Evidence map›Paper›PMID 42288701›Full record

ArticleCommunications medicine2026

Local and global patterns support medical imaging as a biomarker of ageing.

Tamara T Mueller, Sophie Starck, Rozafë Llalloshi, Georgios Kaissis, Alexander Ziller, Robert Graf, Christopher Schlett, Steffen Ringhof, Fabian Bamberg, Mark Wielpütz and 11 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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

21 authors.

Tamara T Mueller *Lab for AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany. tamara.mueller@tum.de.
Sophie Starck *Lab for AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.
Rozafë LlalloshiLab for AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.
Georgios KaissisLab for AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.
Alexander ZillerLab for AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.
Robert GrafLab for AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.
Christopher SchlettDepartment of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany.
Steffen RinghofDepartment of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany.
Fabian BambergDepartment of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany.
Mark WielpützInstitut für Community Medicine, University Medicine Greifswald, Greifswald, Germany.
Henry VölzkeInstitut für Community Medicine, University Medicine Greifswald, Greifswald, Germany.
Michael LeitzmannInstitute for Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany.
Thoralf NiendorfBerlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine, Helmholtz Association, Berlin, Germany.
Thomas KeilInstitute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Lilian KristInstitute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Tobias PischonBerlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine, Helmholtz Association, Berlin, Germany.
André KarchInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
Klaus BergerInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
Jan KirschkeDepartment of Diagnostic and Interventional Neuroradiology, School of Medicine, TUM University Hospital, Munich, Germany.
Daniel Rueckert *Lab for AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.
Rickmer Braren *Institute for Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnderstanding human ageing across multiple organs is essential for characterising individual health trajectories and identifying abnormal ageing processes. Multi-organ imaging provides an opportunity to quantify biological ageing beyond chronological age. The aim of this study is to assess organ-specific and whole-body ageing patterns and their associations with disease and lifestyle factors.

methodsIn this large-scale study, we evaluate biological ageing patterns using 70,000 MRI scans from the UK Biobank and the German National Cohort. We employ 3D ResNet-18 models to predict chronological age from various body regions (brain, heart, liver, spine, lungs, muscle, and intestine) and the whole body. From these predictions, we derive "age gaps" relative to a strictly healthy reference cohort, which enables the identification of accelerated ageing patterns. We then evaluate associations with chronic diseases and lifestyle factors, and a virtual ageing framework was developed to explore counterfactual scenarios by substituting anatomical regions across subjects, quantifying local impacts on global biological age.

resultsHere we show significant associations between detected accelerated ageing and specific chronic diseases, including multiple sclerosis and chronic obstructive pulmonary disease, as well as lifestyle factors such as smoking and physical activity. Virtual substitution of anatomical regions demonstrates that local substitutions can influence global ageing patterns.

conclusionsThis study demonstrates that multi-organ imaging enables the detection of abnormal ageing patterns at both local and global levels. The presented framework provides a foundation for improved risk stratification and supports the development of personalised approaches to health assessment and disease prevention.

Identifiers

PMID42288701
PMCPMC13264634

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