BACKGROUND: Ambulation, balance, and lower extremity bone mass and strength are all partially dependent on lower limb skeletal muscle mass. At present, both research and clinical methods of evaluating lower limb skeletal muscle mass as a component of nutrition assessment are limited. One potential simple and inexpensive method is lower extremity bioimpedance analysis (BIA). The present study had two objectives: to examine the determinants of lower limb resistance, with the underlying hypothesis that fluid-containing muscle is the main electrical conductor of the lower limbs; and to establish if a correlation of equivalent magnitude and similar covariates is observed when height squared (H2) is used instead of lower limb length squared (L2) in multiple regression models relating resistance to independent variables. METHODS: Lower limb resistance was measured using a contact-electrode BIA system, and lower limb fat and skeletal muscle were estimated by dual-energy x-ray absorptiometry in healthy adults. A physical BIA model was developed in the form of a regression equation with path-length (as L2 and H2)-adjusted resistance as dependent variables and lower limb skeletal muscle, fat, age, and gender as potential independent variables. RESULTS: There were 94 subjects, 34 men and 60 women, with a mean (-/+SD) age of 41.5+/-17.8 years. Strong associations were observed between L2/resistance and lower limb skeletal muscle, although for both men and women, age entered into the model as a significant covariate (total R2, men = .79 and women = .72; both p < .001). Similar models were observed with H2/resistance as dependent variable. Additional analyses showed a significantly lower resistance in lower limb skeletal muscle and height-matched old vs young subjects. CONCLUSIONS: Strong associations exist between measured lower limb resistance and lower limb muscle mass, adjusting for electrical path length either by L2 or H2. These observations suggest the potential of predicting skeletal muscle using BIA-measured lower limb resistance adjusted for stature. Age is also an independent variable in lower limb resistance-skeletal muscle associations, suggesting the need to establish underlying mechanisms of age-related resistance effects and to consider subject age when developing BIA prediction models.
BACKGROUND: Ambulation, balance, and lower extremity bone mass and strength are all partially dependent on lower limb skeletal muscle mass. At present, both research and clinical methods of evaluating lower limb skeletal muscle mass as a component of nutrition assessment are limited. One potential simple and inexpensive method is lower extremity bioimpedance analysis (BIA). The present study had two objectives: to examine the determinants of lower limb resistance, with the underlying hypothesis that fluid-containing muscle is the main electrical conductor of the lower limbs; and to establish if a correlation of equivalent magnitude and similar covariates is observed when height squared (H2) is used instead of lower limb length squared (L2) in multiple regression models relating resistance to independent variables. METHODS: Lower limb resistance was measured using a contact-electrode BIA system, and lower limb fat and skeletal muscle were estimated by dual-energy x-ray absorptiometry in healthy adults. A physical BIA model was developed in the form of a regression equation with path-length (as L2 and H2)-adjusted resistance as dependent variables and lower limb skeletal muscle, fat, age, and gender as potential independent variables. RESULTS: There were 94 subjects, 34 men and 60 women, with a mean (-/+SD) age of 41.5+/-17.8 years. Strong associations were observed between L2/resistance and lower limb skeletal muscle, although for both men and women, age entered into the model as a significant covariate (total R2, men = .79 and women = .72; both p < .001). Similar models were observed with H2/resistance as dependent variable. Additional analyses showed a significantly lower resistance in lower limb skeletal muscle and height-matched old vs young subjects. CONCLUSIONS: Strong associations exist between measured lower limb resistance and lower limb muscle mass, adjusting for electrical path length either by L2 or H2. These observations suggest the potential of predicting skeletal muscle using BIA-measured lower limb resistance adjusted for stature. Age is also an independent variable in lower limb resistance-skeletal muscle associations, suggesting the need to establish underlying mechanisms of age-related resistance effects and to consider subject age when developing BIA prediction models.