Evidence mapPaperPMID 41896267Full record

ArticleCommunications medicine2026

Comprehensive cross-sectional and longitudinal comparison of sixteen markers of biological aging from the Berlin Aging Study II.

Valentin Max Vetter, Johanna Drewelies, Jan Homann, Sandra Düzel, Laura Deecke, Philippe Jawinski, Simone Kühn, Elisa Kubala, Sebastian Markett, Michael Mülleder and 6 more

Abstract read
In one paragraph

Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Valentin Max VetterCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Endocrinology and Metabolic Diseases (including Division of Lipid Metabolism), Biology of Aging Working Group, Augustenburger Platz 1, Berlin, Germany. valentin.vetter@charite.de.ORCID http://orcid.org/0000-0001-5003-7766
Johanna DreweliesCenter for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany.ORCID http://orcid.org/0000-0002-3774-2169
Jan HomannInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.ORCID http://orcid.org/0000-0003-2791-7065
Sandra DüzelFriede Springer Cardiovascular Prevention Center, Charité - Universitätsmedizin Berlin (CBF), Berlin, Germany.
Laura DeeckeInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
Philippe JawinskiDepartment of Psychology, Humboldt University Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0002-2994-3075
Simone KühnCenter for Environmental Neuroscience, Max-Planck-Institut für Bildungsforschung, Berlin, Germany.ORCID http://orcid.org/0000-0001-6823-7969
Elisa KubalaCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Endocrinology and Metabolic Diseases (including Division of Lipid Metabolism), Biology of Aging Working Group, Augustenburger Platz 1, Berlin, Germany.
Sebastian MarkettDepartment of Psychology, Humboldt University Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0002-0841-3163
Michael MüllederCore Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0001-9792-3861
Markus RalserThe Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0001-9535-7413
Ulman LindenbergerMax Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and, London, UK.
Christina M LillInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.ORCID http://orcid.org/0000-0002-2805-1307
Denis GerstorfDepartment of Psychology, Humboldt University Berlin, Berlin, Germany.
Lars BertramLübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany.ORCID http://orcid.org/0000-0002-0108-124X
Ilja DemuthCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Endocrinology and Metabolic Diseases (including Division of Lipid Metabolism), Biology of Aging Working Group, Augustenburger Platz 1, Berlin, Germany. ilja.demuth@charite.de.ORCID http://orcid.org/0000-0002-4340-2523

Funding

Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) #01UW0808; #16SV5536K, #16SV5537, #16SV5538, #16SV5837, #01GL1716A, and #01GL1716BCure Alzheimer's Fund (Alzheimer's Disease Research Foundation) CIR-CUITSDeutsche Forschungsgemeinschaft (German Research Foundation) 460683900Deutsche Forschungsgemeinschaft (German Research Foundation) LI 2654/4-1EU Joint Programme - Neurodegenerative Disease Research (Programi i Përbashkët i BE-së për Kërkimet mbi Sëmundjet Neuro-degjeneruese) JPND2021-650-289
6 · The paper itself

Abstract

backgroundThe disproportionate increase in lifespan compared to healthspan over the past decades results in a growing proportion of life marked by diseases, even if incidence rates are falling in some cases. However, not everyone ages at the same pace and some people remain in good health and preserve physical and cognitive function into old age. To quantify inter-individual differences in the biological aging process, numerous indicators of biological age have been developed.

methodsIn this study, we analyzed 16 measures of biological aging including epigenetic clocks, proteomics clock, telomere length, and SkinAge, laboratory composite markers (BioAge, Allostatic Load), psychological aging, and Brain Age. These age markers were evaluated cross-sectionally as well as longitudinally in the context of age-associated outcomes covering frailty, mobility, cognitive function, depressive symptoms, autonomy in daily life, nutrition, morbidity, and chronic disease in participants of the Berlin Aging Study II (BASE-II).

resultsHere, we analyze longitudinal data from 1083 participants (mean age of 68.3 years at baseline, 52% women) with an average follow-up period of 7.4 years. Allostatic Load Index and DunedinPACE show the strongest and most consistent cross-sectional and longitudinal associations with age-associated phenotypes. Furthermore, both biomarkers individually increase the accuracy of a logistic regression model trained to predict incident cases of Metabolic Syndrome, high cardiovascular risk (Lifes's Simple 7) as well as incident frailty (Fried's frailty index) 7.4 years after baseline examination by up to 24 percentage points.

conclusionsOur findings support the previously shown distinction between indicators of aging and provide a comprehensive overview of their individual strengths and weaknesses in the context of wide variety of age-associated phenotypes.

Identifiers

PMID41896267
PMCPMC13031708

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.