TL;DR
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Researchers analyzed blood plasma from 18,476 people aged 1 to 96 and developed metabolite-based measures of aging and mortality risk. Higher scores were associated with chronic disease and death, and modestly improved risk discrimination beyond some existing measures. The observational study does not show that specific foods or habits cause biological aging.
According to the study’s report, the research team analyzed samples from participants in the Shanghai Pudong Cohort, recruited across six centers. They first built a metabolomic age 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 participants’ mean ages were about 55 in the healthy groups and 68 in the disease group.
The study describes the model as estimating a person’s metabolomic age from chemicals measured in plasma. Researchers compared that estimate with actual age to calculate metabolomic age acceleration, or mAA. For participants with existing conditions, they also developed an age-deviation measure based on how metabolite-linked mortality risk compared with what would be expected for a person’s age.
The study report says a panel of 56 age-related metabolites retained most of the age-predictive information in a larger set of 232 metabolite features. Higher mAA was associated with greater mortality risk even after researchers accounted for established health measures. People with accelerated mAA were also more likely to develop conditions including diabetes, hypertension and fatty liver disease. Both measures modestly improved the separation of higher- and lower-risk participants beyond frailty assessments, cardiovascular health scores and routine blood biomarkers.
How Blood Measures Could Refine Risk
The findings matter because chronological age is an incomplete measure of health. If further studies validate these blood-based measures, they could help researchers or health systems stratify risk across populations and identify groups who may benefit from closer monitoring. The study reports associations, not a ready-to-use clinical test or a confirmed way to prevent illness.
The study authors reported that the improvement in distinguishing mortality risk was modest; their report does not establish that using either measure to guide care would improve health outcomes. The results should not be interpreted as an individual diagnosis or as evidence that a particular food or behavior will change someone’s biological age.
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How Researchers Built the Measures
According to the study report, baseline assessments took place in 2019. Researchers reassessed chronic disease status in 2023 and tracked deaths through January 2025. In 2023, 3,745 people in the healthy cohort and 4,658 in the disease cohort completed repeat surveys and updated disease assessments; 1,500 also had repeat plasma metabolomic profiling.
The study’s questionnaires recorded diet, lifestyle, sleep, mental health and family medical history where available. The researchers reported that higher mAA was associated with factors including active and secondhand smoking, shift work history and parental history of premature cardiovascular disease. They also reported associations with dietary patterns and exercise. Repeat analyses linked increased physical activity and lower fried-food intake with more favorable mAA changes; among participants with hypertension, reaching blood pressure below 140/90 mmHg was associated with a more favorable change in the mortality-linked measure. These are observational associations, not proof of cause and effect.
The study report says the research team also tested its ensemble approach using data from the UK Biobank, the U.S. National Health and Nutrition Examination Survey and human gut microbiome data. The authors describe biological pathways involving amino-acid and other metabolism, but these findings do not by themselves show how a particular pathway causes aging or disease.
Limits of the Evidence
The study does not show that metabolomic age measures can diagnose disease, determine an individual’s lifespan or replace routine clinical assessments. The authors described the risk-discrimination gains as modest, and the study report provides no evidence that acting on the scores improves outcomes.
Because the lifestyle and disease findings are observational, it remains unclear whether the associated habits or exposures directly change the measures, whether other factors explain the links, or how well the results would generalize beyond this cohort. The report also does not specify a standardized clinical threshold for an individual to be considered at high risk.
Validation Before Clinical Use
The study’s findings point to a need for further research to test the measures in other populations and determine whether they add useful information in real-world care. Researchers would also need to establish how reproducible the tests are, define clinically meaningful thresholds and assess whether score-guided decisions improve health outcomes.
For now, the study reports population-level associations from a large cohort, rather than a clinical recommendation. It does not establish that changing diet, exercise or another exposure will lower a person’s metabolomic age or mortality risk.
Key Questions
What does metabolomic age mean?
It is an estimate based on patterns of chemicals in blood plasma. In this study, researchers compared that estimate with a participant’s actual age to calculate metabolomic age acceleration.
Did the study prove that diet or exercise changes biological aging?
No. The researchers reported associations between some diet and lifestyle factors and the measures. The findings were observational and do not establish that those factors caused changes in biological aging or disease risk.
Can this measure predict an individual’s risk of death?
The study found associations with mortality risk across participants, and the measures modestly improved risk discrimination beyond some established assessments. It does not establish a precise individual prediction or a clinical test for determining lifespan.
Who took part in the study?
The analysis included 18,476 participants in the Shanghai Pudong Cohort, ranging from 12 months to 96 years old. Researchers divided them into healthy development and validation groups and a disease cohort.
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