4.7 Article

Development and validation of a simple anthropometric equation to predict appendicular skeletal muscle mass

Journal

CLINICAL NUTRITION
Volume 40, Issue 11, Pages 5523-5530

Publisher

CHURCHILL LIVINGSTONE
DOI: 10.1016/j.clnu.2021.09.032

Keywords

Anthropometry; Body composition; Body size; Dual-energy X-ray absorptiometry scan; Prediction equation; Sarcopenia

Funding

  1. Japan Society for the Promotion of Science, Japan [JP18K17982, JP19H04008, JP26242070]
  2. MEXT-Supported Program for the Strategic Research Foundation at Private Universities from the Ministry of Education, Culture, Sports, Science and Technology, Japan [S1511017]

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The study developed a more accurate anthropometric equation to predict appendicular skeletal muscle mass (ASM) compared to equations using calf circumference as the sole variable and previously reported equations. The equation showed smaller total error and higher intraclass correlation coefficient values in both men and women.
Background & aims: A limited number of studies have developed simple anthropometric equations that can be implemented for predicting muscle mass in the local community. Several studies have suggested calf circumference as a simple and accurate surrogate maker for muscle mass. We aimed to develop and cross-validate a simple anthropometric equation, which incorporates calf circumference, to predict appendicular skeletal muscle mass (ASM) using dual-energy X-ray absorptiometry (DXA). Furthermore, we conducted a comparative validity assessment of our equation with bioelectrical impedance analysis (BIA) and two previously reported equations using similar variables. Methods: ASM measurements were recorded for 1262 participants (837 men, 425 women) aged 40 years or older. Participants were randomly divided into the development or validation group. Stepwise multiple linear regression was applied to develop the DXA-measured ASM prediction equation. Parameters including age, sex, height, weight, waist circumference, and calf circumference were incorporated as predictor variables. Total error was calculated as the square root of the sum of the square of the difference between DXA-measured and predicted ASMs divided by the total number of individuals. Results: The most optimal ASM prediction equation developed was: ASM (kg) = 2.955 x sex (men = 1, women = 0) + 0.255 x weight (kg) 0.130 x waist circumference (cm) + 0.308 x calf circumference (cm) + 0.081 x height (cm) 11.897 (adjusted R-2 = 0.94, standard error of the estimate = 1.2 kg). Our equation had smaller total error and higher intraclass correlation coefficient (ICC) values than those for BIA and two previously reported equations, for both men and women (men, total error = 1.2 kg, ICC = 0.91; women, total error = 1.1 kg, ICC = 0.80). The correlation between DXA-measured ASM and predicted ASM by the present equation was not significantly different from the correlation between DXA-measured ASM and BIA-measured ASM. Conclusions: The equation developed in this study can predict ASM more accurately as compared to equations where calf circumference is used as the sole variable and previously reported equations; it holds potential as a reliable and an effective substitute for estimating ASM. (C) 2021 The Author(s). Published by Elsevier Ltd.

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