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Intermuscular adipose tissue-free skeletal muscle mass: estimation by dual-energy X-ray absorptiometry in adults

¹Obesity Research Center and ²Gastrointestinal Division, St. Luke's-Roosevelt Hospital, and ³Departments of Clinical Psychiatry and ⁴Medicine, College of Physicians and Surgeons, Columbia University, New York, New York 10025

Submitted 10 March 2004 ; accepted in final form 10 April 2004

Skeletal muscle (SM) is a large and physiologically important compartment. Adipose tissue is found interspersed between and within SM groups and is referred to as intermuscular adipose tissue (IMAT). The study objective was to develop prediction models linking appendicular lean soft tissue (ALST) estimates by dual-energy X-ray absorptiometry (DXA) with whole body IMAT-free SM quantified by magnetic resonance imaging. ALST and total-body IMAT-free SM were evaluated in 270 healthy adults [body mass index (BMI) of <35 kg/m²]. The SM prediction models were then validated by the leave-one-out method and by application in a new group of subjects who varied in SM mass [anorexia nervosa (AN), n = 23; recreational athletes, n = 16; patients with acromegaly, n = 7]. ALST alone was highly correlated with whole body IMAT-free SM [model 1: R² = 0.96, standard error (SE) = 1.46 kg, P < 0.001]; age (model 2: R² = 0.97, SE = 1.38 kg, P < 0.001) and sex and race (model 3: R² = 0.97, SE = 1.06 kg, both P < 0.001) added significantly to the prediction models. All three models validated in the athletes and patients with acromegaly but significantly (P < 0.01–0.001) over-predicted SM in the AN group as a whole. However, model 1 was validated in AN patients with BMIs in the model-development group range (n = 11; BMI of >16 kg/m²) but not in those with a BMI of <16 kg/m² (n = 12). The DXA-based models are accurate for predicting IMAT-free SM in selected populations and thus provide a new opportunity for quantifying SM in physiological and epidemiological investigations.

body composition; magnetic resonance imaging; anorexia nervosa; acromegaly

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