A groundbreaking study published in npj Metabolic Health and Disease (Nature Portfolio) has introduced a new “metabolic clock” that can predict biological age, uncover disease-specific metabolic signatures, and support early detection of health risks. Unlike chronological age, which measures only the passage of years, this tool focuses on biological processes, offering a more accurate reflection of an individual’s actual health status.
The clock was developed from data provided by more than 13,500 participants in the AKRIBEA cohort, a large-scale health study conducted in the Basque Country in collaboration with the Mondragón Corporation. In total, the final dataset included around 20,000 people spanning a wide age range, making it one of the most comprehensive efforts to date. By building on this robust foundation, the research team has created a model with the power to transform how ageing and disease are understood.
At the heart of the innovation is nuclear magnetic resonance (NMR) metabolomics, a technology that examines small molecules circulating in the blood. Using machine-learning algorithms, the researchers developed a system capable of predicting biological age with striking accuracy. “Our goal was to obtain an independent measure of age, beyond the information on a passport,” explains Dr Óscar Millet, who led the work at CIC bioGUNE, part of the Basque Research and Technology Alliance (BRTA). “The significance of this lies in the ability to detect discrepancies between chronological and metabolic age, which may act as early indicators of disease.”
The researchers tested the clock by analysing samples from individuals with a variety of conditions. In men with prostate cancer, the average metabolic age was nearly five years older than their actual age, while in patients with fatty liver disease (MASLD), the difference rose to more than 14 years. These results suggest that “metabolic distortion” could serve as a valuable marker for identifying diseases at an early stage, potentially long before they are apparent through conventional diagnostic methods.
Beyond its predictive power, the platform is capable of estimating more than 25 standard clinical parameters from a single blood sample, including measures of inflammation and kidney function. This ability to provide a broader, more integrated picture of a person’s health could support clinicians in tailoring treatment strategies and monitoring patients more effectively. As Dr Millet notes, “It is remarkable how much information is already encoded within a serum NMR spectrum, waiting to be unlocked.”
By offering a reliable and non-invasive means of measuring biological age and disease risk, the metabolic clock paves the way for more personalised and preventative healthcare. It holds the promise not only of spotting illnesses earlier but also of helping individuals and doctors to monitor the pace of ageing, opening up new opportunities to maintain health and quality of life across the lifespan.
More information: José M. Mato et al, Metabolomic-based aging clocks, npj Metabolic Health and Disease. DOI: 10.1007/s00394-023-03123-x
Journal information: npj Metabolic Health and Disease Provided by CIC bioGUNE
