Aging is a gradual process associated with the accumulation of changes over time, leading to tissue and function decline. This decline increases susceptibility to disease and mortality. As lifespans lengthen, age-related diseases become a growing burden. Therefore, identifying hallmarks of aging and their associated biomarkers is key to understanding aging mechanisms and developing new therapies.
Our recent study of over 5,000 individuals’ blood proteins (Oh et al., Nature 2023) revealed that aging rates vary not only between people but also among organs within individuals. Using machine learning models across 11 vital organs and five diverse cohorts, we developed a robust method for quantifying organ age. The findings are startling: nearly 20% of individuals suffer from significantly accelerated aging in at least one organ, and 1.7% across multiple organs. This translates to a chilling 20-50% increase in mortality risk. Even more compelling, specific organ aging patterns directly link to corresponding diseases, opening doors to early intervention and personalized medicine based on individual organ aging profiles.
Expanding upon our recent discoveries linking blood proteins to organ-specific aging, we propose a comprehensive research program leveraging the UK Biobank’s rich dataset of over 50,000 participants.
Aim 1: Develop novel statistical models to quantitatively characterize the relationships between diverse plasma protein levels, ratios, and network interactions with both organ-specific and whole-organism aging.
Aim2: Identify and validate robust protein biomarkers of aging through comprehensive analysis, with a focus on early detection and risk stratification for age-related diseases.
Aim 3: Leverage machine learning to integrate these proteomic aging biomarkers with comprehensive data on psychological, social, and environmental factors to construct a multi-dimensional aging index.
Aim 4: Conduct in-depth analysis of individual proteomic profiles, including their associations with lifestyle and/or independent and interacting biological/genetic factors linked to age-related diseases or their clinical markers.
The expected duration of this project is 3 years. This project holds the potential to dramatically advance our understanding of aging, providing crucial insights into early detection, preventive strategies, and the development of new biomarkers for research. Our findings will be freely available to the scientific community, fueling further innovation in aging studies. Moreover, our work will offer valuable guidelines on tailoring machine learning models for predicting disease onset and progression, ultimately helping individuals age not just longer, but better.