Author Identifier (ORCID)
Marc Sim: https://orcid.org/0000-0001-5166-0605
Cassandra Smith: https://orcid.org/0000-0002-2517-2824
Afsah Saleem: https://orcid.org/0000-0001-7240-0837
Syed Zulqarnain Gilani: https://orcid.org/0000-0002-7448-2327
Carlos J. Toro-Huamanchumo: https://orcid.org/0000-0002-4664-2856
David Suter: https://orcid.org/0000-0001-6306-3023
Wai H. Lim: https://orcid.org/0000-0002-3410-3572
Nicola P. Bondonno: https://orcid.org/0000-0001-5905-444X
Jonathan M. Hodgson: https://orcid.org/0000-0001-6184-7764
Joshua R. Lewis: https://orcid.org/0000-0003-1003-8443
Abstract
Background: Abdominal aortic calcification (AAC) is a subclinical measure of atherosclerotic cardiovascular disease (ASCVD). AAC can be captured on lateral spine images obtained from bone density machines during routine osteoporosis screening. Identifying individuals with AAC provides a new opportunity to prevent disease progression. Objectives: The aim of the study was to externally validate a machine learning-derived AAC 24-point algorithm (ML-AAC24) with incident ASCVD. Methods: Middle-aged individuals from the UK Biobank Imaging Study with lateral spine images, obtained via dual-energy x-ray absorptiometry, were included. ML-AAC24 scores were grouped as low (<2), moderate (2 to <6), and high (≥6). Linked health records were used to identify ASCVD-associated events, including hospitalizations and death. Results: Among 53,611 participants (52% female; mean age 65 years), 78.2% had low, 16.4% had moderate, and 5.4% had high ML-AAC24. After excluding people with prevalent ASCVD or missing data, 1,163 (2.3%) of 50,923 people had an incident ASCVD event over a median follow-up of 4.1 [3.0-5.5] years. In age- and sex-adjusted analysis, compared to those with low ML-AAC24, those with moderate (HR: 1.80 [95% CI: 1.57-2.08]) and high ML-AAC24 (HR: 2.87 [95% CI: 2.39-3.44]) had a higher HR for incident ASCVD. Results remained comparable after adjustment for established ASCVD risk factors. Consistent patterns were observed when considering incident coronary artery disease, myocardial infarction, and stroke. Conclusions: Assessing ML-AAC24 on lateral spine images offers a new and promising screening method to identify people with higher risk of incident ASVD events.
Document Type
Journal Article
Date of Publication
3-1-2026
Volume
5
Issue
3
Publication Title
JACC: Advances
Publisher
Elsevier
School
Nutrition and Health Innovation Research Institute / Centre for Artificial Intelligence and Machine Learning (CAIML) / School of Science
RAS ID
88771
Funders
National Health and Medical Research Council / Medical Research Future Fund 2022 Cardiovascular Health Mission Grant (MRF2024225) / Royal Perth Hospital Research Foundation Fellowship (RPHRF CAF 00/21) / Western Australian Future Health Research and Innovation Fund / National Heart Foundation of Australia (107194, 102817, 107323) / Lions Medical Research Foundation / Forrest Research Foundation Scholarship / Edith Cowan University / Raine Medical Research Foundation / UK Medical Research Council (MRC) [MC_PC_21003 & 21001] / NIHR Southampton Biomedical Research Centre / University of Southampton / University Hospital Southampton NHS Foundation Trust, UK
Grant Number
NHMRC Numbers : APP1183570, GNT1177938, APP1159914
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Sim, M., Webster, J., Smith, C., Saleem, A., Gilani, S. Z., Toro-Huamanchumo, C. J., Suter, D., Figtree, G., Lagendijk, A. K., Duncan, E. L., Schultz, C., Szulc, P., Hung, J., Lim, W. H., Raina, P., Bondonno, N. P., Woodman, R., Hodgson, J. M., Kiel, D. P., . . . Lewis, J. R. (2026). Automated abdominal aortic calcification scores and atherosclerotic cardiovascular disease in the UK Biobank imaging study. JACC: Advances, 5(3), 102570. https://doi.org/10.1016/j.jacadv.2025.102570