Early prediction and diagnosis of eye and brain diseases as non-renewable organ-related diseases remain challenging. Although techniques such as retinal imaging have been shown to correlate with pathological processes in the brain, current prediction accuracy and generalizability are limited and further validation is urgently needed. Biological age is an important indicator for assessing the aging process, and the wealth of genetic, proteomic, phenotypic and imaging data in UK Biobank offers the possibility of calculating the aging process in individuals. However, existing studies are still limited in the selection and cross-organizational applicability of biological age metrics.
Therefore, this project intends to integrate multi-omics data (including proteomic, genomic, imaging and clinical information) in UKB, construct an individual aging degree assessment model based on machine learning, and explore its potential application in early prediction of ocular and neurodegenerative diseases. We will combine phenotypic and histological features to identify key biomarkers and systematically analyze their commonalities and specificities in different disease types, providing a theoretical basis for understanding aging mechanisms, disease prediction and individualized intervention.
