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Researchers used blood metabolite measurements from 18,476 people aged 1 to 96 to develop metrics of metabolomic aging and mortality risk. The measures were associated with chronic disease and death risk, and added modest predictive information beyond standard health measures; they are not yet proven clinical tools.
Researchers analyzing blood samples from 18,476 people aged 1 to 96 developed two measures of metabolomic aging and found that higher-risk readings were associated with chronic disease and death. The study, published in Science Translational Medicine, suggests blood chemistry may add information to age-based health assessments, but the measures are research tools, not established diagnoses.
According to the study in Science Translational Medicine, researchers used plasma samples from participants in the Shanghai Pudong Cohort, recruited across six centers in Shanghai. They developed a metabolomic age model using 5,252 healthy participants, tested it in another 5,456 healthy participants, and examined a separate group of 7,768 people with existing diseases. The team used nano-assisted laser desorption/ionization mass spectrometry to measure metabolites in blood plasma.
For the healthy group, the researchers calculated metabolomic age acceleration, or how far a person’s metabolite-based age estimate differed from their actual age. For participants with health conditions, they created an age-deviation measure based on mortality risk relative to what would be expected for a person’s age. The paper reports that chronic disease was reassessed in 2023, while mortality was tracked through January 2025.
The study authors report that a panel of 56 metabolites retained most of the age-predictive information found across 232 metabolite features. Higher metabolomic age acceleration was associated with later development of conditions including diabetes, hypertension and fatty liver disease, as well as higher all-cause mortality risk. The authors also report that both measures modestly improved mortality-risk discrimination when added to assessments such as frailty, cardiovascular health scores and routine blood biomarkers.
What Blood-Based Aging Measures Add
The findings address a limitation of using chronological age alone to describe health risk: people of the same age can have different health profiles. A measure based on many circulating metabolites could, if validated and made practical, help researchers identify differences in risk that routine measures may miss.
The study reports associations, not a test ready for routine use. The improvement in mortality-risk discrimination was modest, and the results do not show that a particular metabolite score can predict an individual’s future with certainty. Nor do they establish that changing a score will prevent disease or extend life. The work is relevant as a possible avenue for population research and future risk stratification, not as a substitute for clinical assessment.
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How the Cohort Was Followed
According to the study report, baseline assessments began in 2019, including questionnaires on factors such as diet, lifestyle, sleep, mental health and family medical history where those data were available. In 2023, participants were asked about health again and chronic disease status was updated. Of those completing repeat surveys, 1,500 also had repeat plasma metabolomic profiling.
The researchers also tested their ensemble-learning approach using data from the UK Biobank, the U.S. National Health and Nutrition Examination Survey and human gut microbiome datasets. The report describes these tests as applications of the approach to other kinds of biological data; they do not replace the need for further validation of the blood-based measures in different populations and clinical settings.
Limits of the Findings
The study authors characterize their work as observational, so it cannot establish that foods, exercise, smoking exposure or other associated factors directly cause changes in biological aging. The paper reports that higher metabolomic age acceleration was associated with active and secondhand smoking, shift-work history, hair dye use and parental history of premature cardiovascular disease. It also reports associations with greater consumption of several foods and lower values linked with mixed grains and exercise. These patterns may be affected by other differences between participants.
Some participants had repeat testing, but the reported follow-up and observational design do not establish whether a change in an individual’s score reliably predicts a later health outcome. The report does not establish how well the measures would perform across populations beyond the Shanghai cohort, or what thresholds might be useful in clinical care. The results do not make the measures a diagnosis or a basis for changing treatment.
Validation Before Clinical Use
Further research would need to test the measures in independent and more diverse groups, assess how consistently they perform over time, and determine whether they add useful information in real-world clinical settings. Researchers would also need to establish how the scores should be interpreted and whether using them improves health decisions or outcomes.
For now, the study’s next step is further evaluation rather than immediate adoption as a routine blood test. The findings may guide research into metabolic patterns associated with aging, but people should not treat the reported food and lifestyle associations as proof of a direct effect on biological age.
Key Questions
What does metabolomic age acceleration mean?
It describes how much a person’s estimated age based on blood metabolite patterns differs from their chronological age. In this study, higher values were associated with greater disease and mortality risk; the measure is not a diagnosis.
Does the study show that a blood test can predict how long someone will live?
No. The researchers found associations between their measures and mortality risk across a cohort. The measures improved risk discrimination only modestly and do not provide a certain forecast for an individual.
Did particular foods or exercise cause changes in the aging measures?
The study found observational associations between some reported habits and metabolomic age acceleration. Its design does not establish cause and effect, so it cannot show that a specific food or activity directly changes biological aging.
When could this become a routine clinical test?
The study does not establish a timeline. The measures need further validation in independent populations and clinical settings, along with evidence that using them improves health decisions or outcomes.
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