| Title: | Automated Abdominal Aortic Calcification Scores and Atherosclerotic Cardiovascular Disease in the UK Biobank Imaging Study |
| Journal: | JACC Advances |
| Published: | 29 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41616586/ |
| DOI: | https://doi.org/10.1016/j.jacadv.2025.102570 |
Publication 18423
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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.</p>
OBJECTIVES: The aim of the study was to externally validate a machine learning-derived AAC 24-point algorithm (ML-AAC24) with incident ASCVD.</p>
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.</p>
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.</p>
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.</p>
25 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
1 Application
| Application ID | Title |
|---|---|
| 93712 | Machine Learning on Densitometric Imaging for Novel Cardiometabolic & Musculoskeletal Health Phenotypes of Disease and their Developmental Origins |
Enabling scientific discoveries that improve human health