| Title: | Local and global patterns support medical imaging as a biomarker of ageing |
| Journal: | Communications Medicine |
| Published: | 13 Jun 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42288701/ |
| DOI: | https://doi.org/10.1038/s43856-026-01722-3 |
Publication 20644
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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.</p>
21 Authors
- Tamara T. Mueller
- Sophie Starck
- Rozafë Llalloshi
- Georgios Kaissis
- Alexander Ziller
- Robert Graf
- Christopher Schlett
- Steffen Ringhof
- Fabian Bamberg
- Mark Wielpütz
- Henry Völzke
- Michael Leitzmann
- Thoralf Niendorf
- Thomas Keil
- Lilian Krist
- Tobias Pischon
- André Karch
- Klaus Berger
- Jan Kirschke
- Daniel Rueckert
- Rickmer Braren
1 Application
| Application ID | Title |
|---|---|
| 87802 | Assessment of frailty and biological age using multi-modal deep learning |
Enabling scientific discoveries that improve human health