Automated Abdominal Aortic Calcification Scores and Atherosclerotic Cardiovascular Disease in the UK Biobank Imaging Study
Year: 2026
Type: article
Study design: Observational Study
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.
Source: JACC Advances
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, 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 R. Lewis
Institutions: Edith Cowan University, The University of Western Australia, University of Oxford, Universidad San Ignacio de Loyola, OBEMET Center for Obesity and Metabolic Health, The University of Sydney, The University of Queensland, Institute for Molecular Bioscience, King's College London, Guy's and St Thomas' NHS Foundation Trust, Inserm, Hospices Civils de Lyon, Sir Charles Gairdner Hospital, McMaster University, Flinders University, Beth Israel Deaconess Medical Center, Harvard University, Hebrew SeniorLife, University of Manitoba, Translational Research Institute, Mater Research, University Hospital Southampton NHS Foundation Trust, NIHR Southampton Biomedical Research Centre, University of Southampton, University of Minnesota, Park Nicollet Clinic, HealthPartners
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,936
Funders: University Hospital Southampton NHS Foundation Trust, National Institute for Health and Care Research, University of Southampton, National Heart Foundation of Australia, Raine Medical Research Foundation, Edith Cowan University, Lions Medical Research Foundation, National Institute for Health Research Southampton Biomedical Research Centre, Medical Research Council, National Health and Medical Research Council, Western Australian Future Health and Innovation Fund, Government of Western Australia
Awards: G153099573, G2702737431, G2928335931, G3093781312, G3101364852, G3795045136, G4282075139, Identifying pharmacological targets for osteoporosis intervention using whole genome and exome sequencing of bone related phenotypes, G4874075359, Programme 3: Early development and risk of adult musculoskeletal disease, G5633744563, Variations in flavonoid metabolism as an explanation for inter-individual differences in physiological responses to flavonoid-rich foods, G601069604, G622721441, Automated methods for evaluating structural vascular disease, Improving screening, understanding, prevention and treatment of abdominal aortic calcification, Better identification, understanding and prevention of advanced blood vessel disease in women
OpenAlex ID: W7126052701