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Researchers used blood metabolite data from 18,476 people aged 1 to 96 to develop measures of metabolomic aging and age-related mortality risk. Higher scores were associated with chronic disease and death, but the study does not show that particular foods or lifestyle changes cause biological aging or alter those outcomes.
Researchers analyzed plasma samples from 18,476 people aged 1 to 96 in a Shanghai cohort to develop blood-based measures of metabolomic aging and mortality risk. In a study published in Science Translational Medicine, the researchers reported that higher scores were associated with chronic conditions and a greater risk of death. The study suggests blood chemistry may provide information about health risk beyond a person’s calendar age, but its observational design does not establish cause and effect.
According to the study, the team developed two measures for different groups. Among healthy participants, an ensemble model estimated metabolomic age from blood metabolites and compared it with actual age. The difference, called metabolomic age acceleration, or mAA, indicates whether a person’s metabolite-based age estimate is higher or lower than their calendar age. For participants with existing health conditions, the researchers built an age-deviation measure based on how mortality risk differed from what was expected for a person’s age.
The study drew on samples collected across six centers in Shanghai. Researchers used 5,252 healthy participants to develop the metabolomic age model, tested it in another 5,456 healthy participants, and examined the mortality-linked measure in a disease cohort of 7,768 people. Follow-up assessments included chronic disease status in 2023, while deaths were tracked through January 2025. The authors reported that a panel of 56 metabolites retained most of the age-predictive information in the full set of 232 features.
The researchers reported that higher mAA was associated with a greater likelihood of developing diabetes, hypertension and fatty liver disease, as well as a higher risk of death from any cause. They also reported that both measures modestly improved the ability to distinguish mortality risk beyond frailty assessments, cardiovascular health scores and routine blood biomarkers. This describes added predictive information in the study, not a test proven to improve patient outcomes.
How Blood Measures May Add Risk Information
Calendar age is a broad indicator: people of the same age can have different health histories and risks. The study authors say patterns across blood metabolites could help identify some of those differences, including among people already living with disease. They describe the measures as having potential for population-level risk assessment and improved risk stratification.
The distinction is between association and practical clinical use. The authors reported that although the measures added some ability to separate higher- and lower-risk participants, the improvement was modest. The study does not establish that clinicians should use the scores to make treatment decisions, nor does it show that changing a score prevents disease or extends life. Further testing would be needed before the measures could be considered a reliable tool for routine care.
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How the Shanghai Cohort Was Studied
As described in the study, researchers used nano-assisted laser desorption/ionization mass spectrometry to profile metabolites in plasma. Participants’ diet, lifestyle, sleep, mental health and family medical history were collected through standardized questionnaires where information was available. Baseline assessments took place in 2019, and chronic disease status was reassessed in 2023. Of participants who completed repeat surveys and updated assessments, 1,500 also had repeat metabolomic profiling.
The team examined possible links between mAA and lifestyle or background factors. The researchers reported associations with active and secondhand smoking, shift work, hair dye use and a parental history of premature cardiovascular disease. They also reported associations with consumption of organ meat, sugar, processed meat, seafood and fried foods; mixed grains and exercise were linked to lower values. In repeated analyses, increased physical activity and reduced fried-food intake were linked to more favorable changes in mAA. These are observational associations, not proof that any listed exposure or food directly changes biological aging.
Among participants with hypertension, the study authors reported that 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 researchers also retrained their approach using data from the UK Biobank, the U.S. National Health and Nutrition Examination Survey and human gut microbiome data. The available report does not provide enough detail to treat those tests as proof of clinical validation across populations.
Limits of the Current Evidence
The study was observational, so it cannot determine whether diet, exercise, smoking or other associated factors caused the differences in metabolomic aging scores. Nor does an association between a score and later disease establish that the score itself can predict an individual’s future with certainty. The researchers described the improvements in mortality-risk discrimination as modest; the available report does not specify their size in a way that supports a direct comparison here.
It also remains unclear how well the measures would perform in populations outside the Shanghai cohort, how consistently they would work in routine clinical settings, and whether using them would change care or health outcomes. The study’s age range spans childhood to old age, but the report does not explain how performance varied across all age groups. The precise contribution of individual metabolites and the best thresholds for interpreting a person’s score also remain unsettled.
Validation Before Clinical Use
Further evaluation in independent populations is needed to establish how well these measures perform across different ages, health conditions and settings. Researchers would also need to determine whether the scores add useful information to existing assessments in practice and whether acting on them leads to better outcomes.
For now, the study findings support continued research into blood metabolite patterns as markers of health risk, not a stand-alone diagnosis or a personal prescription. The report does not identify a planned clinical trial or a timetable for clinical adoption. It also does not show that any specific food or behavior is a proven way to change biological age.
Key Questions
What did the researchers measure in blood?
They profiled plasma metabolites—chemicals present in blood—and used patterns across those measurements to estimate metabolomic age or age-related mortality risk.
Does a higher metabolomic age mean someone will develop a disease?
No. The researchers found that higher scores were associated with greater risk in the study, but they do not establish what will happen to any individual. The measures are not presented in the report as a diagnosis.
Did the study prove that diet or exercise changes biological aging?
No. The reported links between foods, physical activity and mAA came from observational analyses. They do not prove that those factors caused the score changes or altered disease or mortality risk.
Could this blood-based approach be used in clinics now?
The study authors describe potential for risk assessment, but the reported improvement in distinguishing mortality risk was modest. The report does not establish routine clinical use or show that using the measures improves health outcomes.
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