Principal Investigator: Professor Fuzhong Xue
Shandong University, Jinan, ChinaTags: 51470, cancer, causal inference, gene and environment, Mendelian randomization, metabolic diseases, phenome-wide-association-study
Cancer and metabolic diseases account for a large proportion of the global burden of disease. Epidemiological association studies reported a variety of environmental risk factors and risk genes for these diseases. However, limited studies have definite causal effects on metabolic disease or cancer, except for few widely recognized risk factors such as smoking for lung cancer and high-calorie diet for obesity. In fact, the effects of these exposures and genes on the diseases are very complicated including direct effects, indirect effects, mediating effects, gene-environment interactions, gene pleiotropy, effect modification and so on. Meanwhile, routine observational studies inevitably suffer from confounding or reverse causality. Besides, metabolic diseases and cancer may also share common risk factors with each other, however, the causal effect and pathogenic mechanism of these factors may be highly heterogeneous.
Therefore, for each potential risk factor, if the causal association with a specific disease was determined, we could intervene the exposure levels of risk factors promptly to reduce the morbidity or mortality of metabolic diseases and cancer. So we aimed to explore the potential causal effect of specific exposure or pathway (physical measurement, blood biochemistry, lifestyle, environment, genes, interactions) on chronic metabolic diseases or cancer and to provide evidence for intervention and prevention. Then we also expect to develop some novel causal inference methods and effective disease prediction methods.
We intend to perform our research for the duration of three years. The study may have certain practicality values for public health and further research if our results are supported. Study on both genes and environmental exposures would provide strong evidence to clarify the relationship between various exposures and diseases. Causal findings may contribute to identify novel biological pathways for disease prevention, diagnosis and treatment or provide suitable prediction indicators. This is of great significance to public health for cancer and metabolic diseases controlling. Additionally, we may also provide new analytical strategies and methods, which may have some application values for further research.