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

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.; Prince R.L.; Leslie W.D.; Kemp J.P.; Harvey N.C.; Schousboe J.T.; Lewis J.R. · JACC: Advances (2026) · DOI: 10.1016/j.jacadv.2025.102570

Resumen

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.

Instituciones: Universidad San Ignacio de Loyola