Author Identifier
Cassandra Smith: https://orcid.org/0000-0002-2517-2824
Marc Sim: https://orcid.org/0000-0001-5166-0605
Zaid Ilyas: https://orcid.org/0000-0001-6072-2441
Syed Zulqarnain Gilani: https://orcid.org/0000-0002-7448-2327
David Suter: https://orcid.org/0000-0001-6306-3023
Joshua R. Lewis: https://orcid.org/0000-0003-1003-8443
Document Type
Journal Article
Publication Title
Journal of Bone and Mineral Research
Volume
40
Issue
3
First Page
323
Last Page
331
PubMed ID
39749990
Publisher
Oxford Academic
School
Nutrition and Health Innovation Research Institute / School of Medical and Health Sciences / Centre for Artificial Intelligence and Machine Learning (CAIML) / School of Science
Publication Unique Identifier
10.1093/jbmr/zjae208
RAS ID
76591
Funders
Rady Innovation Fund / Rady Faculty of Health Sciences / University of Manitoba / National Health and Medical Research Council of Australia / Medical Research Future Fund 2022 Cardiovascular Health Mission Grant (MRF2024225) / National Heart Foundation (107194, 107323) / Royal Perth Hospital Research Foundation Fellowship (RPHRF CAF 00/21) / Western Australian Future Health Research and Innovation Fund / Raine Medical Research Foundation
Grant Number
NHMRC Number : APP1183570
Abstract
Vertebral fracture assessment (VFA) images from bone density machines enable the automated machine learning assessment of abdominal aortic calcification (ML-AAC), a marker of cardiovascular disease (CVD) risk. The objective of this study was to describe the risk of a major adverse cardiovascular event (MACE, from linked health records) in patients attending routine bone mineral density (BMD) testing and meeting specific criteria based on age, BMD, height loss, or glucocorticoid use have a VFA in the Manitoba BMD Registry. The cohort included 10 250 individuals (mean age 75.5 yr, 94% women without CVD) with VFA (February 2010 to March 2017). ML-AAC24 scores were categorized (low <2; moderate 2–<6; high ≥6). Over follow-up (mean 3.9 yr), 1265 people (12.3%) experienced a MACE. Among those with low, moderate, and high ML-AAC24, MACE rates per 1000 person-years were 18.4 (95% CI 16.4-20.5), 34.1 (95% CI 30.9-37.4), and 55.6 (95% CI 50.8-60.1), respectively. A similar gradient was observed after stratifying by age and sex. Incidence rate ratios (IRRs) for low vs moderate and high groups were 1.9 (95% CI 1.6-2.2) and 3.0 (95% CI 2.6-3.5), respectively. In those most likely to benefit from pharmaceutical intervention (<80 yr, not on statins), MACE rates among those with low, moderate, and high ML-AAC24 were 13.5 (95% CI 11.5-15.8), 26.0 (95% CI 22.1-30.3) and 44.1 (95% CI 37.0-52.0). Corresponding IRRs for low vs moderate 1.9 (95% CI 1.5-2.4) and high ML-AAC24 was 3.3 (95% CI 2.6-4.1]), respectively. In routine osteoporosis screening, individuals with moderate and high ML-AAC24 had substantially greater MACE rates compared to those with low ML-AAC24. Consequently, AAC detection during osteoporosis screening (especially in women) may guide intensification of preventative cardiovascular strategies.
DOI
10.1093/jbmr/zjae208
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Comments
Smith, C., Sim, M., Ilyas, Z., Gilani, S. Z., Suter, D., Reid, S., ... & Leslie, W. D. (2025). Automated abdominal aortic calcification and major adverse cardiovascular events in people undergoing osteoporosis screening: The Manitoba Bone Mineral Density Registry. Journal of Bone and Mineral Research, 40(3), 323-331. https://doi.org/10.1093/jbmr/zjae208