Circulating plasma proteins play key roles in human health and can potentially be used to measure biological age, allowing risk prediction for age-related diseases, multimorbidity, and mortality.
The researchers developed a machine learning model that uses blood proteomic information to estimate a proteomic age clock in 45,441 participants (age: 40-70 years old) from the UK Biobank, and the results were further validated with participants from China and Finnland biobanks. The researchers identified 204 aging-related proteins to predict chronological age and further identified a small set of only 20 proteins that showed similar prediction accuracy. Results found that proteomic aging was associated with the incidence of 18 major chronic diseases as well as multimorbidity and all-cause mortality risk.
The proteomic age clock, which was predicted from the blood test of only 20 proteins, may have the potential to be a biomarker for preventive interventions targeting aging and multimorbidity.
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