1. Development and validation of a simple anthropometric equation to predict appendicular skeletal muscle mass
- Author
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Taishi Midorikawa, Kaori Ishii, Motohiko Miyachi, Suguru Torii, Chiyoko Usui, Kumpei Tanisawa, Tomoko Ito, Mitsuru Higuchi, Isao Muraoka, Shizuo Sakamoto, Katsuhiko Suzuki, Ryoko Kawakami, and Koichiro Oka
- Subjects
Adult ,Male ,Sarcopenia ,Waist ,Intraclass correlation ,Critical Care and Intensive Care Medicine ,Correlation ,Absorptiometry, Photon ,Predictive Value of Tests ,Clinical Decision Rules ,Linear regression ,Statistics ,Electric Impedance ,Humans ,Medicine ,Muscle, Skeletal ,Leg ,Nutrition and Dietetics ,Anthropometry ,business.industry ,Reproducibility of Results ,respiratory system ,musculoskeletal system ,Circumference ,Standard error ,Body Composition ,Linear Models ,Female ,business ,Bioelectrical impedance analysis - Abstract
Summary 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 × sex (men = 1, women = 0) + 0.255 × weight (kg) − 0.130 × waist circumference (cm) + 0.308 × calf circumference (cm) + 0.081 × height (cm) − 11.897 (adjusted R2 = 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.
- Published
- 2021
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