Chronic diseases are a major contributor to disease burden worldwide. 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 elucidate the causal relationship between chronic diseases (hypertension, stroke, coronary heart disease, diabetes, dementia, Alzheimer’s disease, Parkinson’s disease, schizophrenia, depression disorders, chronic obstructive pulmonary disease, cancer, etc.) and candidate risk factors, including demographic and sociological characteristics (gender, education, etc.), lifestyle (physical activity, dietary, smoking, etc.), biomarkers (lipids and lipoproteins, sex hormones, etc), and genetics (SNPs, etc.). Our team focuses on the application of statistical methods in medical research. Therefore, in this project, we plan to apply a data-driven strategy. This study will use some appropriate statistical methods to help us discover associations or pathways between above listed factors and chronic diseases. 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 provide potential biomarkers, appropriate statistical methods, and predictive models, and ultimately reducing the global burden of chronic diseases.
