Type 2 Diabetes (T2D) and obesity-related traits are global epidemics. In the UK alone, ~4 million people are living with diabetes and 10% of the NHS budget is spent on diabetes. A limitation to early detection and treatment of T2D is its diverse clinical presentation and response to medication. Improved methods for stratification of patients would have a large clinical impact. Genome wide association studies (GWAS) have identified genetic variants associated with T2D, and there is much excitement in using Polygenetic Risk Scores (PRS) to predict risk of disease from genetic data. Clinical subtypes of T2D have been linked to different genetic variants, suggesting the molecular drivers of disease may differ across genetic subtypes.
This project will leverage large biobanks, electronic health records and ‘omic profiling to 1) identify the molecular signature of T2D genetic subtypes and 2) determine if PRS predictive ability is increased by combining genetic and ‘omic data to predict disease.
Define Type 2 Diabetes clinical subtype Polygenic Risk Scores in TwinsUK and other datasets with available ‘omic data (gene expression from adipose (fat), skin, gut and blood, plus epigenetics, proteomics, metabolomics, and microbiome) to identify ‘omic signatures associated with genetic risk of different Type 2 Diabetes subtypes.
Evaluate subtype specific ‘omic signatures for disease mechanisms and links to therapeutic targets
Construct combined PRS and ‘omics risk score (including baseline and trajectories over time) to test for improved ability to predict future development or disease progression of T2D.