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Researchers analyzed blood metabolite data from 18,476 people aged 1 to 96 and developed measures that captured aspects of aging-related health risk beyond chronological age. The measures were associated with chronic disease and mortality, although improvements in risk prediction were modest and the observational study cannot show that lifestyle factors caused changes in biological aging.
Researchers have developed two measures based on chemicals in blood that were associated with chronic disease and mortality risk in a study of 18,476 people aged 1 to 96. Published in Science Translational Medicine, the study suggests metabolite profiles could add information to chronological age and standard health assessments, though the reported gains in distinguishing risk were modest.
The research team analyzed plasma samples from participants in the Shanghai Pudong Cohort, recruited across six centers in Shanghai, China. They used nano-assisted laser desorption/ionization mass spectrometry to measure metabolite patterns. A group of 5,252 healthy participants was used to build a model estimating metabolomic age; the researchers tested it in another 5,456 healthy participants and studied a further 7,768 people with existing diseases.
For the healthy group, the researchers calculated metabolomic age acceleration, or how much a person’s metabolite-based age differed from their actual age. For participants with health conditions, they developed a separate age-deviation measure based on mortality risk relative to what would be expected for a person’s age. The analyses found that higher values on these measures were associated with increased mortality risk and, for accelerated metabolomic age, a greater likelihood of developing conditions including diabetes, hypertension and fatty liver disease.
A panel of 56 metabolites retained most of the age-predictive information in the full set of 232 measured features. The researchers also reported associations between higher metabolomic age acceleration and factors including smoking, secondhand smoke exposure, shift work and parental history of premature cardiovascular disease. In repeated analyses, increased physical activity and reduced fried-food intake were linked to more favorable changes; these observational results do not establish that the behaviors caused the changes.
Blood Profiles and Health Risk
Chronological age is a broad measure: people of the same age can have different health histories and risks. A blood-based measure that captures metabolic patterns could eventually help researchers or health systems identify differences in risk that age alone does not show. In this study, both measures improved the separation of higher- and lower-mortality-risk participants beyond assessments such as frailty, cardiovascular health scores and routine blood biomarkers.
The study did not establish that these tools are ready for routine clinical decisions. The improvement in risk discrimination was modest, and the analysis was based on one cohort in Shanghai. The findings are evidence of an association and potential use for population-level risk assessment, not proof that an individual’s metabolomic age predicts their health outcome or that changing a particular habit will slow aging.
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How Researchers Measured Aging
Age-related changes in metabolism are one possible way to study the difference between chronological age and biological health. The researchers used an ensemble model, combining multiple algorithms, to estimate metabolomic age in healthy participants. They used a separate statistical approach for participants with existing conditions, reflecting the study’s distinction between estimating age and estimating mortality-related age deviation.
Baseline assessments took place in 2019, chronic disease status was reassessed in 2023, and deaths were tracked through January 2025. Some participants completed repeat surveys and health assessments in 2023, while 1,500 underwent repeat metabolomic profiling. The team also retrained its ensemble approach using data from the UK Biobank, the U.S. National Health and Nutrition Examination Survey and human gut microbiome datasets, as described in the report.
“The findings highlight the potential of plasma metabolomic profiling for future population-level risk assessment and improved risk stratification.”
— Study authors, as described in the News-Medical report
Limits of the Risk Measures
The results do not show that the metabolomic measures cause disease or reliably forecast an individual’s future. The reported risk-discrimination gains were modest, and it is not clear from the source report how well the measures would perform in other populations or in routine care. While the study used additional datasets to retrain its approach, the report does not provide enough detail to establish that the specific Shanghai-derived measures were independently validated for clinical use elsewhere.
The lifestyle findings also remain uncertain: diet, exercise and other factors were associated with measure values, but the study was observational. The available report does not give effect sizes for the reported associations or establish that changing any one factor would alter disease or mortality risk. Further testing would be needed before the measures could guide medical decisions.
Testing Beyond the Shanghai Cohort
The next step is to determine whether these measures predict outcomes consistently in other populations and add useful information in settings where health decisions are made. The researchers’ use of UK Biobank, NHANES and microbiome data to retrain the ensemble approach offers additional testing, but the report does not specify a timetable for further studies or clinical deployment.
For now, the results support continued research into blood metabolite patterns as indicators of aging-related risk. They do not provide a basis for diagnosing disease or changing treatment; such decisions require clinical assessment and advice from a qualified health professional.
Key Questions
What did the study measure?
Researchers analyzed plasma metabolites, chemicals in blood, and used them to estimate metabolomic age or a mortality-related age deviation for different participant groups.
Does a higher metabolomic age mean someone will become ill?
No. The study found associations with chronic disease and mortality risk, but it does not establish an individual outcome or provide a diagnosis.
Did the study prove that diet or exercise changes biological aging?
No. Some lifestyle factors were associated with the measures, and repeat analyses linked increased activity and reduced fried-food intake to more favorable changes. Because the study was observational, it cannot show those changes caused the results.
Could this blood test be used in clinics now?
The report describes possible future use in population risk assessment. The measures’ improvements in risk discrimination were modest, and the findings do not establish that they are ready for routine clinical use.
Who took part in the research?
The study analyzed 18,476 Shanghai Pudong Cohort participants, ranging in age from 12 months to 96 years, across healthy and disease groups.
Source: rss
