TL;DR
Researchers analyzed blood metabolite profiles from 18,476 people aged 1 to 96 and developed measures that differed from chronological age. The measures were associated with chronic disease and mortality risk, and modestly improved risk prediction beyond established assessments; the observational study does not show that the identified lifestyle factors cause biological aging.
Researchers analyzing blood samples from 18,476 people aged 1 to 96 developed two measures based on metabolite patterns to assess aging-related health risks. The study, published in Science Translational Medicine, found that the measures were associated with chronic disease and mortality, suggesting blood chemistry may add information beyond a person’s chronological age, though the improvement in prediction was modest.
The research drew participants from the Shanghai Pudong Cohort across six centers. The team used nano-assisted laser desorption/ionization mass spectrometry to examine plasma, the liquid component of blood. A group of 5,252 healthy participants was used to build a metabolomic age model, which was tested in another 5,456 healthy participants. Researchers also analyzed 7,768 participants with existing diseases.
For healthy participants, the researchers calculated metabolomic age acceleration, or how far the age estimated from metabolite patterns differed from actual age. For people 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. A panel of 56 metabolites retained most of the age-predictive information found 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 associations remained after the researchers accounted for established health measures. The study also reported that the measures improved mortality-risk discrimination beyond frailty assessments, cardiovascular health scores and routine blood biomarkers, but described the gains as modest.
What Blood-Based Age Measures Could Add
Chronological age is a simple measure, but people of the same age can have different health profiles. The study’s measures offer a possible way to summarize patterns in blood metabolites that correlate with those differences and could, in future, help identify people who may warrant closer risk assessment. The researchers say the findings point to potential use in population-level risk assessment, not to a ready-made diagnosis or individual prediction tool.
The results also do not establish that changing a particular behavior will change a person’s biological age or prevent disease. The links between the measures and outcomes are associations from an observational study. Any practical use would require further validation, including evidence that the scores work reliably in other populations and improve decisions beyond existing clinical tools.
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How the Shanghai Cohort Was Studied
Baseline assessments took place in 2019, and chronic disease status was reassessed in 2023. Mortality was tracked through January 2025. In 2023, thousands of participants completed follow-up surveys and updated disease assessments; 1,500 also had their plasma metabolomes measured again. The surveys covered factors such as diet, lifestyle, sleep, mental health and family medical history where information was available.
The researchers tested the ensemble-learning approach by retraining it on other kinds of data, including UK Biobank proteomic data, physiological biomarkers from the U.S. National Health and Nutrition Examination Survey, and human gut microbiome data. The report describes these tests as part of evaluating the approach; it does not establish that the Shanghai-derived measures are already validated for routine clinical use in those settings.
“The study highlights the potential of plasma metabolomic profiling for future population-level risk assessment and improved risk stratification.”
— The study authors, as reported by News-Medical
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Limits of the Risk Estimates
The findings show associations, not cause and effect. Higher metabolomic age acceleration was linked to factors including active and secondhand smoke exposure, shift work, hair dye use and parental history of premature cardiovascular disease. Diet patterns and exercise were also associated with the measure, but the study cannot establish that any one exposure or behavior caused the differences.
It is not clear from the reported results how well the measures would perform for individuals outside the Shanghai cohort, whether they would change clinical care, or what thresholds should guide decisions. The predictive improvement was modest, and the report does not provide enough information to treat the scores as a substitute for standard risk assessments. The supplied report also ends during its list of specific metabolites, so a complete account of all metabolite findings is not available here.
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Validation Before Clinical Use
The next step is to test the measures in independent populations and assess whether their performance holds across different ages, health backgrounds and settings. Researchers would also need to determine whether using the scores improves risk assessment or health outcomes compared with existing approaches. The available report does not specify a timetable for further studies or plans to introduce a clinical test.
For now, the findings are best understood as evidence that plasma metabolite patterns can be associated with aging-related health risks in a large cohort. They do not show that a blood-based aging score can diagnose disease, predict an individual’s lifespan, or replace evaluation by a qualified health professional.
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Key Questions
What did the researchers measure?
They analyzed metabolites in plasma, the liquid part of blood, and used the patterns to estimate metabolomic age or age-related mortality risk.
Does a higher metabolomic age mean someone will develop a disease?
No. Higher values were associated with increased chronic disease and mortality risk in the study, but they do not establish what will happen to an individual or provide a diagnosis.
Did the study prove that diet or exercise changes biological aging?
No. The study reported associations between behaviors and metabolomic age measures. Its observational design cannot show that a specific food or lifestyle change caused the measured differences.
Is this blood test ready for routine medical use?
The report does not establish that it is. The measures need further validation, and their reported improvement in risk discrimination was modest.
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