Simple Blood Test Spots Disease Through Metabolic Distortion

Aging and disease progression are difficult to detect early with current clinical tools, leaving many health issues unnoticed until symptoms appear. Biological age, rather than chronological age, provides a more accurate reflection of overall health, but measuring it has been a challenge. A new approach now offers a simple blood-based method to predict biological age, detect early signs of disease, and stratify patient risk.

Scientists at CIC bioGUNE (Derio, Spain) have developed a metabolic aging clock using nuclear magnetic resonance (NMR) metabolomics. This technology analyzes small molecules in blood, while machine learning builds models to predict biological age. The clock was created using data from more than 13,500 participants in the AKRIBEA cohort in the Basque Country, resulting in a final dataset of approximately 20,000 individuals across a wide age range.

The study, published in npj Metabolic Health and Disease, found that discrepancies between chronological and metabolic age revealed early disease markers. In prostate cancer patients, metabolic age was nearly five years older than actual age, while in fatty liver disease, the difference exceeded 14 years. Subtypes of fatty liver disease also showed distinct aging patterns, which standard clinical tests struggle to identify.

In addition to predicting aging, the platform can estimate over 25 standard clinical parameters, including inflammation and kidney function, from the same blood sample. This enhances precision medicine by providing a comprehensive health profile through a single test. With more validation, the tool could be expanded across healthcare systems, offering early disease detection, personalized monitoring, and improved management of chronic conditions.

“The idea is to capture as much information as possible from existing clinical tests,” said Dr. Óscar Millet, head of the Precision Medicine and Metabolism Laboratory. “It is remarkable how much of this information is already encoded within a serum NMR spectrum.”

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