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Researchers analyzed blood metabolite patterns from 18,476 people aged 1 to 96 and developed measures associated with chronic disease and mortality risk. The measures modestly improved risk discrimination beyond several existing assessments, but the observational study does not show that specific foods or habits cause faster or slower 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, according to a study published in Science Translational Medicine. The findings suggest blood chemistry may add information to health-risk assessments beyond a person’s calendar age, but the study does not establish that the measured patterns cause illness or predict an individual’s future with certainty.
The research team used plasma samples from participants in the Shanghai Pudong Cohort, recruited across six centers in Shanghai, China. Using mass spectrometry, they measured metabolic features in blood and built an age model using 5,252 healthy participants. They tested it in a separate healthy group of 5,456 people and examined a second metric in 7,768 participants who had existing diseases.
For healthy participants, the researchers calculated metabolomic age acceleration, or how far a person’s metabolite-based age differed from their actual age. For participants with existing conditions, they created an age-deviation measure reflecting how metabolite-based mortality risk differed from what would be expected at a person’s age. A panel of 56 metabolites retained most of the age-predictive information in the full set of 232 features.
Higher metabolomic age acceleration was associated with greater likelihood of developing conditions including diabetes, hypertension and fatty liver disease, as well as higher risk of death from any cause. The metrics also added to the ability to distinguish higher- and lower-risk participants beyond frailty assessments, cardiovascular health scores and routine blood biomarkers. The reported improvement in discrimination was described as modest.
Blood Measures May Add Risk Information
The results point to a possible way of identifying health risks that are not fully captured by chronological age or standard measures alone. A blood-based metric that works across a wide age range could, if confirmed and made practical, help researchers or clinicians group people by patterns linked to disease risk.
That possibility remains a research finding, not a ready-made test for individual decisions. The study reports associations, not proof of cause, and the improvement over existing assessments was modest. It does not show that a metabolomic age score can diagnose disease, determine how long someone will live, or guide treatment on its own.
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How Researchers Built the Metrics
The study used baseline assessments collected in 2019, with chronic disease status reassessed in 2023 and mortality tracked through January 2025. Some participants completed repeat surveys and updated health assessments in 2023; 1,500 also had repeat blood metabolite profiling. The researchers used an ensemble machine-learning approach for the healthy-cohort age model and statistical methods to develop the mortality-linked measure for participants with health conditions.
The team also examined lifestyle and background factors. Higher age acceleration was associated with smoking and secondhand smoke exposure, shift work, certain reported dietary patterns and a parental history of premature cardiovascular disease. Exercise and mixed-grain consumption were associated with lower values. Repeated analyses linked increased physical activity and reduced fried-food intake to more favorable changes in the measure. These observations do not establish that any one behavior or food caused the changes.
“The findings highlight the potential of plasma metabolomic profiling for future population-level risk assessment and improved risk stratification.”
— The study authors, as summarized by News-Medical
Limits of the Current Evidence
The findings come from a cohort in Shanghai, and the report does not establish how well the measures would perform across other populations or in routine clinical settings. Although the team retrained its ensemble approach using UK Biobank proteomic data, U.S. health survey biomarkers and gut microbiome data, the main reported cohort results are from Shanghai participants.
It is also unclear whether the metrics can meaningfully change medical decisions or improve health outcomes. The study found associations and modest gains in risk discrimination, but did not show that using the measures to screen or treat people reduces disease or mortality. Observational links between diet, activity and scores cannot determine cause and effect.
Validation Before Clinical Use
The next step is further testing of the measures in independent and more diverse populations, with attention to how consistently they predict outcomes and whether they add useful information in actual care. The source report does not identify a planned clinical trial or a timetable for a commercial or clinical test.
For now, the findings are best understood as evidence that blood metabolite patterns may complement established health indicators in population research. They do not justify changing treatment or interpreting a personal blood result without guidance from a qualified health professional.
Key Questions
What did the researchers measure?
They analyzed plasma metabolites, chemicals in blood, and used the resulting patterns to estimate metabolomic age or age-related mortality risk.
Does the study show that lifestyle changes slow biological aging?
No. Some behaviors were associated with the measures, and repeat analyses linked increased activity and reduced fried-food intake with more favorable score changes. The observational study cannot show that those changes caused the differences.
Can this research predict how long a person will live?
No individual lifespan prediction was established. The metrics were associated with mortality risk across groups and modestly improved risk discrimination beyond several other assessments.
Is this a clinical blood test now?
The report describes research measures, not a validated test for routine clinical use. Their performance and usefulness would need further study before they could guide individual care.
Source: rss
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