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Researchers used plasma metabolite data from 18,476 people aged 1 to 96 to develop measures of metabolomic aging and age-related mortality risk. The measures were associated with chronic disease and death risk, but the study was observational and their added predictive value was modest.
Researchers analyzing blood samples from 18,476 people aged 1 to 96 developed two measures based on circulating metabolites that were associated with chronic disease and mortality risk. The study, published in Science Translational Medicine, suggests blood metabolite patterns could complement age and standard health measures in assessing risk, but it does not show that the patterns cause illness or predict an individual’s future with certainty.
The study used plasma samples from participants in the Shanghai Pudong Cohort across six centers in Shanghai, China. Researchers first trained a 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. The groups had mean ages of about 55, 55 and 68, respectively.
For healthy participants, the team calculated metabolomic age acceleration, or how far a blood-based age estimate differed from a person’s chronological age. For participants with existing conditions, it developed an age-deviation measure based on how their metabolite-related mortality risk compared with what was expected for their age. A panel of 56 metabolites retained most of the age-prediction information in the full set of 232 features.
Baseline assessments took place in 2019. Chronic disease status was reassessed in 2023, and deaths were tracked through January 2025. The researchers reported that higher metabolomic age acceleration was associated with greater mortality risk even after accounting for established health measures, and with greater likelihood of developing conditions including diabetes, hypertension and fatty liver disease. Both measures modestly improved mortality-risk discrimination beyond frailty assessments, cardiovascular health scores and routine blood biomarkers.
Blood Measures May Add Risk Information
Chronological age is a simple measure, but people of the same age can have different health profiles. The study’s measures are intended to capture some of that variation through patterns in blood chemistry. If validated in further research, they could help researchers or health systems stratify risk across populations and identify groups who may benefit from closer monitoring.
The reported improvement in risk discrimination was modest, and the study does not establish that these metrics are ready for routine clinical use. They should not be read as diagnoses or as a substitute for established clinical assessments. The findings show an association in the studied cohorts, not proof that a person’s metabolomic age causes a condition or that changing it will prevent one.
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How the Shanghai Cohort Was Studied
Metabolites are small molecules produced or used in the body, and their levels can reflect biological processes. The researchers used nano-assisted laser desorption/ionization mass spectrometry to profile plasma samples, then applied an ensemble-learning model to estimate age-related patterns. They also examined the model using UK Biobank proteomic data, U.S. National Health and Nutrition Examination Survey physiological biomarkers, and human gut microbiome data.
Participants completed questionnaires about factors such as diet, lifestyle, sleep and family medical history where information was available. A subset returned for repeat assessments in 2023, and 1,500 underwent repeat metabolomic profiling. The report also describes analyses of lifestyle factors and changes over time, but those comparisons remain observational and may reflect influences beyond the measured behaviors.
“The findings highlight the potential of plasma metabolomic profiling for future population-level risk assessment and improved risk stratification.”
— The study authors, as described in the News-Medical report
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Limits of the Risk Estimates
The study does not establish whether particular foods or lifestyle changes directly alter biological aging. Higher metabolomic age acceleration was associated with factors including active and secondhand smoking, shift work, and some reported dietary patterns; exercise and mixed-grain consumption were associated with lower values. The authors’ reported associations cannot show that any one factor caused the metabolite patterns or health outcomes.
The available report also does not provide enough information to judge how well the measures perform in populations outside the study or how they would translate into decisions for individual patients. Although the researchers tested the approach with other datasets, external validation and clinical usefulness remain open questions. The report gives no basis for using a metabolomic age result as a diagnosis.
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Validation Before Clinical Use
The next step is further testing in independent and more diverse populations, including research that examines whether the measures remain accurate across different settings and groups. Studies would also need to determine whether adding them to existing assessments changes clinical decisions or improves health outcomes, rather than only modestly separating risk groups in statistical analyses.
Until that evidence is available, the findings are best understood as a research development in population-level risk assessment. The study followed participants for chronic disease and mortality over the periods described, but it does not establish a timetable for clinical adoption or a recommendation for consumers to seek metabolomic testing.
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Key Questions
What did the researchers measure?
They analyzed plasma metabolites and developed one measure of metabolomic age acceleration for healthy participants and another age-deviation measure linked to mortality risk for participants with existing conditions.
Does a higher metabolomic age mean someone will become ill?
No. The study found associations between higher values and increased risk in the groups studied. The measures do not determine an individual’s future or provide a diagnosis.
Did the study show that diet or exercise changes biological aging?
No. Some dietary and activity patterns were associated with the measures, and repeat analyses linked increased activity and reduced fried-food intake with more favorable changes. Because the research was observational, it cannot establish cause and effect.
Can people use these measures in routine care now?
The report describes research measures, not a confirmed routine clinical test. More evidence is needed on performance in other populations and whether using the measures improves care or health outcomes.
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