Human organs age at different rates, and these variations may be linked to future disease risks. Scientists at Stanford School of Medicine have developed a method to estimate the relative biological age of individual organs based on proteins found in the blood.
Researchers created an algorithm that assesses the relative biological age of 11 organs and systems by analyzing the protein composition of blood plasma. The calculated indicators were found to correlate with diagnoses that participants received in subsequent years. The findings were published in the journal Nature Medicine.
Chronological age does not fully reflect the body’s condition. In two individuals of the same age, protein markers in the brain, heart, or kidneys can differ significantly, and the organs of a single person may age at varying speeds. However, the estimated age remains a statistical approximation rather than a direct measurement of tissue health.
Lead author Tony Wyss-Coray stated that the method allows for evaluating an organ’s relative age and linking it to the likelihood of future illnesses. On average, researchers tracked participants’ health for about ten years, with a maximum observation period extending to 17 years. It is not yet certain how accurately the algorithm can predict the risk for a specific patient in a routine clinical setting.
The study utilized data from 44,498 participants in the UK Biobank, aged 40 to 70, whose health was monitored for up to 17 years. Levels of 2,916 proteins were measured in plasma samples, and algorithms were trained to estimate the relative age of 11 organs and systems. These included the brain, heart, lungs, liver, kidneys, pancreas, muscles, arteries, intestines, immune system, and adipose tissue.
Approximately one in three participants had at least one organ classified as unusually old or unusually young. Scientists defined such cases as indicators that deviated from the average for their age group by at least one and a half standard deviations.
The strongest link to future diseases was observed with the calculated age of the brain. Participants with an unusually old brain had a 3.1 times higher relative risk of Alzheimer’s disease compared to those with average values. The gap widened to about 12 times when comparing the two extreme categories—unusually old versus unusually young brains.
An unusually old brain was also associated with a 182% increase in the relative risk of death over a period of about 15 years. Conversely, participants with an unusually young brain had a 40% lower relative risk. These figures reflect statistical associations and do not prove that the brain’s age directly determined lifespan.
Wyss-Coray described the brain as a kind of “longevity guardian.” Among all calculated indicators, its biological age showed the strongest connection to overall mortality. However, the study does not determine whether a young brain protects other organs or merely reflects a generally healthier state of the body.
Similar connections were found for other organs. An unusually old heart was associated with an increased risk of heart failure and atrial fibrillation, while older lungs were linked to chronic obstructive pulmonary disease. Age indicators for the kidneys, pancreas, and other systems also correlated with diseases typical of those organs.
The authors hope that in the future, this method could help identify individuals at higher risk of disease and test whether medications or lifestyle changes influence the calculated age of specific organs. However, the study has not yet demonstrated that decisions based on such analysis improve patient health or prevent disease development.
Currently, the method is only available for scientific research. Stanford has licensed the associated technologies to companies Teal Rise and Vero Bioscience, co-founded by Wyss-Coray. According to his estimate, a commercial test could become available within two to three years. The developers plan to initially focus on the brain, heart, and immune system to reduce analysis costs and more precisely link indicators to specific diseases.
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