Automated Abdominal Aortic Calcification Scores and Atherosclerotic Cardiovascular Disease in the UK Biobank Imaging Study.
Authors
Affiliations (21)
Affiliations (21)
- Nutrition and Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Medical School, The University of Western Australia, Perth, Australia. Electronic address: [email protected].
- Applied Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
- Nutrition and Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Medical School, The University of Western Australia, Perth, Australia.
- Nutrition and Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Centre for AI and ML, School of Science, Edith Cowan University, Perth, Australia.
- Nutrition and Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Centre for AI and ML, School of Science, Edith Cowan University, Perth, Australia; Computer Science and Software Engineering, The University of Western Australia, Perth, Australia.
- Nutrition and Health Innovation Research Institute, Edith Cowan University, Perth, Australia; OBEMET Center for Obesity and Metabolic Health, Lima, Peru; Universidad San Ignacio de Loyola, Lima, Peru.
- Faculty of Health and Medicine, The University of Sydney, Sydney, New South Wales, Australia; Kolling Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Cell Biology and Chronic Disease, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia; School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
- Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, King's College London, London, United Kingdom; Department of Endocrinology, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.
- Medical School, The University of Western Australia, Perth, Australia.
- INSERM UMR 1033, University of Lyon, Hospices Civils de Lyon, Lyon, France.
- Medical School, The University of Western Australia, Perth, Australia; Cardiovascular Epidemiology Research Centre, The University of Western Australia, Perth, Australia.
- Nutrition and Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Medical School, The University of Western Australia, Perth, Australia; Department of Renal Medicine and Transplantation, Sir Charles Gairdner Hospital, Perth, Australia.
- 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.
- Nutrition and Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Danish Cancer Institute, Copenhagen, Denmark.
- Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, South Australia, Australia.
- Department of Medicine Beth Israel Deaconess Medical Center and Harvard Medical School, Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA.
- Departments of Medicine and Radiology, University of Manitoba, Winnipeg, Canada.
- Mater Research Institute, The University of Queensland, Translational Research Institute, Woolloongabba, Queensland, Australia.
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, United Kingdom; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom.
- Park Nicollet Clinic and HealthPartners Institute, HealthPartners, Minneapolis, Minnesota, USA; Division of Health Policy and Management, University of Minnesota, Minneapolis, Minnesota, USA.
Abstract
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. The aim of the study was to externally validate a machine learning-derived AAC 24-point algorithm (ML-AAC24) with incident ASCVD. 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. 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. Assessing ML-AAC24 on lateral spine images offers a new and promising screening method to identify people with higher risk of incident ASVD events.