TL;DR
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Researchers analyzed blood metabolite patterns from 18,476 people aged 1 to 96 and developed measures of metabolomic aging and mortality-related age deviation. The measures were associated with chronic disease and death risk, and modestly improved risk discrimination beyond existing assessments; the observational study does not show that particular foods or habits cause biological aging.
Researchers have developed two measures based on blood metabolite patterns that were associated with chronic disease and mortality risk in a study of 18,476 people aged 1 to 96. Published in Science Translational Medicine, the study suggests these measures could help assess health risk beyond chronological age, but it does not show that the measured patterns—or specific lifestyle factors linked to them—cause illness or faster aging.
The study used plasma samples from participants in the Shanghai Pudong Cohort, recruited across six centers. The team first built a metabolomic age model using 5,252 healthy participants, then tested it in a separate group of 5,456 healthy participants and examined it in a disease cohort of 7,768 people. The mean ages in the healthy and disease groups were 55 and 68, respectively.
For healthy participants, the researchers calculated metabolomic age acceleration, or mAA: the difference between metabolite-estimated age and actual age. For participants with existing health conditions, they developed an age-deviation measure based on how metabolite-associated mortality risk compared with the risk expected for a person’s age. The study’s follow-up included chronic disease reassessments in 2023 and mortality tracking through January 2025.
Higher mAA was associated with greater risk of developing conditions including diabetes, hypertension and fatty liver disease, as well as higher risk of death from any cause. Both measures improved the researchers’ ability to distinguish people at higher and lower mortality risk beyond frailty assessments, cardiovascular health scores and routine blood biomarkers. The reported improvement was modest, and the findings do not establish that the measures are ready for routine clinical use.
Blood-Based Measures and Risk Assessment
The findings matter because chronological age alone does not capture every difference in health. A blood-based measure that reflects patterns across multiple metabolites could, if validated and made practical, help researchers identify people whose health risks differ from those expected for their age. The study also examined both healthy participants and people with existing conditions, offering evidence across a broad age range.
However, the study supports a potential research and risk-stratification tool, not a new diagnosis or a proven way to prevent disease. The gains in distinguishing mortality risk were modest; the report does not provide evidence that using these measures to guide care improves health outcomes. The associations are also not proof that changing a person’s diet or activity level will change their biological aging or lifespan.
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How the Shanghai Study Was Built
The researchers analyzed plasma using nano-assisted laser desorption/ionization mass spectrometry. They developed the healthy-group model with an ensemble method that combines multiple algorithms. A selected panel of 56 age-related metabolites retained most of the age-predictive information found across all 232 metabolite features examined.
Baseline assessments took place in 2019. In 2023, participants completed repeat surveys and updated chronic disease assessments; 1,500 also had repeat metabolomic profiling. The researchers collected questionnaire information on factors such as diet, lifestyle, sleep, mental health and family history where available. They also tested the ensemble approach using data from the UK Biobank, the U.S. National Health and Nutrition Examination Survey, and human gut microbiome data.
The analysis identified metabolite patterns involving pathways related to threonine, serine, glycine, butanoate, cysteine and methionine metabolism. The report also linked higher mAA with factors including active and secondhand smoking, shift work history, and parental history of premature cardiovascular disease. These are associations within the study, not evidence that each factor independently changes a person’s aging rate.
“The findings highlight the potential of plasma metabolomic profiling for future population-level risk assessment and improved risk stratification.”
— The study researchers, as summarized in News-Medical
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Limits of the Aging Measures
The study was observational, so it cannot establish that exposures such as smoking, particular foods or exercise caused changes in mAA, disease or mortality. The source report does not provide effect sizes for the associations, making it difficult to judge their size in practical terms. It also does not establish how well the measures would perform in populations outside the Shanghai cohort, despite the researchers’ additional model testing with other datasets.
It remains unclear whether using these measures in healthcare would change decisions or improve outcomes, and the report does not describe a standard clinical threshold for interpreting an individual’s result. The blood measures should not be treated as a diagnosis or a substitute for established medical assessment.
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Validation Before Clinical Use
The next steps are further testing of the measures in independent populations and research into whether they add useful information in real-world health settings. Studies would also need to clarify how stable the measures are over time, what results mean for an individual, and whether acting on them improves outcomes. Until then, the reported findings are best understood as population-level research associations, not a personalized forecast or a reason to change treatment without a qualified health professional.
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Key Questions
What does metabolomic age acceleration mean?
Metabolomic age acceleration is the study’s estimate of how much a person’s metabolite-based age differs from their chronological age. It is a research measure, not a standard diagnosis.
Did the study show that certain foods make people age faster?
No. Higher mAA was associated with greater reported consumption of some foods, while mixed grains and exercise were linked to lower values. The observational study cannot establish cause and effect.
How many people were included?
The researchers analyzed plasma data from 18,476 participants in the Shanghai Pudong Cohort, ranging in age from 12 months to 96 years.
Can these blood measures predict an individual’s lifespan?
The study found associations with mortality risk and modestly improved risk discrimination in its analyses. It does not establish that the measures can accurately predict an individual’s lifespan or guide clinical care.
Source: rss
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