Research

The Exeter Diabetes, Obesity and Metabolic Health research theme grouping consists of multidisciplinary teams of researchers studying the breadth of human endocrine and metabolic disease and its associated complications. Our teams of researchers employ cutting-edge genomic technology, in vivo and in vitro cellular biological techniques, AI data science and biostatistics, and clinical and metabolic studies to advance knowledge of the diagnosis, treatment and prevention of human disease. The work of our teams has impacted on people around the world.

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Our research focuses on understanding basic functions of the central nervous system and the mechanisms driving neurodevelopment and neurodegeneration.

We research mental health conditions and dementia, and we’re pioneering studies into the neuroendocrine system's role in regulating behaviour and physiology. We employ cutting-edge methods in genomics and electrophysiology, exploring the epigenome and transcript diversity in preclinical disease models and human tissue from patients.

Our state-of-the-art neuroimaging centre supports clinical trials for novel treatments of neurodegenerative diseases, while at a population level, our genetics research uses whole genome sequencing to identify the causes of rare neurodevelopmental disorders, and also investigates links between metabolic phenotypes and mental health conditions. By taking an interdisciplinary approach, we aim to identify the underlying mechanisms of brain diseases to improve patient treatment.

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Our wide-ranging Genomics theme aims to translate genomic science into health benefits.

Our work includes: using genome sequencing to diagnose and manage rare monogenic conditions; analysing the role of genomic and epigenomic variation in common complex diseases and traits; using long-read transcript sequencing to explore alternative splicing in health and disease; and evaluating the use of polygenic risk scores and pharmacogenomic markers to inform treatment.

Learn more about our genomics research

• Genomics of Rare Disease
• Monogenic Diabetes
• Hyperinsulinism
• Complex Trait Genomics
• Complex Disease Epigenetics Group
• Early Growth Genetics Consortium
• Reproductive Genomics Group
• Neurogenomics
• RNA-Research Projects

We are a cross cutting theme with expertise in a range of data science and AI methods. These include big data analyses, clinical and regulatory genomics, electronic health record epidemiology, causal inference, computational modelling and statistics, high performance computing, wearable technologies and machine learning.

The approaches we use are disease agnostic and are applied across a range of conditions from diabetes to dementia to discover disease mechanism, identify drug targets, identify modifiable risk factors and classify and predict disease. We have important links to the Turing Institute and the Institute for Data Science and Artificial Intelligence (IDSAI). We maintain best practices in reproducible data science research.

Learn more about our Data Science and AI research

• Complex Trait Genomics
• Artificial Intelligence, Data Science and Biostatistics in Diabetes
• Data Science and Neuroinformatics
• PROTECT Study: Research to support healthy ageing and reduce dementia risk
• Rare Disease Genomics

Translating our research into clinical applications is a key aim in our department and we have interdisciplinary research groups working on therapies and treatments that target our core diseases. We have also formed spin-out companies in order to facilitate bringing therapies to market.

Learn more about our target identification and drug repurposing research

• RNA-Research Projects
• Clinical Neuroscience
• Neuroendocrinology
• Neurodevelopment
• Neurophysiology