TL;DR
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Researchers analyzed blood metabolite data from 18,476 people aged 1 to 96 and developed measures that estimate metabolomic age and mortality-related age deviation. The measures were associated with chronic disease and death risk, but their improvement over existing risk assessments was modest and the observational results do not prove cause and effect.
Researchers reporting in Science Translational Medicine developed two blood-based measures of aging using data from 18,476 people aged 1 to 96, and found that the measures were associated with chronic disease and mortality risk. The results suggest that plasma metabolite profiles could add information to health-risk assessment, although the reported gains over existing measures were modest and do not establish that the measured patterns cause illness or shorter life.
The study used plasma samples from participants in the Shanghai Pudong Cohort, recruited across six centers in Shanghai, China. Researchers first built a metabolomic age model using 5,252 healthy participants, then tested it in a separate group of 5,456 healthy participants and examined its performance in a disease cohort of 7,768 people. The average ages of the healthy and disease groups were about 55 and 68, respectively.
For healthy participants, the team estimated a metabolomic age and calculated metabolomic age acceleration, or how much that estimate differed from a participant’s chronological age. For people with existing health conditions, researchers developed a separate age-deviation measure based on how metabolite-related mortality risk compared with what would be expected for a person’s age. Follow-up assessments began from baseline in 2019; chronic disease status was reassessed in 2023, and deaths were tracked through January 2025.
A panel of 56 metabolites retained most of the age-predictive information in the full set of 232 measured features. Higher metabolomic age acceleration was associated with later diagnoses including diabetes, hypertension and fatty liver disease, as well as higher risk of death from any cause. The study also identified associations with factors such as smoking exposure, shift work and parental history of premature cardiovascular disease. These findings describe statistical relationships, not proof that any one exposure or behavior caused faster aging.
What Blood Profiles Add to Risk Assessment
The findings point to a possible way to identify health risks that chronological age alone may not capture. The researchers reported that both measures helped distinguish people at higher and lower mortality risk when added to assessments such as frailty, cardiovascular health scores and routine blood biomarkers. However, the improvement in risk discrimination was modest; the study does not show that the measures are ready to guide individual treatment or screening decisions.
The results may be useful for research into how changes across multiple metabolic pathways relate to aging and disease. The team linked aging patterns to pathways involving threonine, serine, glycine, butanoate, cysteine and methionine. Further studies would be needed to determine whether these measures work reliably in other populations and whether using them improves health outcomes.
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How the Shanghai Cohort Was Studied
The study’s approach combined mass spectrometry with statistical modeling. Plasma samples were analyzed using nano-assisted laser desorption/ionization mass spectrometry, and an ensemble model was used to estimate metabolomic age. Researchers also assessed selected associations with lifestyle, diet, family history and health status, where those data were available.
Participants completed baseline assessments in 2019, and some completed repeat surveys and updated disease assessments in 2023. Of those repeat participants, 1,500 had a second metabolomic profile. The researchers also tested their ensemble approach by retraining it on data from the UK Biobank, U.S. National Health and Nutrition Examination Survey, and human gut microbiome datasets. The source report does not provide enough detail to establish that those tests validate the specific Shanghai measures for routine clinical use.
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Limits of the Evidence
The study was observational, so it cannot show that foods, exercise, smoking exposure or other associated factors directly changed biological aging or caused disease. The report described links between higher metabolomic age acceleration and certain food-consumption patterns, while exercise and mixed-grain consumption were linked to lower values. It also reported that increased physical activity and reduced fried-food intake were associated with more favorable changes over repeated measurements. Those associations may be influenced by other differences among participants.
It is also unclear how well the measures would perform outside the Shanghai cohort, how stable they are over time for an individual, or what threshold would count as clinically meaningful. The reported risk-prediction gains were modest, and the study does not establish whether testing would change medical decisions or improve outcomes. The source material provides no direct quotations from the study authors.
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Validation Before Clinical Use
The next steps are independent studies that test the measures in different populations and assess their accuracy, stability and practical value. Researchers would also need to establish whether the measures add enough information beyond standard clinical assessments to justify their use, and whether acting on the results benefits patients.
For now, the findings support further research into blood metabolite patterns as population-level risk indicators, not a stand-alone diagnosis or a replacement for routine medical evaluation. The study does not recommend that people change diet, exercise or treatment based on a metabolomic age result.
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Key Questions
What does metabolomic age mean?
It is an estimate derived from patterns of metabolites in plasma, chemicals involved in the body’s processes. In this study, researchers compared that estimate with chronological age to calculate metabolomic age acceleration.
Did the study show that blood tests can predict an individual’s lifespan?
No. The measures were associated with mortality risk across study participants, but the report does not establish a precise lifespan prediction for an individual or show that the test is ready for clinical use.
Did diet or exercise cause changes in the aging measures?
The study found associations between reported lifestyle factors and the measures, including more favorable changes linked with increased physical activity and lower fried-food intake. Because the research was observational, it cannot establish cause and effect.
How many people took part?
The analysis included 18,476 participants aged 1 to 96 from the Shanghai Pudong Cohort, divided into healthy model-development and validation groups and a disease cohort.
Can these measures replace standard health assessments?
The study did not establish that they can. They modestly improved risk discrimination alongside existing assessments, but further validation is needed before their role in clinical care is known.
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