Automated abdominal aortic calcification, muscle health and incident falls: the UK Biobank Imaging Study.
Authors
Affiliations (21)
Affiliations (21)
- 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.
- Applied Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Centre for Artificial Intelligence & Machine Learning, School of Science, Edith Cowan University, Perth, WA, Australia.
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA, Australia.
- Mater Research Institute, The University of Queensland, Translational Research Institute, Woolloongabba, QLD, Australia.
- Bone, Muscle & GeroScience Group, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
- Dr Joseph Kaufmann Chair in Geriatric Medicine, Department of Medicine, McGill University, Montreal, QC, Canada.
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, VIC Australia.
- Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, King's College London, London, UK.
- Department of Endocrinology, Guy's and St Thomas' NHS Foundation Trust, London, UK.
- Department of Internal Medicine, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Korea.
- McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada.
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
- Labarge Centre for Mobility in Aging, McMaster University, Hamilton, Ontario, Canada.
- Marcus Institute for Ageing Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
- Department of Internal Medicine, Rady Faculty of Health Sciences, 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.
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.
Abstract
Emerging evidence suggests that vascular disease is linked with poorer muscle strength and higher falls risk. We evaluated the association between abdominal aortic calcification (AAC), scored using a well-established and validated 24-point machine learning algorithm (ML-AAC24), with magnetic resonance imaging-derived fat-free muscle volume (FFMV, n=33,640) and muscle fat infiltration (MFI, n=33,640), appendicular lean mass (ALM, n=36,526), handgrip strength (HGS, n=49,049), sarcopenia (n=35,834) and incident falls (n=48,482) in community-dwelling adults (mean age 64.6 ± 7.8 years, 50.9% women). ML-AAC24 was assessed on dual-energy X-ray absorptiometry (DXA)-derived lateral spine images and classified into established categories based on severity; low (<2), moderate (2-5) and high (≥6). Age and sex specific cut-points for low FFMV and sex-specific high MFI were based on previous work. Low ALM, weak HGS and sarcopenia were based on the revised European sarcopenia guidelines. The associations between ML-AAC24 extent, odds of having poorer muscle health measures and incident falls were tested in multivariable-adjusted logistic and Cox proportional hazards regressions, respectively. Individuals with moderate and high, compared to low ML-AAC24, had greater odds for low FFMV (1.58, 95%CI: 1.28-1.95 and 2.52, 95%CI: 1.88-3.38, respectively), high MFI (1.09, 95%CI: 1.01-1.18 and 1.45, 95%CI: 1.29-1.64, respectively), and low ALM (1.14, 95%CI: 1.04-1.24 and 1.28, 95%CI: 1.11-1.47, respectively). They also had higher odds for weak HGS (1.18, 95%CI: 1.07-1.29 and 1.24, 95%CI:1.09-1.42, respectively) and sarcopenia (1.40, 95%CI:1.12-1.76 and 1.69, 95%CI:1.24-2.29, respectively). Compared to low ML-AAC24, high ML-AAC24 was associated with greater hazards for an incident fall-related hospitalisation (1.31, 95%CI: 1.02-1.68). Greater ML-AAC24 extent, which can be opportunistically identified during routine bone density testing, was associated with poorer muscle composition and, function, sarcopenia and incident falls in community-dwelling adults. Such findings may explain previous reports between AAC and higher fall and fracture risk, supporting a nexus between vascular and musculoskeletal health.