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As a Clinical Lecturer in Psychiatry at the University of Cambridge (Assistant Prof), I investigate the landscape of early psychosis, schizophrenia, and mood disorders. My research focus centers on unraveling the psychiatric, immune, and cardio-metabolic dimensions of these mental illnesses. My approach combines psychiatric clinical informatics and data science. Through analysis of large datasets, I have constructed longitudinal “virtual cohorts” of psychiatric patients. These cohorts serve as powerful tools for characterising immune and metabolic markers across various stages of illness. The culmination of this work lies in the development of risk prediction models for health outcomes in psychosis. In particular, I’ve created a clinical risk prediction tool named MOZART, aimed at calculating the risk of treatment-resistant schizophrenia at the time of a first episode of psychosis. This work earned recognition, including the Schizophrenia International Research Society Early Career Award. My academic journey also includes a PhD in bioinformatics at Imperial College London, which focussed on the genetics of schizophrenia. Additionally, I have harnessed clinical research methods such as Magnetic Resonance Imaging and biomarker analysis to investigate cardiac and adipose tissue changes in schizophrenia. These investigations have led to a novel hypothesis regarding the potential etiology of cardiac alterations in this condition. I am Joint Lead of the Cambridge Mental Health Mission Mood Disorders Research Clinic; Deputy Lead of the Early Psychosis Workstream, Mental Health Translational Research Collaboration; Cambridge PI of the Early Psychosis Workstream, Mental Health Mission; Deputy Lead of Clinical Academic Training in Psychiatry, University of Cambridge; and an Early Psychosis Multi-arm, Multi-stage Platform Trial (PUMA) clinician. My research themes for future students include: - Clinical risk prediction modelling: I developed a clinical risk prediction tool called MOZART, aimed at calculating the risk of treatment-resistant schizophrenia at the time of a first episode of psychosis - this will be refined adding genetic predictors (https://europepmc.org/article/MED/37034013). - Epidemiology: work on using large population-based datasets to investigate the origins of mental illnesses such as depression and psychosis (see for example https://mentalhealth.bmj.com/content/28/1/e301506). - Electronic health records: work on mining EHR for clues on the links between inflammation, cardiometabolic changes, and mental illnesses such as depression and psychosis - including pharmacological outcomes such as treatment response. - Genomics: investigating polygenic risk scores and other rare variant-linked scores and their associations with mental illnesses - and how these can be used for clinical risk prediction. - Genetic epidemiology: Mendelian Randomisation and other genetic epidemiology techniques to disentangle potentially causal effects in mental illness.
MOZART: a risk prediction tool for treatment resistant schizophrenia
Commonly recorded clinical information at psychosis onset including blood markers can help predict whether a person will develop treatment resistant schizophrenia.
