Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56741-w |
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| _version_ | 1823861824270893056 |
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| author | Perry J. Pickhardt Michael W. Kattan Matthew H. Lee B. Dustin Pooler Ayis Pyrros Daniel Liu Ryan Zea Ronald M. Summers John W. Garrett |
| author_facet | Perry J. Pickhardt Michael W. Kattan Matthew H. Lee B. Dustin Pooler Ayis Pyrros Daniel Liu Ryan Zea Ronald M. Summers John W. Garrett |
| author_sort | Perry J. Pickhardt |
| collection | DOAJ |
| description | 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 < 0.001). Age- and sex-corrected survival hazard ratio for the highest-vs-lowest risk quartile was 8.73 (95% CI,8.14-9.36) for the CT biological age model, and increased to 24.79 after excluding cancer diagnoses within 5 years of CT. Muscle density, aortic plaque burden, visceral fat density, and bone density contributed the most. Here we show a personalized phenotypic CT biological age model that can be opportunistically-derived, regardless of clinical indication, to better inform risk assessment. |
| format | Article |
| id | doaj-art-9dd571ed0f4d4883ae34c91af2408c14 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9dd571ed0f4d4883ae34c91af2408c142025-02-09T12:45:58ZengNature PortfolioNature Communications2041-17232025-02-0116111110.1038/s41467-025-56741-wBiological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevityPerry J. Pickhardt0Michael W. Kattan1Matthew H. Lee2B. Dustin Pooler3Ayis Pyrros4Daniel Liu5Ryan Zea6Ronald M. Summers7John W. Garrett8The Department of Radiology, University of Wisconsin School of Medicine & Public HealthThe Department of Quantitative Health Sciences, Cleveland ClinicThe Department of Radiology, University of Wisconsin School of Medicine & Public HealthThe Department of Radiology, University of Wisconsin School of Medicine & Public HealthDepartment of Radiology, Duly Health and CareThe Department of Radiology, University of Wisconsin School of Medicine & Public HealthThe Department of Radiology, University of Wisconsin School of Medicine & Public HealthImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical CenterThe Department of Radiology, University of Wisconsin School of Medicine & Public HealthAbstract 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 < 0.001). Age- and sex-corrected survival hazard ratio for the highest-vs-lowest risk quartile was 8.73 (95% CI,8.14-9.36) for the CT biological age model, and increased to 24.79 after excluding cancer diagnoses within 5 years of CT. Muscle density, aortic plaque burden, visceral fat density, and bone density contributed the most. Here we show a personalized phenotypic CT biological age model that can be opportunistically-derived, regardless of clinical indication, to better inform risk assessment.https://doi.org/10.1038/s41467-025-56741-w |
| spellingShingle | Perry J. Pickhardt Michael W. Kattan Matthew H. Lee B. Dustin Pooler Ayis Pyrros Daniel Liu Ryan Zea Ronald M. Summers John W. Garrett Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity Nature Communications |
| title | Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity |
| title_full | Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity |
| title_fullStr | Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity |
| title_full_unstemmed | Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity |
| title_short | Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity |
| title_sort | biological age model using explainable automated ct based cardiometabolic biomarkers for phenotypic prediction of longevity |
| url | https://doi.org/10.1038/s41467-025-56741-w |
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