Automated abdominal aortic calcification and trabecular bone score independently predict incident fracture during routine osteoporosis screening.
Authors
Affiliations (12)
Affiliations (12)
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia.
- Medical School, The University of Western Australia, Perth, WA, Australia.
- Centre for AI & ML, School of Science, Edith Cowan University, Perth, Australia.
- Computer Science and Software Engineering, The University of Western Australia, Perth, Australia.
- Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland.
- Department of Computer Science, Concordia University, Montreal, Canada.
- George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, Canada.
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.
- Department of Statistics, University of Manitoba, Winnipeg, Canada.
- Park Nicollet Clinic and HealthPartners Institute, HealthPartners, Minneapolis, USA.
- Division of Health Policy and Management, University of Minnesota, Minneapolis, USA.
- Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.
Abstract
Abdominal aortic calcification (AAC), a marker of subclinical cardiovascular disease, has previously shown to be associated with low bone mineral density (BMD) and fracture. However, it remains unclear whether AAC is associated with trabecular bone score (TBS), a gray-level textural measure, or whether it predicts fracture risk independent of this measure. Here, we examined the cross-sectional association of AAC scored using a validated machine learning algorithm (ML-AAC24) with TBS, and their simultaneous associations with incident fractures in 7,691 individuals (93.4% women) through the Manitoba BMD Registry (mean age 75.3 years). The association between ML-AAC24 and TBS was tested using generalised linear regression. Cox proportional hazards models tested the simultaneous relationships of ML-AAC24 and TBS with incident fractures. At baseline, 41.3% of the study cohort had low (<2), 32.4% had moderate (2 to <6) and 26.3% had high (≥6) ML-AAC24. Compared to low ML-AAC24, high ML-AAC24 was associated with a 0.81% lower TBS in the multivariable-adjusted model. Independent of each other and multiple established fracture risk factors, ML-AAC24 and TBS were each associated with an increased risk of incident fractures. Specifically, high ML-AAC24 (HR 1.41 95%CI 1.15-1.73, compared to low ML-AAC24) and lower TBS (HR 1.13 95%CI 1.05-1.22, per SD decrease) were associated with increased relative hazards for any incident fracture. High ML-AAC24 and lower TBS were also associated with incident major osteoporotic fracture (HR 1.48 95%CI 1.18-1.87 and HR 1.15 95%CI 1.06-1.25, respectively) and hip fracture (HR 1.56 95%CI 1.05-2.31 and HR 1.25 95%CI 1.08-1.44, respectively). In conclusion, high ML-AAC24 is associated with lower TBS in older adults attending routine osteoporosis screening. Both measures were associated with incident fractures. The findings of this study highlight high ML-AAC24, seen in more than 1 in 4 of the study cohort, and lower TBS provide complementary prognostic information for fracture risk.