Researchers in Shanghai have built a blood-based ageing clock that was linked to chronic disease and predicted death more closely than several established risk measures. The study, published in Science Translational Medicine on 14 October 2026 by Linlin Cao and colleagues at Shanghai Jiao Tong University, profiled plasma samples from 18,476 people aged 1 to 96 recruited across six centres in China. The team identified 56 metabolites that shift with age, spanning collagen turnover, amino acid metabolism, kidney and muscle turnover and host-microbiome co-metabolism, then used ensemble machine learning to produce a metabolomic age and compared it with each participant's actual age. People in the top quarter of that gap had higher rates of fatty liver disease, high blood pressure and diabetes, and higher all-cause mortality, than those in the bottom quarter. The work was funded by Chinese public research bodies, with no industry sponsor listed.
What the numbers showed
The design is observational, so it records associations rather than cause and effect. A faster metabolomic clock was associated with smoking, shift work and a diet the authors classed as unhealthy, while a slower one was associated with mixed grain intake and exercise. Tracking change within the same people, the authors reported that more physical activity and less fried food went with a favourable shift, and that new second-hand smoke exposure, more preserved food, rising depressive symptoms and moving into the overweight or obese range went with an unfavourable one. The measure also discriminated mortality better than the frailty index and the American Heart Association's Life's Essential 8.
What this means in practice
The clock exists only as a research model. Direct-to-consumer biological age products already use similar language, and the paper does not validate any of them, because the model was built and tested inside one research cohort using laboratory mass spectrometry. The authors describe it as a framework for prevention research, not individual screening. They also state that the cohort was primarily Chinese and that validation in populations of other ethnic and geographic backgrounds is still needed, which is a direct limit on reading these numbers across to a UK population. The authors have made their age prediction framework publicly available through a web interface. The study tested no supplement, diet or other intervention.
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