Automated Abdominal Aortic Calcification Scores and Atherosclerotic Cardiovascular Disease in the UK Biobank Imaging Study

JACC Advances 2026 Open access

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Authors: Marc Sim, James Webster, Cassandra Smith, Afsah Saleem, Syed Zulqarnain Gilani, Carlos J. Toro-Huamanchumo, David Suter, Gemma Figtree, Anne Karine Lagendijk, Emma L. Duncan, Carl Schultz, Pawel Szulc
+13 more Joseph Hung, Wai H. Lim, Parminder Raina, Nicola P. Bondonno, Richard Woodman, Jonathan M. Hodgson, Douglas P. Kiel, Richard L. Prince, William D. Leslie, John P. Kemp, Nicholas C. Harvey, John T. Schousboe, Joshua Lewis
DOI: 10.1016/j.jacadv.2025.102570
Cited by: 4
References: 32

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 (

Fields of Study & Subjects

Parathyroid Disorders and Treatments Cardiac Imaging and Diagnostics Aortic aneurysm repair treatments

Publication details

Journal / source
JACC Advances
Publisher
Elsevier BV
Volume
5
Issue
3
Pages
102570–102570
Publication date
29 January 2026
Language
EN