The joint effects of genetic, lifestyle and environmental risk factors on common diseases and multimorbidity
Most of the diseases are caused by the interaction of genetic, environmental and lifestyle risk factors. According to the WHO, lifestyle can account for 60% of the health and longevity, genetic conditions 15%, environmental and social factors 17%, and medical conditions 8%. More and more people are suffering from multiple non-communicable diseases(NCDs), such as diabetes, cancers, cardiovascular disease, and chronic obstructive pulmonary disease. However, there are few studies investigating the panoramic associations between genetic, environmental and lifestyle risk factors and common diseases which involved diabetes, site-specific cancers, cardiovascular diseases, mental diseases , chronic obstructive pulmonary disease(COPD), and Alzheimer's disease . The term "multimorbidity" in our study is referred to the coexistence of two or more common diseases in the same individual? We will estimate the joint effect of the genetic, lifestyle and environmental risk factors on common diseases and multimorbidity. The polygenic risk scores for individuals will be calculated. The associations of genetic, environmental and lifestyle factors with the risk of common diseases will be tested using Cox proportional hazards. The prediction model for the risk of common diseases will be constructed using the machine learning methods, such as the random forest method. The proposed project will use existing data collected by UK Biobank and will take approximately 24 months to complete. Understanding genetic predisposition to disease and knowledge of lifestyle modifications is necessary for the public to make informed choices. To investigate the associations between the genetic, environmental and lifestyle risk factors and common diseases is of great importance for public health. The risk assessment tool for common diseases based on the combination of the genetic, environmental and lifestyle risk factors can provide decision-making supports for precise and individualized intervention.
|Lead investigator:||Professor Yaogang Wang|
|Lead institution:||Tianjin Medical University|