TL;DR
Researchers analyzed blood metabolite patterns from 18,476 people aged 1 to 96 and developed measures of metabolomic aging and mortality-related age deviation. The measures were associated with chronic disease and death risk, and added modest predictive information beyond several existing health assessments; the observational results do not establish that lifestyle or diet changes caused shifts in biological aging.
Researchers analyzing blood samples from 18,476 people aged 1 to 96 developed two measures of metabolomic aging that were associated with chronic disease and mortality risk. The study, published in Science Translational Medicine, suggests blood-based metabolic profiles could add information to population health-risk assessments, but it does not show that the measures diagnose disease or that changing a person’s habits will slow aging.
According to the study in Science Translational Medicine, researchers drew on the Shanghai Pudong Cohort, with participants recruited across six centers. The team used plasma samples and nano-assisted laser desorption/ionization mass spectrometry to assess metabolite patterns. Researchers first built a metabolomic age model using 5,252 healthy participants, then tested it in a separate healthy group of 5,456 and examined a disease cohort of 7,768 people.
As described in the study, for the healthy group the researchers estimated metabolomic age and calculated metabolomic age acceleration, or the difference between that estimate and a participant’s chronological age. For people with existing health conditions, they developed an age-deviation measure based on how metabolite-related mortality risk compared with expectations for a person’s age. The team tracked deaths through January 2025 and reassessed chronic disease status in 2023.
The study authors reported that 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. They also reported that both measures improved mortality-risk discrimination beyond frailty assessments, cardiovascular health scores and routine blood biomarkers, though the gains were modest. A panel of 56 metabolites retained most of the age-predictive information in the full set of 232 features.
What Blood-Based Aging Measures Add
Chronological age is a broad marker, while people of the same age can have different health profiles. The study’s authors say measures built from blood metabolites could, if validated and refined, help researchers identify differences in risk that are not captured by age alone. Their findings point to possible use in population-level risk stratification, rather than a ready-to-use clinical test for an individual.
The study reports that improvement in risk discrimination was modest, and does not establish that metabolomic measures should guide treatment. The results may help researchers investigate how biological pathways change with age and how those patterns relate to disease burden. More evidence would be needed to show whether using these measures changes health outcomes or clinical decisions.
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How Researchers Built the Measures
According to the study, researchers collected baseline information in 2019, including health measures and, where available, questionnaire data on diet, lifestyle, sleep, mental health and family medical history. Chronic disease status was reassessed in 2023. A subset of 1,500 participants had repeat plasma metabolomic profiling, allowing the team to examine changes over time.
The study authors also examined links between metabolomic age acceleration and factors such as smoking, secondhand smoke exposure, shift-work history and parental history of premature cardiovascular disease. They reported that higher values were associated with consumption of several foods, while mixed-grain consumption and exercise were linked to lower values. In repeat analyses, increased physical activity and reduced fried-food intake were associated with more favorable changes. Among participants with hypertension, those whose blood pressure was below 140/90 mmHg by 2023 showed more favorable changes in the mortality-related age-deviation measure than those whose hypertension remained uncontrolled.
These relationships were observational, as the study authors note. The study cannot show that any particular food, activity or exposure caused a change in metabolomic aging. The team also retrained its ensemble approach on UK Biobank proteomic data, U.S. NHANES physiological biomarkers and human gut microbiome data, but the reported findings center on the Shanghai cohort.
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Limits of the Current Evidence
The study establishes associations, not cause and effect. Its authors do not show that adopting a specific diet or exercise routine will lower a person’s biological age or prevent disease. Lifestyle and food findings may reflect other differences among participants, and questionnaire information was available only where collected.
It is also unclear how well the measures would perform in populations beyond the Shanghai cohort or how useful they would be for individual clinical decisions. The report describes modest gains in risk discrimination but does not establish a threshold for diagnosis, a treatment decision, or a routine screening recommendation. The study provides no basis for treating a metabolomic age estimate as a substitute for medical assessment.
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Validation Before Clinical Use
The findings support further evaluation of metabolomic aging measures in other populations and research settings. Future work would need to test how consistently the measures predict health outcomes, whether their added predictive value holds across different groups, and whether using them leads to better decisions or outcomes.
The study does not announce a clinical rollout or a confirmed next milestone. For now, the measures remain research tools, and the reported lifestyle associations should not be read as proof that changing one behavior will alter aging-related risk.
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Key Questions
What does metabolomic age mean?
It is an estimate derived from patterns of small molecules, or metabolites, measured in blood. In this study, researchers compared that estimate with a participant’s chronological age to calculate metabolomic age acceleration.
Does the study show that blood tests can diagnose how fast someone is aging?
No. The researchers developed measures associated with health and mortality risk in a cohort study. The report does not establish them as diagnostic tests or recommend their use in routine care.
Did the findings prove that diet or exercise changes biological aging?
No. Some food and activity patterns were associated with metabolomic age measures, including more favorable changes linked with increased activity and reduced fried-food intake in repeat analyses. Because the study was observational, it cannot establish that these changes caused the differences.
How many people were included?
The analysis included 18,476 participants in the Shanghai Pudong Cohort, ranging from 12 months to 96 years old. Researchers used separate healthy development and validation groups as well as a disease cohort.
When was mortality tracked through?
The study tracked mortality through January 2025. Chronic disease status was reassessed in 2023, and a subset of participants also provided repeat blood samples for metabolomic profiling.
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