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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

María Moreno‐Villanueva, Michael Junk, Grażyna Mosieniak, Ewa Sikora, Miriam Capri, Paolo Garagnani, Chiara Pirazzini, Nicolle Breusing, Jürgen Bernhardt, Christiane Schön, María Blasco, Gerben Zondag, Florence Debacq‐Chainiaux, Beatrix Grubeck‐Loebenstein, Birgit Weinberger, Simone Fiegl, Eugenio Mocchegiani, Marco Malavolta Orcid Logo, Robertina Giacconi, Francesco Piacenza, Sebastiano Collino, Efstathios S. Gonos Orcid Logo, Daniela Gradinaru Orcid Logo, Martijn E. T. Dollé, Eugène Jansen, Michel Salmon, Peter Kristensen Orcid Logo, 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 Orcid Logo

Aging Cell, Volume: 25, Issue: 3, Start page: e70437

Swansea University Author: Helen Griffiths

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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...

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Published in: Aging Cell
ISSN: 1474-9718 1474-9726
Published: Wiley 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71696
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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. 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spelling v2 71696 2026-04-01 Biologically Younger Individuals, as Identified by MARK-AGE Biological Age Scores, Display a Distinct Favourable Blood Chemistry Profile Regardless of Age 0366ea9a689b222146b7d63c6baf8427 Helen Griffiths Helen Griffiths true false 2026-04-01 SMT 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. Journal Article Aging Cell 25 3 e70437 Wiley 1474-9718 1474-9726 biochemical markers; biological age score; human 13 3 2026 2026-03-13 10.1111/acel.70437 COLLEGE NANME Senior Leadership Team COLLEGE CODE SMT Swansea University Other European Commission. Grant Number: HEALTH-F4-2008-200880 2026-04-01T10:58:17.6633310 2026-04-01T10:38:55.7194981 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine María Moreno‐Villanueva 1 Michael Junk 2 Grażyna Mosieniak 3 Ewa Sikora 4 Miriam Capri 5 Paolo Garagnani 6 Chiara Pirazzini 7 Nicolle Breusing 8 Jürgen Bernhardt 9 Christiane Schön 10 María Blasco 11 Gerben Zondag 12 Florence Debacq‐Chainiaux 13 Beatrix Grubeck‐Loebenstein 14 Birgit Weinberger 15 Simone Fiegl 16 Eugenio Mocchegiani 17 Marco Malavolta 0000-0002-8442-1763 18 Robertina Giacconi 19 Francesco Piacenza 20 Sebastiano Collino 21 Efstathios S. Gonos 0000-0001-8956-9954 22 Daniela Gradinaru 0000-0001-7666-3108 23 Martijn E. T. Dollé 24 Eugène Jansen 25 Michel Salmon 26 Peter Kristensen 0000-0001-7205-6853 27 Helen Griffiths 28 Claude Libert 29 Valerie Vanhooren 30 Andreas Simm 31 Duncan Talbot 32 Paola Caiafa 33 Maria Giulia Bacalini 34 Michele Zampieri 35 Bertrand Friguet 36 Isabelle Petropoulos 37 P. Eline Slagboom 38 Rudi Westendorp 39 Antti Hervonnen 40 Mikko Hurme 41 Richard Aspinall 42 Sheila Govind 43 Daniela Weber 44 Wolfgang Stuetz 45 Jan H. J. Hoeijmakers 46 Iuliia Gavriushina 47 Oliver R. Sampson 48 Gastone Castellani 49 Michael R. Berthold 50 Tilman Grune 51 Claudio Franceschi 52 Alexander Bürkle 0000-0003-1069-2656 53 71696__36464__aa4dc7d2a6de4d06893e25a78f194f42.pdf 71696.VoR.pdf 2026-04-01T10:43:25.8261199 Output 4265052 application/pdf Version of Record true © 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. true eng http://creativecommons.org/licenses/by/4.0/
title Biologically Younger Individuals, as Identified by MARK-AGE Biological Age Scores, Display a Distinct Favourable Blood Chemistry Profile Regardless of Age
spellingShingle Biologically Younger Individuals, as Identified by MARK-AGE Biological Age Scores, Display a Distinct Favourable Blood Chemistry Profile Regardless of Age
Helen Griffiths
title_short Biologically Younger Individuals, as Identified by MARK-AGE Biological Age Scores, Display a Distinct Favourable Blood Chemistry Profile Regardless of Age
title_full Biologically Younger Individuals, as Identified by MARK-AGE Biological Age Scores, Display a Distinct Favourable Blood Chemistry Profile Regardless of Age
title_fullStr Biologically Younger Individuals, as Identified by MARK-AGE Biological Age Scores, Display a Distinct Favourable Blood Chemistry Profile Regardless of Age
title_full_unstemmed Biologically Younger Individuals, as Identified by MARK-AGE Biological Age Scores, Display a Distinct Favourable Blood Chemistry Profile Regardless of Age
title_sort Biologically Younger Individuals, as Identified by MARK-AGE Biological Age Scores, Display a Distinct Favourable Blood Chemistry Profile Regardless of Age
author_id_str_mv 0366ea9a689b222146b7d63c6baf8427
author_id_fullname_str_mv 0366ea9a689b222146b7d63c6baf8427_***_Helen Griffiths
author Helen Griffiths
author2 María Moreno‐Villanueva
Michael Junk
Grażyna Mosieniak
Ewa Sikora
Miriam Capri
Paolo Garagnani
Chiara Pirazzini
Nicolle Breusing
Jürgen Bernhardt
Christiane Schön
María Blasco
Gerben Zondag
Florence Debacq‐Chainiaux
Beatrix Grubeck‐Loebenstein
Birgit Weinberger
Simone Fiegl
Eugenio Mocchegiani
Marco Malavolta
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
format Journal article
container_title Aging Cell
container_volume 25
container_issue 3
container_start_page e70437
publishDate 2026
institution Swansea University
issn 1474-9718
1474-9726
doi_str_mv 10.1111/acel.70437
publisher Wiley
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
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description 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.
published_date 2026-03-13T10:58:19Z
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