Proteomic Aging Clocks for Specific Organs
Aging clocks can be constructed from any sufficiently large collection of biological data using machine learning techniques. Here, researchers report on the production of aging clocks for specific organs from the circulating proteome assessed in blood samples. New clocks are published constantly; it remains to be seen as to which of these many clocks will become adopted and used more broadly. The primary challenge remains to develop an understanding of how the specific age-related changes that make up a given clock relate to the underlying causes of aging. It is difficult to rely upon a clock to speed up the development of interventions to slow or reverse aging without this knowledge. Just because a clock works fairly well in normally aged individuals doesn't mean that it will accurately reflect all of the contributions to degenerative aging. The clock may be insensitive or overly sensitive to any one specific contribution that is the target of therapy, and thus results will be misleading.
Recent studies show that human organs age at different rates similar to what has been reported in animals, which suggests a need for organ-specific measures of biological age. Previously developed organ age estimates include those developed from clinical metrics of organ function (glomerular filtration rate, blood pressure, etc), clinical blood chemistry, brain MRI scans, immune cell DNA methylation profiles, and the levels of organ-specific proteins in blood plasma. Many questions regarding the reproducibility and utility of organ age estimates remain. For example, it is unclear the extent to which organ age estimates are stable across cohorts and longitudinal sampling, are sensitive to organ-specific diseases and modifiable lifestyle choices, and whether they predict mortality independent of each other and established aging biomarkers. Furthermore, it is unclear which organs are key to longevity in humans.
Organ-derived plasma protein signatures derived from aptamer protein arrays track organ-specific aging, disease, and mortality in humans, but the robustness and clinical utility of these models and their biological underpinnings remain unknown. Here, we estimate biological age of 11 organs from 44,526 individuals in the UK Biobank using an antibody-based proteomics platform to model disease and mortality risk. Organ age estimates are associated with future onset of heart failure (heart age HR=1.83), chronic obstructive pulmonary disease (lung age HR=1.39), type II diabetes (kidney age HR=1.58), and Alzheimer's disease (brain age HR=1.81) and sensitive to lifestyle factors such as smoking and exercise, hormone replacement therapy, or supplements.
Remarkably, the accrual of aged organs progressively increases mortality risk while a youthful brain and immune system are uniquely associated with disease-free longevity. These findings support the use of plasma proteins for monitoring organ health and the efficacy of drugs targeting organ aging disease.