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Biologically Younger Individuals, as Identified by MARK-AGE Biological Age Scores, Display a Distinct Favourable Blood Chemistry Profile Regardless of Age
,
Robertina Giacconi,
Francesco Piacenza,
Sebastiano Collino,
Efstathios S. Gonos ,
Daniela Gradinaru ,
Martijn E. T. Dollé,
Eugène Jansen,
Michel Salmon,
Peter Kristensen ,
Helen Griffiths,
Claude Libert,
Valerie Vanhooren,
Andreas Simm,
Duncan Talbot,
Paola Caiafa,
Maria Giulia Bacalini,
Michele Zampieri,
Bertrand Friguet,
Isabelle Petropoulos,
P. Eline Slagboom,
Rudi Westendorp,
Antti Hervonnen,
Mikko Hurme,
Richard Aspinall,
Sheila Govind,
Daniela Weber,
Wolfgang Stuetz,
Jan H. J. Hoeijmakers,
Iuliia Gavriushina,
Oliver R. Sampson,
Gastone Castellani,
Michael R. Berthold,
Tilman Grune,
Claudio Franceschi,
Alexander Bürkle
Aging Cell, Volume: 25, Issue: 3, Start page: e70437
Swansea University Author: Helen Griffiths
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© 2026 DNage B.V. BioTeSys GmbH and The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.
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DOI (Published version): 10.1111/acel.70437
Abstract
Biomarkers of ageing are defined as age-related changes in body function or composition that could serve as a measure of ‘biological’ age and predict the onset of age-related diseases and/or residual life expectancy. We conducted the MARK-AGE Study, a European population study (3300 subjects aged 35...
| Published in: | Aging Cell |
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| ISSN: | 1474-9718 1474-9726 |
| Published: |
Wiley
2026
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71696 |
| Abstract: |
Biomarkers of ageing are defined as age-related changes in body function or composition that could serve as a measure of ‘biological’ age and predict the onset of age-related diseases and/or residual life expectancy. We conducted the MARK-AGE Study, a European population study (3300 subjects aged 35–74) to identify a powerful set of biomarkers of ageing. A total of 362 clinical-chemistry, genetic, cellular or molecular biomarkers were analysed for each subject. Using statistical models as well as machine learning we derived mathematical formulas for females and for males that yield a ‘bioage score’ of an individual, based on sets of 10 biomarkers for females and 10 for males. Collectively, these biomarkers model chronological age of our study population and, thus yield the ‘biological’ age of a certain person. ‘Age difference’ (defined as biological minus chronological age) should then identify biologically older or younger individuals. Using our set of biomarkers, subjects with Down Syndrome and smoking females are biologically older, whereas postmenopausal females taking hormone replacement therapy are biologically younger. Strikingly, our data reveal that age difference of MARK-AGE subjects, but not chronological age, is linearly correlated with levels of HDL, 25-hydroxy-Vitamin D, and CD3+ CD4+/CD45+ ratio in such a way that biologically younger subjects display values that are favourable to good health, whereas other markers such as glucose and HbA1c are correlated with chronological age, but not age difference. This dichotomy of correlations may point to different roles of such markers, that is, drivers of the ageing process versus bystanders of ageing. |
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| Keywords: |
biochemical markers; biological age score; human |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
European Commission. Grant Number: HEALTH-F4-2008-200880 |
| Issue: |
3 |
| Start Page: |
e70437 |