Skeletal muscle estimation using magnetic-resonance-imaging-based equations for dual-energy X-ray absorptiometry and bioelectrical impedance analysis

• Published in: European journal of clinical nutrition Vol. 77; no. 12; pp. 1151 - 1159
• Main Authors: Tinsley, Grant M., LaValle, Christian, Rodriguez, Christian, Siedler, Madelin R., Heymsfield, Steven B.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.12.2023; Nature Publishing Group
• Subjects: 59/57; 692/700/1421; 692/700/2814; Absorptiometry, Photon - methods; Adolescent; Adult; Arm; Bias; Bioelectricity; Body Composition; Body measurements; Clinical Nutrition; Correlation coefficient; Correlation coefficients; Dual energy X-ray absorptiometry; Electric Impedance; Epidemiology; Equivalence; Humans; Impedance; Internal Medicine; Magnetic Resonance Imaging; Mathematical analysis; Medicine; Medicine & Public Health; Metabolic Diseases; Muscle, Skeletal - diagnostic imaging; Muscle, Skeletal - physiology; Muscles; Musculoskeletal system; Public Health; Regression; Skeletal muscle; Statistical analysis; Young Adult
• ISSN: 0954-3007; 1476-5640; 1476-5640
• DOI: 10.1038/s41430-023-01331-6

Background/Objectives Skeletal muscle mass (SMM) estimation is important but challenging in clinical settings. Criterion methods, such as magnetic resonance imaging (MRI), are often inaccessible. However, surrogate methods, such as dual-energy X-ray absorptiometry (DXA) and multi-frequency bioelectrical impedance analysis (MFBIA), can use MRI-based equations to estimate SMM, although the agreement between these methods is unclear. Subjects/Methods Total and segmental SMM were estimated with DXA and MFBIA using MRI-based equations in 313 healthy adults (120 M, 193 F; age 30.2 ± 13.0 y; BMI 24.6 ± 4.0 kg/m 2 ). DXA total SMM was estimated using the Kim and McCarthy equations, and segmental SMM was estimated using the McCarthy equations. Relationships between DXA and MFBIA SMM were examined using Deming regression, Lin’s concordance correlation coefficient (CCC), equivalence testing, Bland-Altman analysis, and related tests. Results Strong linear relationships were observed for total (R 2 0.95, CCC 0.96–0.97), leg (R 2 0.90, CCC 0.85) and arm (R 2 0.93, CCC 0.93) SMM in the entire sample. Kim equation SMM demonstrated statistical equivalence with MFBIA for total SMM, but the Deming regression slope differed from 1 and proportional bias was present. McCarthy equation total SMM exhibited a regression slope that did not differ from 1, and no proportional bias was present in the entire sample. However, equivalence with MFBIA was not observed. Systematically higher leg and arm SMM values were observed with DXA as compared to MFBIA. Conclusions While DXA and MFBIA total SMM generally exhibited strong agreement, higher appendicular SMM by DXA highlights technical differences between methods.