Abstract
Organ-specific aging clocks hold great potential in reflecting organ health. In vivo imaging is inherently organ-specific and delineates structural and functional characteristics more objectively. However, there is no systematic evaluation of imaging-based aging clocks. We utilized 1777 imaging-derived phenotypes (IDPs) from 11,000 healthy participants and assessed the organ-specific biological age of seven organs. The organ-specific age gap was primarily associated with incident diseases and mortality related to corresponding organs. The top-contributing IDPs to organ-specific biological age emerged as biomarkers for incident disease predictions, achieving an area under the curve (AUC) greater than 0.8 for dementia (AUC = 0.82). Subsequent proteomic analysis revealed 966 shared and 507 organ-specific molecular signatures for the aging of different organs. Finally, we identified key modifiable factors and 14 drug targets for organ-specific aging. The imaging-based aging clocks demonstrate organ-specificity at both macro and micro scales, which could promote personalized intervention and treatment of organ aging.
Data availability
The plasma proteomic, metabolic, imaging, health outcomes, and phenotype data are publicly available at the official website of UK Biobank (http://www.ukbiobank.ac.uk/) and were used following the application no. 19542 and 202239.
Code availability
All software and methods used in our study are publicly available and described in the Methods. The code for the main analysis of this study is publicly available at https://github.com/hitrp/MultiOrganImagingAging/.
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Acknowledgements
H.W. was supported by grants from the National Natural Science Foundation of China (No. 62331021). W.C. was supported by grants from the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2025ZD0546300), the National Key R&D Program of China (No. 2023YFC3605400), the National Natural Science Foundation of China (No. 82472055, No. 62433008), the Shanghai Pilot Program for Basic Research—Fudan University 21TQ1400100 (25TQ010), and Shanghai Science and Technology Commission Program (23JS1410100). J.-T.Y. was supported by grants from the Science and Technology Innovation 2030 Major Projects (no. 2022ZD0211600), the National Natural Science Foundation of China (nos 82071201, 81971032 and 92249305), the Shanghai Municipal Science and Technology Major Project (no. 2018SHZDZX01), the Research Start-Up Fund of Huashan Hospital (no. 2022QD002), the Excellence 2025 Talent Cultivation Program at Fudan University (no. 3030277001), Shanghai Talent Development Funding for the Project (no. 2019074), and the Zhangjiang Lab, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University. J.-F.F. was supported by the National Key R&D Program of China (nos 2018YFC1312904 and 2019YFA0709502), the Shanghai Municipal Science and Technology Major Project (no. 2018SHZDZX01), the 111 Project (no. B18015), the Shanghai Center for Brain Science and Brain-Inspired Technology and the Zhangjiang Lab. P.R. was funded by China Postdoctoral Science Foundation (2025M772197 and GZC20230530). We thank all participants and team members of the UK Biobank. All icons were created with BioRender.com. The funders had no role in study design, data collection and analysis or preparation of the manuscript.
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W.C., J.Y., and H.W.: Conceptualization, supervision, project administration, writing—reviewing and editing, and funding acquisition. P.R.: Software, formal analysis, hardware, validation, data analysis, and writing—original draft preparation, reviewing and editing. W.S.: Formal analysis and visualization. J.Y. and Y.L.: Methodology, formal analysis, and writing—reviewing and editing. W.G., W.Z., Z.Z., X.H., W.L., and J.F.: Methodology and writing—reviewing and editing. F.D.: Hardware and technique support. All authors have read and approved the manuscript.
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Ren, P., Su, W., You, J. et al. Imaging-based organ-specific aging clock predicts human diseases and mortality. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02488-7
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DOI: https://doi.org/10.1038/s41746-026-02488-7
