1 Nutrition & Health Innovation Research Institute, Edith Cowan University, Perth, Australia.
2 Medical School, the University of Western Australia, Perth, Australia.
3 Centre for AI&ML, School of Science, Edith Cowan University, Perth, Australia.
4 Computer Science and Software Engineering, University of Western Australia, Perth, Australia.
5 Department of Computer Science, Concordia University, Montreal, Canada.
6 George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, Canada.
7 Department of Statistics, University of Manitoba, Winnipeg, Canada.
8 Faculty of Health and Medicine, The University of Sydney, Sydney, NSW 2050, Australia.
9 Kolling Institute of Medical Research, Sydney, NSW 2064, Australia.
10 Park Nicollet Clinic and HealthPartners Institute, HealthPartners, Minneapolis, MN 55416, United States.
11 Centre for Kidney Research, Children's Hospital at Westmead School of Public Health, Sydney Medical School, the University of Sydney, Sydney, Australia.
12 Departments of Medicine and Radiology, University of Manitoba, Winnipeg, Canada.

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 Bone Mineral Density Registry. The cohort included 10 250 individuals (mean 75.5 years, 94% women without CVD) with VFA (February 2010 to March 2017) were included. ML-AAC24 scores were categorized (low <2; moderate 2- < 6; high =6). Over follow-up (mean 3.9 years), 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 years, 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.