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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. Higher values were associated with chronic disease and death risk, with modest improvements in risk discrimination; the observational study does not show that particular foods or lifestyle changes cause biological aging.
Researchers analyzing blood samples from 18,476 people aged 1 to 96 developed metabolite-based measures of aging that were associated with chronic disease and mortality risk. The study, published in Science Translational Medicine, suggests blood chemistry may add information to conventional health measures, but it does not establish that the measured patterns cause illness or predict an individual’s future with certainty.
According to the study in Science Translational Medicine, 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 used data from 5,252 healthy participants to develop a model estimating metabolomic age, then tested it in a separate healthy group of 5,456 and examined it in a disease cohort of 7,768.
The researchers calculated metabolomic age acceleration for healthy participants, or how far the model’s age estimate differed from a person’s chronological age. For people with existing health conditions, they created an age-deviation measure based on mortality risk relative to what would be expected for age. The study reports that the team tracked chronic disease status through 2023 and mortality through January 2025.
The study reports that a panel of 56 age-related metabolites retained most of the age-predictive information in the full set of 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 risk of death from any cause. The authors found that the measures improved mortality-risk discrimination beyond several existing assessments, but described the improvement as modest.
How Blood Measures Could Add Risk Information
The study authors say the findings point to a possible way of identifying health risks that are not fully captured by chronological age or standard clinical measures. They reported that their metabolite-based measures were associated with overall health, multiple-system disease and the burden of having several conditions. If further validated, such measures could help researchers or health systems refine population-level risk assessment.
The results do not establish that a metabolomic age score should be used to diagnose disease or guide care. The authors reported modest gains in distinguishing higher- from lower-risk participants, and the study’s associations cannot show that changing a specific behavior will lower a person’s biological age or prevent illness. The work is better understood as research into an additional risk signal than as a ready-made personal health test.
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How the Shanghai Cohort Was Studied
As described in the study, researchers collected baseline assessments in 2019, including questionnaire information on diet, lifestyle, sleep, mental health and family history where available. Chronic disease status was reassessed in 2023. That year, thousands of participants completed repeat surveys and updated health assessments; 1,500 people also had repeat plasma metabolomic profiling.
The study authors say they used several modeling and statistical approaches to select metabolites and assess the age measures. They also retrained their ensemble approach using data from the UK Biobank, the U.S. National Health and Nutrition Examination Survey and human gut microbiome datasets. This testing explored whether the approach could be applied to other kinds of biological data; it does not, by itself, establish clinical usefulness in other populations.
The researchers reported associations between higher metabolomic age acceleration and factors including active and secondhand smoking, shift-work history, hair dye use and parental history of premature cardiovascular disease. They also reported associations with diet patterns and exercise. The study linked several metabolic pathways with aging, including pathways involving threonine, serine, glycine, cysteine and methionine.
Limits of the Aging Associations
Because the study is observational, it cannot determine whether specific foods, exercise or exposures directly change biological aging. The authors’ reported links between higher metabolomic age acceleration and consumption of organ meat, sugar, processed meat, seafood and fried foods do not prove that those foods caused the higher scores. Likewise, associations with mixed grains or exercise do not demonstrate a protective effect.
The researchers reported that increased physical activity and reduced fried-food intake were associated with more favorable changes in repeat metabolomic age measures. They also reported that participants with hypertension whose blood pressure was below 140/90 mmHg in 2023 had more favorable changes in the mortality-linked age-deviation measure than participants with uncontrolled hypertension. These comparisons remain observational. The study does not establish whether the measures are accurate enough for routine clinical decisions or how well they would perform across populations beyond those studied.
Validation Before Clinical Use
The study findings support further evaluation of metabolomic measures in other groups and settings, including whether they add useful information to routine health assessments. As the researchers note, further work would need to establish how reliably the scores work across populations and whether their modest improvement in risk discrimination leads to better health decisions or outcomes.
For now, the study does not provide a basis for treating a metabolomic age score as a diagnosis or for making medical decisions from it. Its future significance depends on replication, validation and clinical testing; those steps are not established by the cohort associations reported in the paper.
Key Questions
What does metabolomic age mean?
It is an estimate based on patterns of metabolites in blood plasma. In this study, metabolomic age acceleration described how far that estimate differed from a participant’s chronological age.
Did the study show that blood tests can predict how long someone will live?
No. The researchers found associations between their measures and mortality risk in a cohort, but the results do not provide a certain prediction for an individual. The study reported only modest improvements in distinguishing risk beyond other assessments.
Do the diet findings prove that particular foods speed up aging?
No. The links between food intake and metabolomic age acceleration were observational. They cannot show that a particular food caused a change in the measure or in health outcomes.
Can people use this measure to make medical decisions now?
The study does not establish the measure as a routine clinical test or diagnostic tool. Its accuracy and usefulness for individual care require further evaluation; patients should discuss medical decisions with a qualified health professional.
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