TL;DR
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Researchers analyzed blood metabolite data from 18,476 people aged 1 to 96 and developed measures of metabolomic age and mortality-related age deviation. The measures were associated with chronic disease and death risk, but their added predictive value was modest and the observational results do not show that specific lifestyle choices cause changes in biological aging.
Researchers have developed two blood-based measures of aging-related risk and tested them in 18,476 people aged 1 to 96, finding that the measures were associated with chronic disease and mortality. The study, published in Science Translational Medicine, suggests metabolite profiles may add information to standard health assessments, but it does not show that the measures can diagnose disease or that changing particular habits will slow aging.
The researchers analyzed plasma samples from participants in the Shanghai Pudong Cohort using mass spectrometry. They first built a metabolomic age model using 5,252 healthy participants, tested it in a separate healthy group of 5,456, and examined a disease cohort of 7,768. The team used a machine-learning ensemble to estimate metabolomic age in the healthy group, then compared that estimate with each participant’s actual age. The difference was called metabolomic age acceleration, or mAA.
For participants with existing health conditions, the team created a separate age-deviation measure based on metabolite-derived mortality risk relative to what would be expected for a person’s age. Baseline assessments took place in 2019; chronic disease status was reassessed in 2023, and deaths were tracked through January 2025. A subset of 1,500 participants also had repeat blood profiling. The researchers reported that a panel of 56 metabolites retained most of the age-predictive information in the full set of 232 features.
Higher mAA was associated with greater risk of developing conditions including diabetes, hypertension and fatty liver disease, as well as a higher risk of death from any cause. The measures also improved mortality-risk discrimination beyond frailty assessments, cardiovascular health scores and routine blood biomarkers, although the reported improvement was modest. These are associations from an observational study, not proof that the metabolite patterns cause illness or death.
What Blood Profiles Could Add
Chronological age is easy to measure, but it does not capture all differences in health among people of the same age. The study asks whether signals in blood can help identify those whose biological risk profile differs from what their years alone might suggest. If validated, metabolomic measures could potentially support population-level risk assessment or help identify people who may benefit from closer monitoring.
The findings do not establish a ready-to-use clinical test. The gains in separating higher- and lower-risk participants were modest, and the researchers’ results do not show that measuring these metabolites changes outcomes. For readers, the main takeaway is that blood chemistry may offer another layer of risk information, not a substitute for medical evaluation, established tests or a person’s overall health history.
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How the Two Measures Were Built
The study examined metabolic patterns across a broad age range, from infancy to older adulthood, rather than focusing only on older people. Participants came from six centers in Shanghai. Questionnaires recorded information on diet, lifestyle, sleep, mental health and family medical history where available. The research team also used statistical methods to select metabolites associated with aging and to assess possible sex-specific patterns.
Researchers reported links between higher mAA and factors including active and secondhand smoking, shift work, hair dye use and a parental history of premature cardiovascular disease. Higher values were also associated with greater reported consumption of organ meat, sugar, processed meat, seafood and fried foods; mixed-grain consumption and exercise were associated with lower values. In repeat analyses, increased physical activity and reduced fried-food intake were linked to more favorable mAA changes. Among participants with hypertension, those whose blood pressure was below 140/90 mmHg by 2023 had more favorable changes in the mortality-linked age-deviation measure than those whose hypertension remained uncontrolled.
The report describes the food, activity and blood-pressure findings as observational associations. They cannot establish that any individual food, behavior or treatment caused a change in biological aging. The team also retrained its ensemble approach using UK Biobank proteomic data, U.S. NHANES physiological biomarker data and human gut microbiome data, but the supplied report does not provide detailed performance results for those tests.
“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
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Limits of the Risk Estimates
The study establishes associations within the reported cohort, but the available report does not give enough detail to determine how accurately the measures would perform in other populations or in routine clinical settings. It also does not specify a threshold at which a person should be considered to have accelerated aging. The modest improvement in mortality-risk discrimination means the measures should not be presented as definitive predictors for an individual.
It remains unclear whether metabolomic age changes predict future health independently across different populations, and whether using the measures to guide care would improve outcomes. Although some participants had repeat measurements, the associations between lifestyle changes and more favorable scores do not prove those changes caused the score differences. The study’s results also do not establish that modifying a metabolite or pathway would prevent disease.
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Validation Before Clinical Use
The report does not announce a timetable for clinical deployment or identify a next trial. Further research would need to test the measures in independent and more diverse populations, clarify how stable the scores are over time, and establish whether they add useful information to decisions clinicians already make. Studies would also need to assess whether using a score to guide monitoring or treatment improves health outcomes, rather than merely sorting participants by measured risk.
Until such evidence is available, the findings are best understood as a research development in metabolomic risk assessment. The next substantive milestone would be external validation and evidence of practical benefit; neither a diagnostic role nor a proven way to slow biological aging is established by this study.
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Key Questions
What does metabolomic age mean?
It is an estimate based on patterns of small molecules, or metabolites, measured in blood. In this study, researchers compared that estimate with a person’s chronological age to calculate metabolomic age acceleration.
Does a higher score mean someone will develop a disease?
No. Higher measures were associated with greater disease and mortality risk at the group level, but the study does not establish an individual outcome or provide a confirmed diagnostic threshold.
Did the study prove that diet or exercise changes biological aging?
No. The reported links between diet, physical activity and metabolomic measures were observational. The researchers said the findings do not establish that particular foods or lifestyle changes directly affect biological aging.
Can this blood test be used in a clinic now?
The report describes research measures, not an established clinical test. It does not show that using them to guide care improves outcomes, and the reported added value for distinguishing mortality risk was modest.
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