Predictive equation for assessing appendicular lean soft tissue mass using bioelectric impedance analysis in older adults: Effect of body fat distribution

Background: Low muscle mass is associated with sarcopenia and increased mortality. Muscle mass, especially that of the limbs, is commonly estimated by dual-energy X-ray absorptiometry (DXA) or bioimpedance analysis (BIA). However, BIA-based predictive equations for estimating lean appendicular soft tissue mass (ALST) do not take into account body fat distribution, an important factor influencing DXA and BIA measurements. Objectives: To develop and cross-validate a BIA-based equation for estimating ALST with DXA as criterion, and to compare our new formula to three previously published models. Methods: One-hundred eighty-four older adults (140 women and 44 men) (age 71.5 +/- 7.3 years, body mass index 27.9 +/- 5.3 kg/m2) were recruited. Participants were randomly split into validation (n = 118) and crossvalidation groups (n = 66). Bioelectrical resistance was obtained with a phase-sensitive 50 kHz BIA device. Results: A BIA-based model was developed for appendicular lean soft tissue mass [ALST (kg) = 5.982 + (0.188 x S2 / resistance) + (0.014 x waist circumference) + (0.046 x Wt) + (3.881 x sex) - (0.053 x age), where sex is 0 if female or 1 if male, Wt is weight (kg), and S is stature (cm) (R2 = 0.86, SEE = 1.35 kg)]. Cross validation revealed r2 of 0.91 and no mean bias. Two of three previously published models showed a trend to significantly overestimate ALST in our sample (p < 0.01). Conclusions: The new equation can be considered valid, with no observed bias and trend, thus affording practical means to quantify ALST mass in older adults.

Automated summary

In this study, researchers developed and cross-validated a BIA-based equation for estimating ALST, taking into account the impact of body fat distribution. The new formula showed better accuracy and no bias compared to previously published models, providing a practical means to quantify ALST in older adults.