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The Relationship between Cardiometabolic Disorders and Schizophrenia: From Early-Life Origins to the Development of a Cardiometabolic Risk Prediction Algorithm for Young People with Psychosis 10.17863/CAM.76525 Perry, Benjamin Khandaker, Golam Jones, Peter 000000020387880X My thesis considers the theme of comorbidity between cardiometabolic disorders and schizophrenia by focussing on three key aspects: the nature of association between cardiometabolic disorders and schizophrenia; the potential for common underlying biological mechanisms for the comorbidity; and the prediction of cardiometabolic risk in young adults with psychosis. On the nature of association between cardiometabolic disorders and schizophrenia, using longitudinal repeat measure data from a large birth cohort, I found that disruption to glucose-insulin homeostasis through childhood/adolescence is associated with increased risk of psychosis in early-adulthood; may not be fully explained by common sociodemographic and lifestyle factors; and may be specific to it. On the mechanisms of association between cardiometabolic disorders and schizophrenia, I used a range of genetic and observational epidemiological methods to examine whether inflammation and shared genetic liability may be common underlying biological mechanisms for the comorbidity. Using birth cohort data, I show that genetic risk for type 2 diabetes is associated with psychosis-risk in adulthood, and vice versa. I also show that genetic risk for type 2 diabetes may influence psychosis risk by increasing systemic inflammation. Using summary data from large genome-wide association studies (GWAS), I show a thread of evidence for shared genetic overlap between schizophrenia, cardiometabolic and inflammatory traits. Finally, using Mendelian randomization, I show evidence supporting that inflammation may be a common cause for insulin resistance and schizophrenia. On the prediction of cardiometabolic risk in young adults with psychosis, I performed a systematic review of cardiometabolic risk prediction algorithms and explored their predictive performance in a sample of young people at risk of developing psychosis. In doing so, I show that none are likely to be suitable for this population. Then, using patient data, I developed and externally validated the Psychosis Metabolic Risk Calculator (PsyMetRiC), the first cardiometabolic risk prediction algorithm specifically tailored for young people with psychosis. Together, my work suggests that cardiometabolic disorders and schizophrenia share aetiologic mechanisms, namely inflammation and shared genetic liability. I have shown that it is possible to accurately predict cardiometabolic risk in young people with psychosis using a tool tailored for the population. Such tools can in future become valuable resources for clinicians to reduce the risk of long-term cardiometabolic morbidity and mortality in people with schizophrenia. University of Cambridge 2021-05-10 Thesis Doctoral Doctor of Philosophy (PhD) eng National Institute for Health Research (NIHR) Doctoral Research Fellowship https://www.repository.cam.ac.uk/handle/1810/329079 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/a3f4b688-e3dc-42f9-bcf9-07857d4391ed/download d09fa9a918deb0e69966b55b91c29357 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/5ab900da-48ec-46ea-ae98-55e2f67e48c6/download 353adac0d1ebdfd65ab16480263c3c87 https://www.rioxx.net/licenses/all-rights-reserved/ schizophrenia cardiometabolic disorders genetics epidemiology risk prediction