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Researchers analyzed blood metabolite data from 18,476 people aged 1 to 96 and developed measures that were associated with chronic disease and mortality risk. The results suggest these measures may add modest risk information beyond standard health assessments, but the observational study cannot show that specific habits cause changes in biological aging.
Researchers analyzed blood samples from 18,476 people aged 1 to 96 and developed metabolite-based measures associated with biological aging, chronic disease and risk of death. The study, published in Science Translational Medicine, suggests blood chemistry may help refine population-level health risk assessments, although it does not show that the measured patterns cause illness or that changing them will extend life.
According to the study in Science Translational Medicine, the research drew on participants in the Shanghai Pudong Cohort, recruited across six centers. The team used a mass spectrometry method to examine plasma, the liquid component of blood. It developed its metabolomic age model using 5,252 healthy participants, tested it in a separate group of 5,456 healthy participants, and examined disease-related risk in a further 7,768 participants. The average ages in the healthy and disease groups were 55 and 68, respectively.
The study authors estimated a metabolomic age for healthy participants and calculated metabolomic age acceleration, or mAA: the difference between that estimate and a person’s actual age. For participants with existing conditions, they created an age deviation measure based on how metabolite-related mortality risk differed from the expected risk for a person’s age. As described in the paper, follow-up included chronic disease reassessments in 2023 and mortality tracking through January 2025.
The authors reported that a panel of 56 metabolites retained most of the age-predictive information found across 232 measured features. Higher mAA was associated with later development of diabetes, hypertension and fatty liver disease, as well as greater risk of death from any cause. These are statistical associations reported by the study; they do not establish that the metabolites themselves caused the outcomes.
Blood Profiles Could Refine Risk Estimates
The study authors reported a possible way of identifying health risk that is not captured by a person’s calendar age alone. They found that both metabolomic measures improved the distinction between people at higher and lower mortality risk when added to assessments such as frailty, cardiovascular health scores and routine blood biomarkers. The reported gains were modest, so the study does not establish that the measures are ready for routine clinical use.
The researchers suggest a blood-based measure could be useful for research or population screening if future studies confirm that it performs reliably across different groups. It might help identify people whose risk patterns warrant closer attention, including those whose standard health measures look less concerning. The present study reports a potential association and predictive contribution; it does not show that testing improves health outcomes or should guide an individual’s care.
The paper also reports associations between mAA and lifestyle or environmental factors, including active and secondhand smoking, shift work, hair dye use, and parental history of premature cardiovascular disease. Higher mAA was linked with greater reported consumption of organ meat, sugar, processed meat, seafood and fried foods; mixed grains and exercise were linked with lower values. The authors note that these patterns may help guide further research, but they are not proof that any listed exposure or food caused the measured aging differences.
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How Researchers Measured Metabolic Aging
As the study explains, metabolites are small molecules produced or used by the body, and their levels in blood can reflect a range of biological processes. The researchers examined whether patterns across these molecules could estimate age-related change and help distinguish health risks. They used an ensemble model for the healthy cohort and statistical survival models for the disease cohort, then assessed pathways and metabolites linked with the measures.
According to the paper, baseline assessments began in 2019. In 2023, 3,745 healthy-cohort participants and 4,658 disease-cohort participants completed repeat surveys and updated disease assessments; 1,500 people also had repeat metabolomic profiling. The repeated measurements allowed the team to examine changes over time. Participants’ surveys covered diet, lifestyle, sleep, mental health and family history where information was available.
The researchers also retrained their ensemble approach using data from other sources, including UK Biobank proteomic data, U.S. National Health and Nutrition Examination Survey physiological biomarkers, and human gut microbiome data. The authors describe this as a test of the approach across data types. It does not, by itself, establish that the Shanghai findings apply equally to every population or healthcare setting.
Limits on What the Associations Show
The study authors characterize their research as observational, so it cannot determine whether particular foods, exposures or behaviors directly change metabolomic age or mortality risk. They linked increased physical activity and reduced fried-food intake with more favorable changes in mAA in repeated analyses, but those findings do not establish cause and effect. Other factors may help explain the associations.
The report also found that participants with hypertension whose blood pressure fell below 140/90 mmHg by 2023 had more favorable changes in the mortality-linked age deviation measure than participants whose hypertension remained uncontrolled. This result is an association within the study, not evidence that the metabolomic measure can replace blood pressure monitoring or clinical treatment.
The study authors identify further questions, including how accurately the measures predict outcomes in populations beyond the Shanghai cohort, how much they add for individual patients, and whether their use changes clinical decisions or health outcomes. The report describes modest improvements in risk discrimination but does not provide evidence that the measures are ready for routine screening. It also does not show that altering a particular metabolite will prevent disease or extend lifespan.
Validation Will Determine Clinical Use
The study’s findings point to independent validation in other populations and settings as a next step, including research assessing whether the measures remain accurate across different ages, health conditions and backgrounds. Researchers would also need to establish how the tests compare with existing assessments in practice and whether the added information changes care in a useful way.
Longer follow-up and repeat testing could help clarify how metabolomic measures change over time and how those changes relate to later illness or death. Studies designed to test specific interventions would be needed before researchers could conclude that changing diet, activity or another exposure alters biological aging through these pathways. For now, the findings reported in Science Translational Medicine support further research into blood metabolite profiles as a possible tool for studying and estimating health risk.
Key Questions
What did the study find?
In a study published in Science Translational Medicine, researchers developed blood metabolite-based measures in 18,476 people aged 1 to 96 that were associated with biological age, chronic disease and mortality risk. The associations do not prove that the measures cause those outcomes.
What does metabolomic age acceleration mean?
In this study, it is the difference between a person’s estimated age based on blood metabolite patterns and their actual age. The researchers reported that higher values were associated with several health risks among participants.
Does the study show that diet or exercise changes biological aging?
No. The study authors reported associations between some diet and activity patterns and the measures, and repeated analyses linked more activity and less fried-food intake with favorable mAA changes. Because the study was observational, it cannot establish that those changes caused the results.
Can these blood measures predict an individual’s lifespan?
The study reported associations with risk of death and modest improvements in distinguishing higher- and lower-risk groups. It does not establish a precise prediction of an individual’s lifespan or show that the measures are ready for routine clinical use.
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