They analysed nearly 3,000 proteins in blood samples from participants in the UK Biobank study to develop a machine learning model that uses 204 proteins to estimate a person’s biological age. The researchers compared the participants’ chronological age with their biological age based on blood proteins to calculate the ‘protein age gap’ as a biological indicator of how fast a person is ageing. For some people, their biological ‘clock’ is ticking faster than others and they age faster. In order to gain better insight into who is ageing more quickly or more slowly, researchers have previously developed biological age clocks using various biological and clinical indicators. The study, 'Proteomic aging signatures predict disease risk and mortality across diverse populations', is published in Nature Medicine.