Approved Research

Identifying risk factors and drug repurposing for chronic diseases: a longitudinal cohort study based on UK Biobank

Lay summary

With the increases in the prevalence of chronic diseases (non-alcoholic steatohepatitis (NASH), diabetes, obesity, alzheimer's disease, cardiovascular disease, hypertension, cancer, etc) and aging, human health has been seriously threatened and the social and economic burden has become increasingly heavy. It is critical to identify more accurate risk factors and discover more therapeutic drugs for the prevention and treatment of chronic diseases. We have previously developed a new strategy to search for the risk factors and therapeutic drugs (drug repurposing) for chronic disease based on the network-based approaches to prediction and population-based validation (Food Chem Toxicol. 2020;145:111767). Since drug repurposing focuses on drugs that are already marketed, hypothesis testing is possible using large-scale patient-level databases such as UK biobank. For example, In the preliminary work, we used network proximity approach predicted a variety of potential therapeutic agents and risk factors for NASH that had not been reported in the previous literature. Therefore, in order to verify the accuracy of the results obtained by our network-based calculation, and to identify some novel therapeutic drugs and risk factors for chronic diseases, especially NASH, we plan to apply for the UK Biobank database for population-based cohort validation.

Aim: In this study, we aim to:(1) estimate the associations of modifiable environmental, genetic, metabolic, diet and lifestyle habits, the interactions of these risk factors, as well as their impact on the risk and mortality of specific chronic diseases. (2) To assess the impact of drug candidates on the risk of occurrence, severity, and mortality of specific chronic diseases. (3) To investigate the causal association between modifiable environmental, genetic, metabolic, diet and lifestyle habits, medicines and the specific chronic diseases using Mendelian randomization instrumental variable analysis.

Public Health Impact: Through this study, we will be able to identify robust, sensitive, and accessible risk factors and drug candidates of chronic diseases by incorporating multi-omics data. which will help researchers comprehensively understand the development of chronic diseases, and will be helpful in the precise and individual prevention of chronic diseases.