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Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity

Author

Listed:
  • Perry J. Pickhardt

    (University of Wisconsin School of Medicine & Public Health
    University of Wisconsin School of Medicine & Public Health)

  • Michael W. Kattan

    (Cleveland Clinic)

  • Matthew H. Lee

    (University of Wisconsin School of Medicine & Public Health)

  • B. Dustin Pooler

    (University of Wisconsin School of Medicine & Public Health)

  • Ayis Pyrros

    (Duly Health and Care
    University of Illinois-Chicago)

  • Daniel Liu

    (University of Wisconsin School of Medicine & Public Health)

  • Ryan Zea

    (University of Wisconsin School of Medicine & Public Health)

  • Ronald M. Summers

    (National Institutes of Health Clinical Center)

  • John W. Garrett

    (University of Wisconsin School of Medicine & Public Health
    University of Wisconsin School of Medicine & Public Health)

Abstract

We derive and test a CT-based biological age model for predicting longevity, using an automated pipeline of explainable AI algorithms that quantifies skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs. We apply these AI tools to abdominal CT scans from 123,281 adults (mean age, 53.6 years; 47% women; median follow-up, 5.3 years). The final weighted CT biomarker selection was based on the index of prediction accuracy. The CT model significantly outperforms standard demographic data for predicting longevity (IPA = 29.2 vs. 21.7; 10-year AUC = 0.880 vs. 0.779; p

Suggested Citation

  • Perry J. Pickhardt & Michael W. Kattan & Matthew H. Lee & B. Dustin Pooler & Ayis Pyrros & Daniel Liu & Ryan Zea & Ronald M. Summers & John W. Garrett, 2025. "Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56741-w
    DOI: 10.1038/s41467-025-56741-w
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    References listed on IDEAS

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    1. B. A. Jonsson & G. Bjornsdottir & T. E. Thorgeirsson & L. M. Ellingsen & G. Bragi Walters & D. F. Gudbjartsson & H. Stefansson & K. Stefansson & M. O. Ulfarsson, 2019. "Brain age prediction using deep learning uncovers associated sequence variants," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    2. Hamilton Se-Hwee Oh & Jarod Rutledge & Daniel Nachun & Róbert Pálovics & Olamide Abiose & Patricia Moran-Losada & Divya Channappa & Deniz Yagmur Urey & Kate Kim & Yun Ju Sung & Lihua Wang & Jigyasha T, 2023. "Organ aging signatures in the plasma proteome track health and disease," Nature, Nature, vol. 624(7990), pages 164-172, December.
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