In recent years, the aging of the population has progressed rapidly, and the burden of age-related diseases and comorbidity among elderly people is heavy, especially the burden of cardiopulmonary diseases is at the forefront. However, few studies have explored the panoramic associations between clinical, biological, behavioral, and environmental risk factors and comorbidity. Comorbidity refers to the co-occurrence of two or more diseases with each other. We aim to identify the shared and specific risk factors of chronic diseases and assess the joint effects. Logistic regression and Cox proportional hazard regression were used to estimate the associations of clinical, biological, behavioral, and environmental risk factors with the risk of age-related diseases and their comorbidity. The machine learning methods, such as the random forest method, were used to construct prediction models for those chronic diseases and their comorbidity. The attributable disease burden of clinical, biological, behavioral, and environmental risk factors on the risk of chronic diseases was evaluated by counterfactual analysis and population attributable fraction (PAF). The proposed project will use existing data collected by UK Biobank and will take approximately 24 months to complete. Understanding the extensive risk factors of chronic diseases is very necessary for public health. By strengthening interventions promoting modifiable risk factors and optimizing the allocation of public health resources, we can lay the foundation for establishing a systematic joint prevention and control mechanism for multiple chronic diseases.
