Skeletal muscle mass estimation in Brazilian Jiu-Jitsu athletes: validation of predictive equations - Ojeda-Aravena, Alex; Báez-San Marín, Eduardo; Dopico-Calvo, Xurxo; Cresp-Barría, Mauricio; Olivares-Arancibia, Jorge; Azócar-Gallardo, Jairo (Frontiers in Nutrition, 2025)
Accurate estimation of skeletal muscle mass (SMM) is important for body composition assessment in Brazilian Jiu-Jitsu (BJJ) athletes owing to body mass classification and force production implications. This study compared the validity, reliability, and agreement of three predictive equations—Kim, McCarthy, and Sagayama—for estimating total SMM (expressed in kilograms) in male BJJ athletes. Twenty-two male BJJ athletes (mean age: 33.1 ± 7.5 years; body mass: 78.4 ± 9.6 kg; height: 171.8 ± 6.4 cm) underwent DXA-derived body composition analysis. SMM was estimated using the Kim, McCarthy, and Sagayama equations. Statistical analyses included repeated-measures ANOVA, stepwise linear regression, Pearson's correlation, intraclass correlation coefficient (ICC), coefficient of variation (CV%), and Bland-Altman plots. The mean SMM estimated by the Kim equation was 28.95 ± 4.92 kg (95% CI: 26.89–31.00 kg), by the McCarthy equation, 27.39 ± 4.96 kg (95% CI: 25.32–29.47 kg), and by the Sagayama equation, 27.72 ± 3.71 kg (95% CI: 26.16–29.27 kg). The Kim equation yielded significantly higher SMM values than McCarthy (mean difference = 1.55 kg, p < 0.0001), while Sagayama and McCarthy did not differ significantly. Stepwise regression identified the Kim equation as a strong predictor of Sagayama SMM values (R = 0.851; R2 = 0.724; RMSE = 2.0 kg; F1, 20 = 52.369; p < 0.001), although with proportional underestimation (slope = 0.642). Reliability was acceptable for all equations (ICC > 0.79), and the Sagayama equation demonstrated the lowest CV% (13.4%, 95% CI: 9.44%−17.36%). Bland–Altman analysis revealed systematic biases, particularly for the Kim equation. All three equations provided accurate validity and reliability for estimating absolute SMM (kg) in BJJ athletes. However, the McCarthy and Sagayama equations showed less bias and greater agreement by DXA, supporting their use for accurate quantification of SMM in this population. Their validation with magnetic resonance imaging is needed.