The aim of the proposed research is to understand comorbidities in cardiometabolic diseases. In particular, to answer the following questions: what are the biological mechanisms that drive a comorbidity in patients of type 2 diabetes (T2D)? Is the risk for a comorbidity the same or different across disease subtypes? For effective prevention and treatment, how to stratify patients reliably based on their risks for a comorbidity?
In order to address these questions, we will investigate the genetic effects that are private to a particular disease subtype and that are shared across multiple cardiometabolic diseases. The study aims to deliver genes as therapeutic targets for particular commodities. In order to estimate the efficacy and safety of modulating a gene for a disease, we will use genetic variations as a proxy and investigate their pleiotropic effect to the phenome.
We will build statistical models that learn the structures in the genetic effects for diseases and disease subtypes; the genetic correlation between such structures and a wide range of phenotypes covering risk factors, biomarkers, medication, and social economical factors.
The output from the proposed study can additionally be used to inform personalised risk management. This is by predicting what patient is likely to develop what comorbidity based on their genetic and phenotypic information using the models learned from the study.