Multimodal Analysis for Predicting Occurrence, Development, and Identifying Therapeutic Targets in Aging and Aging-Related Disorders

The global rise in life expectancy presents a growing challenge with the increasing prevalence of aging and associated diseases like dementia, cardiovascular issues, diabetes, and tumors. These conditions now contribute significantly to the global disease burden. Complex factors such as exposure and genetics make it challenging to accurately understand and address the occurrence, development, and treatment of aging-related diseases.
This study, rooted in the UK Biobank database, extensively explores its multimodal data, concentrating on genetics, exposure, and endophenotypes to uncover their intricate relationships with aging and related diseases, aiming to identify potential biomarkers. Integrating various data dimensions, including genetics, plasma proteins, metabolism, imaging, and exposure factors, we will utilize machine learning methods and polygenic risk scores to construct highly predictive models for a more accurate forecast of the occurrence and development of aging-related disorders. Our dedication extends to the development of reliable disease subtyping for precise diagnosis and treatment. Through bioinformatics analysis incorporating plasma proteomics, metabolomics, and genetic data, we aim to unveil the biological functionalities and regulatory mechanisms underlying aging and related diseases. Leveraging Mendelian randomization analysis, our objective is to identify potential drug targets associated with aging-related disorders.
This three-year research project’s completion promises a comprehensive understanding, unraveling the etiology of aging and related diseases, elucidating modifiable and non-modifiable risk factors, and establishing the foundation for crafting practical public health and clinical solutions.