Aging is a gradual and irreversible pathophysiological process. It presents with declines in tissue and cell functions and significant increases in the risks of various aging-related diseases, including neurodegenerative diseases, metabolic diseases, musculoskeletal diseases, and immune system diseases. With the aging of society, these condition have gradually become the most important causes of disability and death in elderly individuals, which contribute significantly to the global disease burden. Therefore, there is an urgent need to better understand the pathogenesis of aging, identify potential risk factors and develop effective prevention and control strategies.
This project aims to assess how modifiable risk factors and their interaction affect brain health in patients with aging-related diseases. We expect to develop a powerful model capable of accurately assessing an individual’s risk of developing aging-related disorder by utilizing deep learning and integrating various data dimensions, including clinical data, multi-omics data, and exposure factors. In addition, integrating analysis of genetics, proteins, metabolism, and imaging will unveil the regulatory mechanisms of aging-related diseases on brain health, and identify potential new biomarkers and drug treatment targets.
This three-year project will provide valuable insights into valuable information for individual risk assessment and management of modifiable risk factors for aging-related disease, and help to improve brain health and clinical outcomes in patients with aging-related disease.
