TL;DR
Get health and wellness essentials delivered free with Prime
- Fast, free delivery on millions of items
- Prime Video, Amazon Music and more included
- Member-only deals all year
Researchers used blood metabolite data from 18,476 people aged 1 to 96 to develop measures of metabolomic aging and mortality risk. The measures were associated with chronic disease and death, but the study does not show that particular foods or lifestyle changes cause biological aging or that the metrics are ready for clinical use.
Researchers analyzed blood samples from 18,476 people aged 1 to 96 to develop measures of metabolomic aging and mortality risk, reporting associations with chronic disease and death. The study, published in Science Translational Medicine, suggests blood chemistry may add information to conventional health measures, but it does not establish that the metrics can predict an individual’s future health with certainty.
According to the study in Science Translational Medicine, the researchers used plasma samples from participants in the Shanghai Pudong Cohort, recruited across six centers in Shanghai. They first built a metabolomic age model using 5,252 healthy participants, then tested it in a separate healthy group of 5,456 people and examined a disease cohort of 7,768 participants. The average ages in the healthy and disease groups were about 55 and 68, respectively.
The study authors estimated metabolomic age for healthy participants and calculated metabolomic age acceleration, or how much that estimate differed from a person’s chronological age. For people with existing health conditions, they developed an age-deviation measure based on how metabolite-associated mortality risk differed from the risk expected for someone of the same age. The paper reports that a panel of 56 metabolites retained most of the age-predictive information found across 232 metabolite features.
As described in the paper, baseline assessments took place in 2019; chronic disease status was reassessed in 2023, and deaths were tracked through January 2025. The researchers found that higher metabolomic age acceleration was associated with later diagnoses including diabetes, hypertension and fatty liver disease, as well as higher risk of death from any cause. The authors also reported modest improvements in mortality-risk discrimination when the measures were added to assessments such as frailty, cardiovascular health scores and routine blood biomarkers.
What Blood-Based Aging Measures Could Add
Chronological age is a simple measure, but people of the same age can have different health profiles. In their Science Translational Medicine study, the researchers tested whether patterns of small molecules in blood could offer another way to distinguish those profiles. The reported associations suggest metabolomic measures may help identify higher-risk groups for research or future population-level risk assessment.
The potential is not the same as a validated clinical test. The study authors reported that the measures improved risk discrimination only modestly, and their findings do not show that using them to guide care improves health outcomes. Their relevance to readers is therefore a research finding, not a reason to interpret a blood result as a diagnosis or a personal forecast.
The study also reported associations between higher acceleration scores and factors including smoking exposure, shift work, some dietary patterns and parental history of premature cardiovascular disease. Those findings may help researchers investigate how health and environment relate to metabolic aging, but they do not prove that any one factor caused the score or a later illness.
As an affiliate, we earn on qualifying purchases.
How the Cohort Was Studied
As detailed in the study, the researchers used nano-assisted laser desorption/ionization mass spectrometry to profile plasma metabolites. They combined several modeling and feature-selection approaches to estimate age-related patterns and assess mortality risk. Questionnaires captured information on diet, lifestyle, sleep, mental health and family history where available.
The paper reports that some participants completed follow-up surveys and updated disease assessments in 2023; a smaller group of 1,500 people had repeat metabolomic profiling. The team also tested its ensemble approach using other datasets, including UK Biobank proteomic data, U.S. health survey physiological biomarkers and human gut microbiome data. The main cohort, however, was drawn from Shanghai, so results may not apply identically to other populations.
The analysis identified pathways involving amino-acid and other metabolism, including threonine, serine, glycine, butanoate, cysteine and methionine. The study authors also described individual metabolites associated with aging patterns. These are statistical signals from the study, not proof that changing a particular metabolite would slow aging.
As an affiliate, we earn on qualifying purchases.
Limits of the Evidence
The findings reported in Science Translational Medicine are observational. Although higher metabolomic age acceleration was associated with disease and mortality, the study cannot establish cause and effect. The researchers also cautioned that reported links between scores and eating patterns or activity do not prove that specific foods or lifestyle changes directly alter biological aging.
The available study report does not provide enough detail to assess the precise size of every association or the model’s performance for individual patients. It also remains unclear how well the metrics would perform in populations outside the Shanghai cohort, whether they would add useful information in routine care, and what thresholds would be appropriate for interpreting a result. The authors described the reported improvement in risk discrimination as modest, and the study did not establish clinical benefit from using the scores.
As an affiliate, we earn on qualifying purchases.
Validation Before Clinical Use
As the study authors’ findings do not establish clinical readiness, further research would need to test the measures in independent and more diverse populations, clarify how reliably they predict outcomes, and determine whether they add value beyond existing health assessments. Studies would also need to examine whether decisions guided by the metrics improve outcomes before they could be considered useful in routine clinical practice.
For now, the study offers evidence that blood metabolite patterns track with aging-related health risks in this cohort, not a standalone test for an individual’s biological age. The published report does not specify a planned clinical rollout or a date for additional validation results.
As an affiliate, we earn on qualifying purchases.
Key Questions
What does metabolomic age mean?
It is an estimate based on patterns of metabolites in blood plasma. In this study, researchers compared the estimate with chronological age to calculate metabolomic age acceleration.
Does the study show that blood tests can predict how long someone will live?
No. The study found associations between its metrics and mortality risk in a cohort, but it does not establish a certain prediction for any individual or show that the measures are ready for clinical use.
Did particular foods cause faster biological aging?
The researchers reported associations between some dietary patterns and higher or lower scores. Because the findings were observational, they do not establish that specific foods caused changes in biological aging.
How many people were included?
The analysis covered 18,476 participants in the Shanghai Pudong Cohort, aged 12 months to 96 years, divided among model-development, healthy-validation and disease groups.
Could this measure be used in a doctor’s office now?
The report does not establish routine clinical use. The measures’ added risk discrimination was described as modest, and further validation and evidence of clinical benefit are needed.
Source: rss
Halloween Picks
halloween
As an affiliate, we earn on qualifying purchases.
