Is Ultrasound an Accurate Method for Predicting Fat-Free Mass in Resistance-Trained Men?

Increasing muscle mass is the main objective of individuals engaged in resistance training (RT) programs. Traditional imaging techniques such as computed tomography, magnetic resonance imaging and dual-energy X-ray absorptiometry (DXA) are commonly used to quantify fat-free mass (FFM), but their high cost, limited accessibility and technical complexity often restrict their routine use. Ultrasound (US), by contrast, offers a non-invasive, portable and cost-effective alternative and therefore can play an important role in monitoring musculoskeletal adaptations because it provides objective measures of muscle mass and muscle quality. Since the literature does not provide an adequate US-based model to estimate whole-body (WB) and appendicular FFM in resistance-trained adult men, we aimed to develop and validate a US-based equation to predict WB and thigh FFM in resistance-trained men.

Seventy-nine men (31.4 ± 7.0 y, 76.3 ± 10.2 kg, 174.3 ± 6.0 cm) with prior RT experience underwent DXA assessments to calculate WB and thigh FFM. Vastus lateralis (VL) and rectus femoris (RF) muscle thickness (MT) were assessed at rest by B-mode US imaging. Stepwise regression analysis was performed to develop US-based equations, and the developed models were cross-validated using the PRESS approach.

VL and RF MT directly correlated with body mass and WB and thigh FFM. Regression analysis showed that RF-MT alone explains 18.9% and 19% of the WB and thigh-FFM, respectively, while VL-MT alone explains 26.5% and 20.1% of the WB and thigh-FFM, respectively. Four models were developed (two for WB and two for thigh-FFM, each based on either VL or RF values), with the best performance being observed for the VL approach: WB-FFM (kg)R² = 0.882 and standard error of estimate (SEE) = 2.99 kg, thigh-FFM (kg)R² = 0.849 and SEE = 0.667 kg.

The present study introduces a statistically robust and ecologically applicable mathematical model based on US measurements, specifically on the VL-MT, for assessing FFM in resistance-trained men. This model may also be valuable for detecting muscle asymmetries and monitoring adaptations to training and rehabilitation programs.