Recent research featured in Nature Medicine highlights a study by researchers who have created a blood test that analyses more than 200 proteins to determine a person’s biological age.
The research indicates that the test, which was developed using machine learning techniques, can forecast an individual’s risk of developing 18 major age-related diseases and their likelihood of premature death from any cause. This study enhances the understanding of the biological processes underlying various age-related conditions and sheds light on how genetics and environmental factors impact the aging process.
The proteome encompasses all the proteins produced by an organism. The research aimed to develop a "proteomic aging clock" to predict the risk of prevalent age-related diseases.
By utilizing proteomics data, scientists can more precisely evaluate ageing by comparing an individual’s biological functions to their chronological age. Unlike most biological ageing clocks that depend on DNA methylation, analyzing protein levels could provide more immediate insight into ageing mechanisms, particularly since proteins are central to drug development.
Researchers analyzed data from 45,441 participants aged 40 to 70 and identified 204 proteins that can accurately predict chronological age. From this larger model, they determined a subset of 20 age-related proteins that maintained 91% of the accuracy in age prediction.
Their findings revealed that proteomic aging assessments are associated with the onset of 18 major chronic diseases, including heart, liver, kidney, and lung diseases, diabetes, neurodegenerative disorders like Alzheimer’s, cancer, as well as multimorbidity and overall mortality risk. Furthermore, proteomic aging was found to correlate with various age-related biological, physical, and cognitive indicators, such as telomere length, frailty index, and performance on cognitive tests.
Reference: Argentieri, M.A., Xiao, S., Bennett, D. et al. Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations. Nat Med (2024). https://doi.org/10.1038/s41591-024-03164-7.
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