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Researchers used blood metabolite data from 18,476 people aged 1 to 96 to develop measures of metabolomic aging and age-related mortality risk. Higher scores were associated with chronic disease and death, but the observational findings do not show that specific foods or habits cause biological aging.
Researchers analyzing blood samples from 18,476 people aged 1 to 96 developed metabolite-based measures associated with chronic disease and mortality risk, reporting that the measures provided some additional risk discrimination beyond several standard health assessments. The study, published in Science Translational Medicine, suggests blood chemistry may add information to a person’s calendar age, but does not establish that the identified patterns cause disease or predict an individual’s future with certainty.
The team studied participants in the Shanghai Pudong Cohort, across six centers in Shanghai, China. Plasma samples were analyzed using nano-assisted laser desorption/ionization mass spectrometry. Researchers 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 participants with existing diseases.
For healthy participants, the researchers calculated metabolomic age acceleration, or the difference between metabolite-estimated age and actual age. For people with health conditions, they built a separate age-deviation measure based on how metabolite-associated mortality risk compared with what was expected for a person’s age. A set of 56 age-related metabolites retained most of the age-predictive information found across 232 measured metabolite features.
Higher metabolomic age acceleration was associated with greater mortality risk even after accounting for established health measures. Participants with higher values were also more likely to develop conditions including diabetes, hypertension and fatty liver disease. The researchers reported that both measures modestly improved the distinction between people at higher and lower mortality risk when added to assessments such as frailty, cardiovascular health scores and routine blood biomarkers.
What Blood-Based Aging Scores Add
The findings matter because chronological age alone does not capture every difference in health risk. A measure based on circulating metabolites could, if validated and made practical, help researchers or clinicians identify patterns associated with elevated risk and refine population-level risk assessment. In this study, the added predictive value was modest, and the measures were studied in a specific cohort rather than established as routine clinical tests.
The results also connect metabolite patterns with a range of health and environmental factors. Higher age-acceleration scores were associated with active and secondhand smoking, shift-work history, hair dye use and a parental history of premature cardiovascular disease. Associations were also reported with dietary patterns and exercise. These links may guide further research, but they do not show that any one exposure or behavior caused a person’s score or health outcome.
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How Researchers Measured Metabolomic Age
Metabolomics examines small molecules produced or used by the body. Because these molecules circulate in blood and reflect multiple biological processes, researchers are investigating whether their patterns can capture aspects of aging that differ from a person’s age in years. The study analyzed baseline information collected in 2019; chronic disease status was reassessed in 2023, and deaths were tracked through January 2025.
Some participants completed repeat surveys and updated health assessments in 2023, and 1,500 underwent repeat blood metabolite profiling. In those repeated analyses, increased physical activity and reduced fried-food intake were linked to 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 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 data. The report describes this as testing the approach with other types of biological data; it does not mean the Shanghai findings were independently confirmed as a clinical tool in those populations.
“The study reports that a 56-metabolite panel retained most of the age-predictive information provided by all 232 metabolite features.”
— The study authors
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Limits of the Risk Associations
The study found associations, not proof of cause and effect. It cannot show that changing a specific food, exposure or activity level will reduce biological aging or prevent illness. Lifestyle and family-history information came from standardized questionnaires where available, and the report does not establish that every possible factor affecting health was captured.
The researchers reported modest gains in mortality-risk discrimination, but the study does not establish how accurately the measures would work for a particular patient or across populations beyond the cohort studied. It also does not show that the measures are ready for routine medical use. The reported research included follow-up through January 2025; longer-term results and further validation remain outstanding.
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Validation Before Clinical Use
The next step is further testing in independent populations to determine whether the measures remain informative across different groups and health settings. Researchers would also need to establish how much the scores improve decisions beyond standard assessments and whether repeated measurements reliably track changes over time.
For now, the study presents metabolomic aging measures as potential tools for future population-level risk assessment, not as diagnoses or personal forecasts. It does not support changing treatment or diet based on a metabolomic age score; clinical decisions should rely on qualified health professionals and established assessments.
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Key Questions
What does metabolomic age mean?
It is an estimate based on patterns of small molecules, or metabolites, measured in blood. In this study, researchers compared that estimate with a participant’s actual age to calculate metabolomic age acceleration.
Does the study show that certain foods cause faster aging?
No. The researchers found associations between some reported dietary patterns and metabolomic age measures, but the observational study cannot establish cause and effect.
Can this blood test predict how long an individual will live?
The study does not establish a precise personal forecast. Its measures were associated with mortality risk in the cohort and modestly improved risk discrimination alongside other assessments; they are not shown here to predict an individual’s lifespan.
Is metabolomic age testing ready for routine medical care?
The report describes research measures, not an established routine clinical test. Further validation is needed to determine how well they work across populations and whether they improve health decisions.
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