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Researchers used blood metabolite data from 18,476 people aged 1 to 96 to develop measures of metabolomic aging and mortality-related age deviation. The measures were associated with chronic disease and death risk and modestly improved risk discrimination beyond established measures; the observational study does not show that changing diet or lifestyle alters aging.
Researchers analyzed plasma samples from 18,476 people aged 1 to 96 in Shanghai and developed blood-based measures associated with chronic disease and mortality risk. The study, published in Science Translational Medicine, suggests metabolite patterns could add information to population-level risk assessment, but it does not establish that the patterns cause illness or predict an individual’s future with certainty.
The research team used nano-assisted laser desorption/ionization mass spectrometry to profile metabolites in plasma from participants in the Shanghai Pudong Cohort. They developed a metabolomic age model using 5,252 healthy participants, tested it in a separate healthy group of 5,456, and examined a disease cohort of 7,768 people. The reported mean ages for the healthy validation and disease groups were 55 and 68, respectively.
For healthy participants, the researchers calculated metabolomic age acceleration, a measure of how much an estimate based on blood metabolites differed from a person’s chronological age. For people with existing health conditions, they developed an age-deviation measure based on how metabolite-related mortality risk compared with what would be expected for someone of that age.
A panel of 56 metabolites retained most of the age-predictive information in the full set of 232 features. Higher metabolomic age acceleration was associated with a greater likelihood of developing conditions including diabetes, hypertension and fatty liver disease, as well as higher risk of death from any cause. The measures added some ability to distinguish mortality risk beyond frailty assessments, cardiovascular health scores and routine blood biomarkers, though the improvement was described as modest.
Blood Profiles Add Risk Information
Chronological age is a simple way to describe how long a person has lived, but it does not capture every difference in health or disease risk. The study’s findings suggest that metabolite patterns may provide another layer of information about aging-related health, including for people whose measured biological profile differs from expectations for their age.
That could matter for research and, if validated, future population-level risk assessment. The results do not show that the measures are ready to guide individual medical decisions, nor that they can replace clinical evaluation. Their added predictive value was modest, and the study did not test whether using the measures to change care improves health outcomes.
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How the Shanghai Study Was Run
Baseline assessments began in 2019, and chronic disease status was reassessed in 2023. Mortality was tracked through January 2025. In 2023, 3,745 people in the healthy cohort and 4,658 in the disease cohort completed repeat surveys and updated disease assessments; 1,500 participants also had repeat metabolomic profiling.
Questionnaires gathered information on diet, lifestyle, sleep, mental health and family medical history where available. The researchers also retrained their ensemble approach using data from the UK Biobank, the U.S. National Health and Nutrition Examination Survey and human gut microbiome research. That testing examined whether the approach could be applied to other types of biological data; it does not by itself establish that the metabolite measures are clinically validated across populations.
The study reported associations between higher metabolomic age acceleration and factors including active or secondhand smoke exposure, shift work history and parental history of premature cardiovascular disease. Some dietary patterns and activity levels were also associated with the measure. These are observational associations, not proof that any listed exposure or food directly changes biological aging.
Limits of the Risk Estimates
The findings show relationships in the studied cohorts, not cause and effect. The researchers’ results do not establish that changing a particular food, exercise routine or exposure will lower a person’s biological age or prevent disease. Although repeat measurements linked increased physical activity and reduced fried-food intake with more favorable changes in metabolomic age acceleration, those observations cannot establish that the changes caused the difference.
It is also not clear from the reported findings how accurately the measures would perform in populations outside the Shanghai cohort, or how much they would improve decisions for an individual patient. The report describes modest improvement in mortality-risk discrimination but does not provide a basis for treating the measures as a diagnosis. The study’s authors and further research will need to clarify validation, practical use and performance across groups.
Validation Before Clinical Use
The next steps are further testing of the measures in independent populations and evaluation of whether they add useful information in real-world health settings. Longer-term and repeated follow-up could help researchers determine how stable the measures are and whether changes track meaningful health outcomes.
For now, the study supports continued research into blood metabolomics as a way to assess aging-related risk. It does not establish a recommended blood test or a treatment strategy. Clinical use would require additional validation, including evidence that the measures are reliable and improve health decisions beyond existing assessments.
Key Questions
What does metabolomic age mean in this study?
It is an estimate based on patterns of metabolites measured in plasma. Researchers compared this estimate with a person’s chronological age to calculate metabolomic age acceleration.
Does the study show that blood metabolites cause disease?
No. The study found associations between metabolomic measures and chronic disease and mortality risk. Its observational design does not establish cause and effect.
Can changing diet or exercise lower biological age?
The study linked some reported lifestyle changes with more favorable changes in a metabolomic measure, but it does not prove those changes caused the result or prevent disease. It does not provide a treatment recommendation.
Is this a validated test for individuals?
The report describes research measures and modest added ability to distinguish mortality risk. It does not establish that the measures are ready for individual diagnosis or routine clinical use.
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