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

Perry J. Pickhardt (), Michael W. Kattan, Matthew H. Lee, B. Dustin Pooler, Ayis Pyrros, Daniel Liu, Ryan Zea, Ronald M. Summers and John W. Garrett
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Perry J. Pickhardt: 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
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

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: 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

Date: 2025
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DOI: 10.1038/s41467-025-56741-w

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