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Researchers used blood metabolite data from 18,476 people aged 1 to 96 to develop measures of metabolic aging 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 study cannot show that lifestyle factors caused the differences.
Researchers analyzing blood samples from 18,476 people aged 1 to 96 developed two measures of metabolomic aging and found that higher-risk readings were associated with chronic disease and death. The study, published in Science Translational Medicine, suggests blood metabolite patterns may add information to health-risk assessments, although the improvements in distinguishing mortality risk were modest and do not establish cause and effect.
The study drew on the Shanghai Pudong Cohort across six centers in Shanghai, China. Researchers used nano-assisted laser desorption/ionization mass spectrometry to analyze plasma, the liquid component of blood. They developed 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 people.
For the healthy group, the team estimated a person’s metabolomic age and calculated metabolomic age acceleration, or how far that estimate differed from their actual age. For participants with existing conditions, researchers developed a separate age-deviation measure based on how their metabolite-linked mortality risk compared with what was expected for their age. The team reported that a panel of 56 metabolites retained most of the age-predictive information in the full set of 232 features.
Baseline assessments took place in 2019. Chronic disease status was reassessed in 2023, and deaths were tracked through January 2025. Higher metabolomic age acceleration was associated with later diabetes, hypertension and fatty liver disease, as well as higher risk of death from any cause. The two measures also added some ability to distinguish mortality risk beyond existing assessments, including frailty measures, cardiovascular health scores and routine blood tests.
How Blood Patterns Could Add Risk Information
The findings speak to a limitation of using calendar age alone: people of the same age can have different health histories and risk profiles. A measure derived from multiple circulating metabolites could potentially help identify differences that are not captured by age or routine health measures, the researchers suggest.
That possibility is not the same as a ready-to-use clinical test. The reported gains in mortality-risk discrimination were modest, and the study does not show that using either metric to guide care would improve health outcomes. The research may inform future population-level risk assessment, but its practical value for individual patients still needs evaluation.
The study also reported associations between higher metabolomic age acceleration and factors including active and secondhand smoking, shift work history, and parental history of premature cardiovascular disease. Food and activity patterns were also associated with the measure. These results can help identify questions for further research, but they do not show that any one exposure or behavior caused a person to age faster.
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How the Shanghai Cohort Was Followed
The analysis combined a healthy group used to build the age model, a separate healthy validation group, and participants who already had health conditions. This design let researchers examine both how metabolite patterns related to age and how the measures performed among people with disease. Participants completed questionnaires on diet, lifestyle, sleep, mental health and family medical history where information was available.
In 2023, thousands of participants completed repeat surveys and updated chronic disease assessments; 1,500 underwent repeat blood metabolomic profiling. The researchers also tested their ensemble approach by retraining it on data from the UK Biobank, the U.S. National Health and Nutrition Examination Survey and human gut microbiome data. The report describes these as tests of the approach using other data types, not as evidence that the Shanghai findings have been validated as a clinical tool in those populations.
“The findings highlight the potential of plasma metabolomic profiling for future population-level risk assessment and improved risk stratification.”
— The study authors, as described in the News-Medical report
Clinical Use and Cause Remain Unsettled
The results are associations from an observational cohort, so they cannot establish that diet, exercise, smoking or other factors caused changes in metabolomic age or mortality risk. The report also does not provide evidence that changing the metric would prevent disease or extend life.
It remains unclear how well the measures would perform in other populations or in routine care, and whether the modest improvement in risk discrimination would alter clinical decisions. The source report does not specify a threshold that patients or clinicians could use to interpret an individual result. The findings therefore should not be treated as a diagnosis or as a substitute for medical assessment.
Further Validation Before Clinical Adoption
The next step is independent evaluation of the measures in other populations and research into whether they provide useful information beyond established clinical tools. Researchers would also need to determine how stable the readings are over time, what level of risk they indicate, and whether acting on them improves outcomes.
Until those questions are answered, the study supports further investigation of blood metabolite patterns as a possible way to refine population risk assessment. It does not establish a new standard test or prescribe changes to an individual’s care.
Key Questions
What did the researchers measure?
They analyzed plasma metabolites—small molecules circulating in the liquid part of blood—and used the patterns to estimate metabolomic age or age-related mortality risk.
Does the study show that blood can determine a person’s true age?
No. It developed measures linked to biological aging and health risks, not a replacement for calendar age or a definitive measure of an individual’s overall health.
Did particular foods or exercise cause changes in biological aging?
The study found associations between reported behaviors and metabolomic age acceleration. Because it was observational, it cannot establish cause and effect.
Can these measures be used as a clinical test now?
The report does not establish them as routine clinical tests. Their added ability to distinguish mortality risk was described as modest, and further validation is needed.
Source: rss
