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.

Plain language summary: Studies investigating associations between machine-learning derived abdominal aortic calcification (AAC) with advanced imaging derived muscle composition and mass, as well as functional measures are limited. For the first time, we demonstrate that AAC identified automatically from bone density images was associated with poorer thigh muscle composition, lower muscle mass, weaker muscle strength, as well as increased sarcopenia and falls risk in middle-aged and older community-dwelling adults. Collectively, our findings demonstrate the need to consider vascular health during musculoskeletal health assessments. This is particularly important given AAC can be opportunistically identified on images from widely available bone density machines.
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