A Novel Proteomic Aging Clock
By now there are most likely dozens of published aging clocks constructed from various omics databases. The proliferation of new clocks isn't helping to solve the fundamental problem with this approach to assessing biological age, which is that the predicted biological age produced by a clock isn't actionable, as no-one yet understands how the clocks relate to causative processes of aging. Thus factions within the research community are arguing for standardization to a single clock, followed by focused effort on understand how those clock measurements relate to underlying processes of aging.
Using a large proteomic cohort in the UK Biobank, we aimed to develop a proteomic aging clock for all-cause mortality risk as a proxy of biological age (BA). Participants in the UK Biobank Pharma Proteomics Project were included with ages between 39 and 70 years (n = 53,021). We developed a proteomic aging clock (PAC) for all-cause mortality risk as a surrogate of BA using a combination of Least Absolute Shrinkage and Selection Operator (LASSO) penalized Cox regression and Gompertz proportional hazards models. The validation for PAC included assessing its age-adjusted associations with, and predictions for all-cause mortality and 18 incident diseases, and head-to-head comparisons with two biological age measures (PhenoAge and BioAge) and leukocyte telomere length (LTL). Additionally, a functional analysis was performed to identify gene sets and tissues enriched with genes associated with BA deviation, based on different BA measures.
The Spearman correlation between PAC proteomic age and chronological age was 0.76. 10.9% of the combined training and test samples died during a mean follow-up of 13.3 years, with the mean age at death 70.1 years. PAC proteomic age, after controlling for age and other covariates, showed stronger associations than PhenoAge, BioAge, and LTL, with mortality and multiple incident diseases in the test set sample and in disease-free participants, such as mortality, heart failure, pneumonia, delirium, Chronic Obstructive Pulmonary Disease (COPD), and dementia. Additionally, PAC proteomic age showed higher predictive power for the conditions above compared to chronological age, PhenoAge, and BioAge. Proteins associated with PAC proteomic age deviation (from chronological age) are enriched in various hallmarks of biological aging, including immunoinflammatory responses, cellular senescence, extracellular matrix remodeling, cellular response to stressors, and vascular biology.