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Abstract

Skeletal muscle mass (SMM) is an important body composition metric in clinical and research settings. However, criterion methods such as magnetic resonance imaging and computed tomography are largely inaccessible, so surrogate methods such as dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance are used to estimate SMM. The agreement of these surrogate estimates may be impacted by physiological characteristics of the specific populations being evaluated. PURPOSE: To compare SMM estimates derived from DXA and bioimpedance methods using differing prediction equations within a sample of muscular, resistance-trained adults. METHODS: Forty participants (23 M, 17 F; [mean ± SD] age: 28.1 ± 7.9 years; body fat percentage: 15.7 ± 5.2%, fat-free mass index: 21.9 ± 2.8 kg/m2; 9.2 ± 5.1 years of resistance training experience) completed a single research visit in which body composition was assessed using DXA (GE iDXA), bioimpedance spectroscopy (BIS; ImpediMed SOZO), multi-frequency bioelectrical impedance analysis (MFBIA; Seca mBCA 515), single-frequency bioelectrical impedance analysis (SFBIA; RJL Quantum V), and consumer-grade bioimpedance (CBIA; InBody H2ON). Assessments were performed after an overnight period (≥8 hours) of fasting from food and fluid and ≥36 hours of abstention from exercise. DXA appendicular lean soft tissue was used to predict total SMM using two published equations based on magnetic resonance imaging. Bioimpedance devices provided SMM estimates via published or proprietary equations. SMM estimates for participants with complete data (n=38; 22 M, 16 F) were compared using linear mixed-effects models and follow-up pairwise comparisons with Bonferroni adjustment. RESULTS: A statistically significant effect of assessment method was observed in the entire sample, males only, and females only (pCONCLUSION:  SMM estimates differed significantly across DXA and bioimpedance assessment tools in resistance-trained individuals. These findings emphasize the impact of model assumptions and technical differences on SMM estimation. Additionally, it highlights the need for population-specific assessment when estimating body composition in athletic populations with unique body properties.

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