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

Abstract Background Understanding human ageing across multiple organs is essential for characterising individual health trajectories and identifying abnormal ageing processes. Multiorgan 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. Methods In 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. Results Here 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. Conclusions This 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. Plain Language Summary As people age, different organs in the body might not always age at the same pace. Understanding these differences can help to explain a person’s health and why they develop diseases earlier than others. In this study, we measure how ageing varies across the body using medical images. We analysed about 70,000 whole-body scans from large population studies in the United Kingdom and Germany. Using AI models, we estimated a person’s biological age from images of different organs and compared it with their actual age. We found that faster ageing in specific organs is linked to certain diseases (such as multiple sclerosis) and lifestyle factors (like smoking and physical activity). These findings may help improve early disease detection and support more personalised approaches to health and ageing in the future.