A new study suggests that people who live past 100 may carry unique metabolic “fingerprints” in their blood that differ from typical aging patterns.
What allows some people to live past 100 while staying remarkably healthy?
Scientists have long known that exceptional longevity is shaped by a combination of genetics and lifestyle, from plant-rich diets and lifelong physical activity to strong social bonds. But beyond these visible habits, researchers are increasingly searching for hidden biological signatures that may help explain why some bodies resist the usual effects of aging for decades longer than others.
Now, researchers at Boston University Chobanian & Avedisian School of Medicine have identified a distinctive metabolic profile in centenarians that appears to set them apart from normal aging altogether. Their blood contained unusually high levels of certain primary and secondary bile acids, along with preserved levels of several steroids—patterns linked to lower mortality risk and rarely seen in younger elderly populations. The findings suggest that people who achieve extreme longevity may follow a unique biological pathway, offering new insight into the chemistry of healthy aging.
Blood chemistry signals unusual aging
“Our study points to measurable chemical fingerprints in the blood that are associated with living a very long and healthy life. If we can understand those fingerprints, we may identify biological pathways that could contribute to protecting people from age-related decline,” explains corresponding author Stefano Monti, PhD, professor of medicine at the school.
Centenarians tested across generations
The researchers analyzed blood samples from 213 people (70 centenarians, their children (offspring), and age-matched controls) who were part of the New England Centenarian Study, one of North America’s largest studies of people with exceptional longevity, led by Thomas Perls, MD, professor of medicine at the school. Using an untargeted metabolomics assay, they measured about 1,495 small molecules in serum.
They compared metabolite levels among centenarians, offspring, and controls, then examined which metabolites shifted with chronological age. They also compared their results with four other metabolomics studies (some that included long-lived people and some that did not) to identify signals that appeared consistently. Next, they assessed which individual metabolites or metabolite groups were linked to how long participants lived after their blood was collected (survival analysis).
Finally, they built a model called a (“metabolomic clock”) to estimate biological age from metabolite levels and tested whether being biologically younger or older than one’s calendar age was connected to survival.
Metabolic pathways become targets
According to the researchers, these metabolites and broader patterns may eventually serve as biomarkers for estimating biological age, identifying people at higher or lower risk of aging-related decline, or tracking how someone responds to lifestyle changes or drugs designed to support healthier aging. They say several pathways (bile acids, NAD-related pathways, gut bacterial metabolites, oxidative stress markers, and certain steroids) deserve closer study as possible future targets for therapies or dietary approaches.
“We hope this study helps point to measurable metabolic signs of healthy aging that can be tracked and targeted. However, the study’s cross-sectional design means we cannot yet determine cause and effect, and these findings need validation in larger, diverse populations. Ultimately, our goal is to translate these insights into tests and safe interventions that help people stay healthier and more active for longer,” adds Monti.
Reference: “Metabolomic signatures of extreme old age: findings from the New England Centenarian Study” by Stefano Monti, Michael S. Lustgarten, Ziwei Huang, Zeyuan Song, Mengze Li, Dylan Ellis, Qu Tian, Luigi Ferrucci, Noa Rappaport, Stacy L. Andersen, Thomas P. Perls and Paola Sebastiani, 27 March 2026, GeroScience.
DOI: 10.1007/s11357-026-02174-2
This work was supported by the National Institutes of Health, NIA cooperative agreements U19AG023122, U19AG063893, UH2AG064704, NIH grant R01-AG061844, and Find the Cause Breast Cancer Foundation (findthecausebcf.org). It was also supported in part by the Intramural Research Program of the National Institutes of Health (NIH).
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