Respiratory diseases are one of the diseases with the highest morbidity and mortality in the world. Their complex interactions with multiple comorbidities significantly exacerbate health risks, reduce quality of life, and impose substantial socioeconomic burdens. However, the understanding of the risk factors and underlying mechanisms of their onset and progression is still limited. This project will integrate multi-modal data, including genomics, exposomics (which includes general external exposures such as physical-chemical and socio-environmental factors, specific external exposures such as lifestyle and drug use, and internal exposures such as biochemical responses within the body), metabolomics, proteomics, radiomics, and clinical data, to explore the significant influences of these factors on the occurrence, development, and prognosis of respiratory diseases and their comorbidities. Specifically, the project will focus on the following aspects: 1) Identifying potential risk factors for respiratory diseases and their comorbidities, as well as the possible interactions between these factors. 2) Investigating causal relationships using tools such as Mendelian randomization. 3) Developing predictive models using machine learning and artificial intelligence algorithms. 4) Exploring the heterogeneity of respiratory diseases and their comorbidities and identifying patient subtypes. 5) Identifying potential intervention targets for respiratory diseases and their comorbidities.
These findings are expected to deepen the understanding of the risk factors associated with respiratory diseases and their comorbidities, providing a foundation for the development of novel intervention strategies, guiding personalized treatment plans, and informing the creation of public health policies, ultimately improving patient outcomes.
