Author Identifier (ORCID)
Syed Zulqarnain Gilani’s ORCID record 
Cassandra Smith’s ORCID record 
Abstract
Clinical cardiovascular disease (CVD) is often present in frail individuals. However, it remains unclear whether subclinical CVD, e.g., abdominal aortic calcification (AAC), is associated with frailty. This study investigated the cross-sectional relationship between AAC scored using a validated machine learning model (ML-AAC24) and physical frailty. 49,081 participants from the UK Biobank Imaging Study without atherosclerotic CVD (ASCVD) diagnosis were included. ML-AAC24 extent was categorised as low, moderate and high, based on established severity categories. Physical frailty was based on a modified Fried’s frailty phenotype comprising weak hand grip strength, slow walking speed, weight loss, exhaustion, and physical inactivity. Individuals with three or more deficits were considered frail, while one or two deficits was considered pre-frail. Multivariable-adjusted multinominal logistic regression models were used to test the associations between ML-AAC24 extent and frailty status. One in five individuals had moderate or high ML-AAC24. Compared to individuals with low ML-AAC24, those with moderate and high ML-AAC24 had greater odds of being pre-frail (ORs 1.06 95%CI 1.00–1.12 moderate; 1.14 95%CI 1.04–1.26 high) or frail (ORs 1.27 95%CI 1.12–1.44 moderate; 1.58 95%CI 1.31–1.91 high), adjusted for multiple covariates. When stratified by sex, similar results for frailty were recorded. In a population, those with moderate and high ML-AAC24 were more likely to present as pre-frail and frail. AAC identified from lateral spine images obtained during routine bone density testing, could serve as a useful marker for the early detection of frailty, highlighting the importance of multimodality care.
Keywords
artificial intelligence, cardiovascular disease, dual-energy x-ray absorptiometry, vascular calcification
Document Type
Journal Article
Date of Publication
1-1-2026
ISSN
25092715
Publication Title
Geroscience
Publisher
Springer
School
Nutrition and Health Innovation Research Institute / School of Medical and Health Sciences / Centre for Artificial Intelligence and Machine Learning (CAIML) / School of Science
RAS ID
95360
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Gebre, A. K., Mundt, M., Dent, E., Webster, J., Saleem, A., Gilani, S. Z., Zhang, Y., Smith, C., Raina, P., Kemp, J. P., Leslie, W. D., Schousboe, J. T., Harvey, N. C., Lewis, J. R., & Sim, M. (2026). Machine learning derived abdominal aortic calcification is associated with physical frailty in community-dwelling adults: The UK Biobank Imaging Study. GeroScience. Advance online publication. https://doi.org/10.1007/s11357-026-02375-9