About
Chronic diseases account for a large proportion of the global burden of disease. In recent years, more studies have explored the association of disease with influencing factors through epidemiological methods such as cohort studies and causal inference methods such as Mendelian randomization. Early intervention in chronic diseases can effectively improve the prognosis and reduce the burden. So, this project plans to find the association (especially the causal association) between several chronic diseases (specifically hypertension, stroke, coronary heart disease, diabetes, psychiatric disorders, depression, asthma, chronic obstructive pulmonary disease, and cancer, etc.) and some candidate risk factors, including demographic and sociological characteristics (gender, education, etc.), lifestyle (physical activity, diet, smoking, etc.), metabolism biomarkers (blood lipids, etc), and genetics (SNPs, etc.). Our team focuses on the application of statistical methods in medical research. Therefore, in this project, we would apply a data-driven study strategy. This study will use some appropriate statistical methods or designs in the UK Biobank dataset to help us discover associations or pathways for the above exposures and outcomes. The project will involve about 500000 subjects and a duration of 3 years. We hope to provide more convincing evidence for detecting new risk biomarkers, pathways from the biomarker to disease, and prediction of the diseases. The project is expected to be useful to public health interests to provide potential biomarkers, suitable statistical methods, and predictive models, which could help to decrease the disease burden.