TY - JOUR
T1 - Development and validation of a simple anthropometric equation to predict appendicular skeletal muscle mass
AU - Kawakami, Ryoko
AU - Miyachi, Motohiko
AU - Tanisawa, Kumpei
AU - Ito, Tomoko
AU - Usui, Chiyoko
AU - Midorikawa, Taishi
AU - Torii, Suguru
AU - Ishii, Kaori
AU - Suzuki, Katsuhiko
AU - Sakamoto, Shizuo
AU - Higuchi, Mitsuru
AU - Muraoka, Isao
AU - Oka, Koichiro
N1 - Funding Information:
This work was supported by a Grant-in-Aid for Early-Career Scientists from the Japan Society for the Promotion of Science, Japan [grant number JP18K17982 ] and for Scientific Research from the Japan Society for the Promotion of Science, Japan [grant numbers JP19H04008 , JP26242070 ]; MEXT-Supported Program for the Strategic Research Foundation at Private Universities from the Ministry of Education, Culture, Sports, Science and Technology, Japan [grant number S1511017 ]. The funders had no role in the study design; data collection, analysis and interpretation of data; writing of the report; or decision to submit the article for publication.
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - Anthropometry
KW - Body composition
KW - Body size
KW - Dual-energy X-ray absorptiometry scan
KW - Prediction equation
KW - Sarcopenia
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U2 - 10.1016/j.clnu.2021.09.032
DO - 10.1016/j.clnu.2021.09.032
M3 - Article
C2 - 34656948
AN - SCOPUS:85117078174
SN - 0261-5614
VL - 40
SP - 5523
EP - 5530
JO - Clinical Nutrition
JF - Clinical Nutrition
IS - 11
ER -