Explaining Segmental Lean Soft Tissue Discrepancies Between Bioelectrical Impedance Analysis And Dual-Energy X-Ray Absorptiometry

Abstract

Interest in evaluating the composition of specific anatomical regions has become commonplace in a variety of settings. Appendicular lean soft tissue (ALST) estimates are considered in the diagnosis of sarcopenia. While dual-energy x-ray absorptiometry (DXA) is viewed as a reference method for regional assessments, its availability is limited. Thus, explaining discrepancies in regional body composition estimates between DXA and the more accessible bioelectrical impedance analysis (BIA) is of utmost importance. PURPOSE: To assess the anthropometric and physiological predictors of variations between BIA and DXA segmental lean soft tissue (LST) estimates. METHODS: During a single visit, 179 participants (103 females, 76 males; Mean ± SD: 33.6 ± 15.3 years; 73.4 ± 16.2 kg; 171.2 ± 9.2 cm; 28.2 ± 8.9% DXA body fat %) underwent body composition assessments via DXA and 8-point single-frequency BIA. Potential predictors of discrepancies between DXA and BIA LST estimates were obtained from these methods and additional laboratory techniques. Specifically, air displacement plethysmography, 3-dimensional optical scanning, and bioimpedance spectroscopy were used to estimate body volume, anthropometrics, and hydration variables, respectively. Significant predictors (p ≤ 0.05) of the mean difference between DXA and BIA estimates of trunk LST (TLST) and ALST were established using ordinary least squares regression. Standardized model coefficients, p-values for coefficients, and R2 values were generated. RESULTS: For both TLST and ALST, extracellular fluid percentage, LST hydration, height, total LST mass, the male sex, and racial identification as Black significantly predicted discrepancies between DXA and BIA. Additional predictors for TLST discrepancies were DXA total fat mass (FM) to LST ratio and DXA TLST, while additional predictors of ALST discrepancies included DXA ALST, DXA FM to LST ratio of the legs, DXA appendicular FM, and DXA-derived volume of the arms and legs. Regression models including these significant predictor variables produced R2 values of 0.92 and 0.95 for TLST and ALST, respectively. CONCLUSIONS: Hydration variables, the quantity of LST in the region of interest, and height were the most influential predictor variables for discrepancies between DXA and BIA segmental LST estimates.