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Researchers analyzed blood metabolite patterns from 18,476 people aged 1 to 96 and developed measures that were associated with chronic disease and mortality risk. The measures added modest risk-discrimination value beyond several existing health assessments, but the observational study does not show that the blood patterns cause disease or predict an individual’s future with certainty.
Researchers studying 18,476 people aged 1 to 96 developed blood-based measures of metabolic aging that were associated with chronic disease and risk of death, according to a study published in Science Translational Medicine. The findings suggest that patterns of chemicals in plasma may add information to health-risk assessments beyond chronological age, but they do not establish that the measures can diagnose disease or determine how long an individual will live.
The research team analyzed plasma samples from participants in the Shanghai Pudong Cohort, recruited across six centers in Shanghai. Using a mass-spectrometry technique, they measured metabolite patterns and built an age-prediction model using 5,252 healthy participants. They tested it in a separate healthy group of 5,456 people and examined disease and mortality associations in another 7,768 participants.
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 existing conditions, they developed a separate age-deviation measure tied to mortality risk. Follow-up included reassessments of chronic disease in 2023 and mortality tracking through January 2025.
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 increased risk of conditions including diabetes, hypertension and fatty liver disease, as well as higher risk of death from any cause. The study also identified differences in metabolite patterns linked to several biological pathways, including amino-acid metabolism.
Blood Measures and Risk Assessment
The results point to a possible way of adding biological information to assessments that currently rely on factors such as age, medical history and routine blood tests. In the study, the metabolomic measures improved the ability to distinguish people at higher and lower mortality risk beyond frailty assessments, cardiovascular health scores and routine biomarkers. The improvement was described as modest, not as a replacement for those assessments.
That distinction matters for patients and health systems: a blood-based measure could eventually help identify people who may benefit from closer monitoring, but this study does not establish that using it improves care or outcomes. It also does not provide a validated individual forecast. The researchers present the approach as having potential for population-level risk assessment, subject to further testing.
The study reported associations between higher metabolomic age acceleration and factors including active and secondhand smoking, shift work history, and parental history of premature cardiovascular disease. It also found associations with reported diet and exercise patterns. These results describe patterns within the cohort; they do not show that any one behavior caused a person’s metabolite age to rise or fall.
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How the Cohort Was Studied
Chronological age records time lived, while biological-aging measures aim to capture changes in the body that may relate to health. Metabolites are small molecules found in blood and other tissues; their levels can reflect multiple processes, including diet and the body’s metabolism. The study’s aim was to examine how such patterns vary over a wide age range and whether they correspond with health outcomes.
Baseline assessments took place in 2019. Researchers reassessed chronic disease status in 2023, when thousands of participants completed follow-up surveys; 1,500 also had repeat plasma profiling. The study reported associations between increased physical activity or reduced fried-food intake and more favorable changes in metabolomic age acceleration. Among participants with hypertension, those whose blood pressure was below 140/90 mmHg by 2023 had more favorable changes in the mortality-linked age-deviation measure than those whose hypertension remained uncontrolled.
The authors also retrained their ensemble approach using data from the UK Biobank, the U.S. National Health and Nutrition Examination Survey and human gut microbiome data. These additional analyses tested the approach with other types of biological information; they do not by themselves establish that the Shanghai findings apply uniformly to other populations.
“The findings highlight the potential of plasma metabolomic profiling for future population-level risk assessment and improved risk stratification.”
— The study authors, as summarized in the supplied report
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Limits of the Aging Measures
The study is observational, so associations between metabolite patterns, lifestyle factors and health outcomes cannot establish cause and effect. The findings do not show that changing a particular food or behavior will alter biological aging or prevent disease. Nor do they show that the measures can diagnose a condition or accurately predict an individual’s lifespan.
The reported gain in mortality-risk discrimination was modest. The supplied study summary does not give enough detail to assess the size of that gain numerically, the best clinical thresholds for using the measures, or whether applying them would change treatment decisions. It also remains unclear how well the approach performs across populations beyond the cohorts and datasets tested.
Metabolite levels can reflect multiple influences, and the study included participants with different health statuses and ages. Further work would be needed to establish how stable the measures are over time, how they should be interpreted for a particular person and whether they provide useful information in routine clinical care.
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Validation Before Clinical Use
The next step is further validation of the measures in independent and varied populations, along with research on whether they improve decisions when added to established health assessments. Studies would also need to clarify how clinicians should interpret a result and whether using it leads to better outcomes, rather than simply identifying statistical differences in risk.
The supplied report does not identify a planned clinical trial or a timeline for routine use. For now, the findings are evidence of an association in a large cohort, not a new test shown to guide care. Readers should not use the reported lifestyle links or a hypothetical metabolomic age score as a substitute for medical assessment.
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Key Questions
What did the researchers measure?
They analyzed metabolites in plasma, the liquid portion of blood, and used patterns in those measurements to estimate metabolomic age or an age-related mortality-risk deviation.
Does the study show that the blood test predicts an individual’s lifespan?
No. The study found associations with mortality risk across groups. It does not establish a certain or clinically validated prediction of how long any individual will live.
Did diet or exercise cause changes in biological aging?
The study reported associations between some reported habits and metabolomic measures, including more favorable changes linked with increased activity and reduced fried-food intake. Because the research was observational, it cannot establish that those changes caused the differences.
Can this measure be used in routine medical care now?
The supplied report describes potential for future risk assessment, not an established clinical test. Further validation and evidence that using the measure improves care would be needed.
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