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Researchers used blood metabolite profiles from 18,476 people aged 1 to 96 to develop measures of metabolomic aging and mortality risk. The measures were associated with chronic disease and death risk, but their added predictive value was modest and the observational findings do not show that lifestyle factors cause biological aging.
Researchers analyzing blood metabolite profiles from 18,476 people developed measures of aging that were associated with chronic disease and mortality risk, according to a study published in Science Translational Medicine. The findings suggest these measures could help identify health risks that chronological age alone may not capture, though their added ability to distinguish risk was modest and they are not established clinical tests.
The study drew on plasma samples from participants in the Shanghai Pudong Cohort, whose ages ranged from 12 months to 96 years. Researchers used nano-assisted laser desorption/ionization mass spectrometry to measure metabolites, chemicals involved in the body’s processes. They developed a metabolomic age model using 5,252 healthy participants, tested it in a further 5,456 healthy participants, and assessed a separate group of 7,768 people with existing diseases.
For the healthy group, the team calculated metabolomic age acceleration, or how far a person’s metabolite-based age estimate differed from their actual age. For participants with health conditions, it created a separate age-deviation measure based on mortality risk relative to what would be expected for their age. Higher values on these measures were associated with greater mortality risk, including after accounting for established health measures. Participants with accelerated metabolomic age were also more likely to develop diabetes, hypertension and fatty liver disease.
A panel of 56 metabolites retained most of the age-predictive information found across 232 metabolite features. The researchers reported that both measures improved mortality-risk discrimination beyond frailty assessments, cardiovascular health scores and routine blood biomarkers, but the improvement was modest. The team also retrained its approach using data from the UK Biobank, a U.S. health survey and human gut microbiome data.
How Blood Profiles Could Inform Risk
The work addresses a limitation of using chronological age as a broad indicator of health: people of the same age can have different health histories and risks. A blood-based measure that adds information beyond routine markers could eventually help researchers or health systems sort people into more tailored risk groups. The study, however, does not establish that these measures are ready for routine screening or that acting on them improves health outcomes.
The researchers also found associations between higher metabolomic age acceleration and factors including active and secondhand smoking, shift work history, and parental history of premature cardiovascular disease. Some food and activity patterns were associated with higher or lower measure values. These results may help direct further research, but they do not show that any particular exposure or behavior caused the measured aging differences.
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How the Cohort Was Followed
Baseline assessments took place in 2019, and participants’ chronic disease status was reassessed in 2023. Mortality was tracked through January 2025. In 2023, 3,745 people in the healthy cohort and 4,658 in the disease cohort completed follow-up surveys and updated disease assessments; 1,500 participants also had repeat metabolomic profiling.
The research team used an ensemble-learning model and statistical methods to select metabolites linked with aging. The study reported associations involving pathways related to amino-acid and other metabolism, including threonine, serine, glycine, butanoate, cysteine and methionine. These findings describe patterns in the data; they do not identify a single biological cause of aging.
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Limits of the Aging Measures
The findings are observational, so they cannot establish whether diet, exercise, smoking or other factors directly change biological aging or mortality risk. The available report also does not provide enough detail to determine how well the measures would perform in populations outside the Shanghai cohort or how they compare with existing tools in clinical practice.
Although the models added some ability to distinguish mortality risk, the improvement was described as modest. It remains unclear whether using the measures to guide care would change treatment decisions or lead to better outcomes. The study does not establish a threshold for an individual patient or recommend a clinical response to a particular result.
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Testing Value Beyond This Cohort
Further research would need to test the measures in independent and more diverse populations, assess how reliably they work over time, and determine whether they add useful information in real clinical settings. Longer follow-up and studies of whether risk-guided interventions improve outcomes would also be needed before the approach could be considered for routine use.
For now, the report presents metabolomic aging as a research tool for population-level risk assessment, not an individual diagnosis. The study’s follow-up period and repeated blood testing provide a basis for additional analysis, but no next clinical milestone or adoption plan is specified in the source material.
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Key Questions
What does metabolomic age mean?
It is an age estimate derived from patterns in blood metabolites. In this study, metabolomic age acceleration described how much that estimate differed from a participant’s actual age.
Does the study show that blood tests can predict an individual’s lifespan?
No. The study found associations between its measures and mortality risk in a cohort. It does not establish an individual lifespan prediction or a test for routine clinical use.
Did the researchers prove that diet or exercise changes biological aging?
No. The study reported associations between some lifestyle factors and metabolomic age measures. Because the findings were observational, they do not prove that those factors caused the changes.
How many people were included?
The study analyzed plasma samples from 18,476 participants in the Shanghai Pudong Cohort, ranging in age from 12 months to 96 years.
Could these measures be used in health care now?
The report does not establish that the measures are ready for routine care. Their added risk discrimination was modest, and further validation and evidence of clinical benefit would be needed.
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