We aim to investigate how biological aging, as measured through proteome clocks, is associated with health outcomes and genetic factors in the UK Biobank. Biological age reflects molecular changes that accumulate over time and may provide insights beyond chronological age. Using established algorithms developed in Oh et al. (PMID: 38057571), Argentieri et al. ( PMID: 39117878), we will calculate proteome age for participants with proteome data available in the UK Biobank.
We will then integrate these proteome aging measures with genetic information (SNP data) and rich phenotypic data from the UK Biobank including lifestyle variables. This integrative analysis will allow us to examine how genetic variation contributes to differences in biological aging and whether accelerated proteome aging is associated with increased risk of age-related health outcomes.
The ultimate goal of this project is to identify molecular and genetic markers of accelerated aging that may help to better predict disease risk and inform prevention strategies. By leveraging the large-scale, deeply characterized UK Biobank cohort, this research has the potential to advance our understanding of the biology of aging and its implications for public health. All analyses will be conducted within secure, approved computing environments in full compliance with UK Biobank data access policies.