiological testing.