Magnetic resonance imaging (MRI) was used to evaluate and compare with anthropometry a fundamental bioelectrical impedance analysis (BIA) method for predicting muscle and adipose tissue composition in the lower limb. Healthy volunteers (eight men and eight women), aged 41 to 62 years, with mean (S.D.) body mass indices of 28.6 (5.4) kg/m2 and 25.1 (5.4) kg/m2 respectively, were subjected to MRI leg scans, from which 20-cm sections of thigh and 10-cm sections of lower leg (calf) were analysed for muscle and adipose tissue content, using specifically developed software. Muscle and adipose tissue were also predicted from anthropometric measurements of circumferences and skinfold thicknesses, and by use of fundamental BIA equations involving section impedance at 50 kHz and tissue-specific resistivities. Anthropometric assessments of circumferences, cross-sectional areas and volumes for total constituent tissues matched closely MRI estimates. Muscle volume was substantially overestimated (bias: thigh, -40%; calf, -18%) and adipose tissue underestimated (bias: thigh, 43%; calf, 8%) by anthropometry, in contrast to generally better predictions by the fundamental BIA approach for muscle (bias: thigh, -12%; calf, 5%) and adipose tissue (bias: thigh, 17%; calf, -28%). However, both methods demonstrated considerable individual variability (95% limits of agreement 20-77%). In general, there was similar reproducibility for anthropometric and fundamental BIA methods in the thigh (inter-observer residual coefficient of variation for muscle 3.5% versus 3.8%), but the latter was better in the calf (inter-observer residual coefficient of variation for muscle 8.2% versus 4.5%). This study suggests that the fundamental BIA method has advantages over anthropometry for measuring lower limb tissue composition in healthy individuals.

This study has shown the value of DXA models for assessment of muscle and AT in leg sections, but suggests the need to re-evaluate some of the assumptions upon which they are based.

This study aimed to assess the value of different DXA and BIA models for predicting muscle volume in mid‐thigh segments obtained by MRI. Three DXA models were used: in model A, muscle was taken to be equivalent to fat‐free soft tissue; in model B the thigh segment was divided into its constituent tissues using fixed assumptions about tissue composition; in model C the assumptions were similar to model B, but with variable distribution of fat and fat‐free soft tissue, depending on body mass index. The two BIA models (both parallel tissue resistance models) involved impedance measurements at 50 kHz, and assumptions about either the specific resistivities of all the constituent tissues (model A), or resistivities of only adipose tissue and muscle (model B). Anthropometric estimates (thigh circumference and skinfold thickness) assumed that both limb and muscle circumference were circular. Compared to MRI estimates of muscle mass, those obtained by DXA model A (fat‐free soft tissue) were not as good as those obtained using models B and C, although the standard deviations of the differences were similar with all three models. The BIA models were superior to the anthropometric estimates of muscle volume (relative to MRI) with respect to bias, but the standard deviations of the differences were large for both. The intraobserver repeatabilities for muscle volume were < 0.5% for MRI, <1% for DXA, 1.8% for BIA, and 1.7% for anthropometry (interobserver value for BIA was 3.8% and for anthropometry 3.5%). The study suggests that DXA modeling provides a promising approach for assessing muscle mass in thigh segments, and suggests the potential value of parallel BIA models for groups of individuals but not for individual subjects, possibly because muscle resistivity is influenced not only by its composition but also by the direction of current flow in muscle.

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