Total and regional appendicular skeletal muscle mass prediction from dual-energy X-ray absorptiometry body composition models

• Published in: Scientific reports Vol. 13; no. 1; pp. 2590 - 10
• Main Authors: McCarthy, Cassidy, Tinsley, Grant M., Bosy-Westphal, Anja, Müller, Manfred J., Shepherd, John, Gallagher, Dympna, Heymsfield, Steven B.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.02.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/308/53; 692/308/53/2421; Absorptiometry, Photon - methods; Adult; Arm; Body Composition; Cachexia; Dual energy X-ray absorptiometry; Humanities and Social Sciences; Humans; Magnetic Resonance Imaging; multidisciplinary; Muscle, Skeletal - diagnostic imaging; Muscle, Skeletal - physiology; Musculoskeletal system; Prediction models; Regression analysis; Sarcopenia; Sarcopenia - diagnostic imaging; Science; Science (multidisciplinary); Skeletal muscle; Whole Body Imaging
• DOI: 10.1038/s41598-023-29827-y
• ISSN: 2045-2322

Sarcopenia, sarcopenic obesity, frailty, and cachexia have in common skeletal muscle (SM) as a main component of their pathophysiology. The reference method for SM mass measurement is whole-body magnetic resonance imaging (MRI), although dual-energy X-ray absorptiometry (DXA) appendicular lean mass (ALM) serves as an affordable and practical SM surrogate. Empirical equations, developed on relatively small and diverse samples, are now used to predict total body SM from ALM and other covariates; prediction models for extremity SM mass are lacking. The aim of the current study was to develop and validate total body, arm, and leg SM mass prediction equations based on a large sample (N = 475) of adults evaluated with whole-body MRI and DXA for SM and ALM, respectively. Initial models were fit using ordinary least squares stepwise selection procedures; covariates beyond extremity lean mass made only small contributions to the final models that were developed using Deming regression. All three developed final models (total, arm, and leg) had high R 2 s (0.88–0.93; all p < 0.001) and small root-mean square errors (1.74, 0.41, and 0.95 kg) with no bias in the validation sample (N = 95). The new total body SM prediction model (SM = 1.12 × ALM – 0.63) showed good performance, with some bias, against previously reported DXA-ALM prediction models. These new total body and extremity SM prediction models, developed and validated in a large sample, afford an important and practical opportunity to evaluate SM mass in research and clinical settings.