T Midorikawa1, M Ohta2, Y Hikihara3, S Torii4, S Sakamoto4. 1. College of Health and Welfare, J.F. Oberlin University, Machida, Tokyo, Japan. 2. School of International Liberal Studies, Chukyo University, Toyota-shi, Aichi, Japan. 3. Faculty of Creative Engineering, Chiba Institute of Technology, Narashino, Chiba, Japan. 4. Faculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Abstract
BACKGROUND/ OBJECTIVE: We aimed to develop regression-based prediction equations for estimating total and regional skeletal muscle mass (SMM) from measurements of lean soft tissue mass (LSTM) using dual-energy X-ray absorptiometry (DXA) and investigate the validity of these equations. SUBJECTS/ METHODS: In total, 144 healthy Japanese prepubertal children aged 6-12 years were divided into 2 groups: the model development group (62 boys and 38 girls) and the validation group (26 boys and 18 girls). Contiguous MRI images with a 1-cm slice thickness were obtained from the first cervical vertebra to the ankle joints as reference data. The SMM was calculated from the summation of the digitized cross-sectional areas. Total and regional LSTM was measured using DXA. RESULTS: Strong significant correlations were observed between the site-matched SMM (total, arms, trunk and legs) measured by MRI and the LSTM obtained by DXA in the model development group for both boys and girls (R2adj=0.86-0.97, P<0.01, standard error of the estimate (SEE)=0.08-0.44 kg). When these SMM prediction equations were applied to the validation group, the measured total (boys 9.47±2.21 kg; girls 8.18±2.62 kg) and regional SMM were very similar to the predicted values for both boys (total SMM 9.40±2.39 kg) and girls (total SMM 8.17±2.57 kg). The results of the Bland-Altman analysis for the validation group did not indicate any bias for either boys or girls with the exception of the arm region for the girls. CONCLUSIONS: These results suggest that the DXA-derived prediction equations are precise and accurate for the estimation of total and regional SMM in Japanese prepubertal boys and girls.
BACKGROUND/ OBJECTIVE: We aimed to develop regression-based prediction equations for estimating total and regional skeletal muscle mass (SMM) from measurements of lean soft tissue mass (LSTM) using dual-energy X-ray absorptiometry (DXA) and investigate the validity of these equations. SUBJECTS/ METHODS: In total, 144 healthy Japanese prepubertal children aged 6-12 years were divided into 2 groups: the model development group (62 boys and 38 girls) and the validation group (26 boys and 18 girls). Contiguous MRI images with a 1-cm slice thickness were obtained from the first cervical vertebra to the ankle joints as reference data. The SMM was calculated from the summation of the digitized cross-sectional areas. Total and regional LSTM was measured using DXA. RESULTS: Strong significant correlations were observed between the site-matched SMM (total, arms, trunk and legs) measured by MRI and the LSTM obtained by DXA in the model development group for both boys and girls (R2adj=0.86-0.97, P<0.01, standard error of the estimate (SEE)=0.08-0.44 kg). When these SMM prediction equations were applied to the validation group, the measured total (boys 9.47±2.21 kg; girls 8.18±2.62 kg) and regional SMM were very similar to the predicted values for both boys (total SMM 9.40±2.39 kg) and girls (total SMM 8.17±2.57 kg). The results of the Bland-Altman analysis for the validation group did not indicate any bias for either boys or girls with the exception of the arm region for the girls. CONCLUSIONS: These results suggest that the DXA-derived prediction equations are precise and accurate for the estimation of total and regional SMM in Japanese prepubertal boys and girls.
Authors: Amy Hsu; Stanley Heshka; Isaac Janumala; Mi-Yeon Song; Mary Horlick; Norman Krasnow; Dympna Gallagher Journal: Am J Clin Nutr Date: 2003-06 Impact factor: 7.045
Authors: Chuan Zhang; Daniel G Whitney; Harshvardhan Singh; Jill M Slade; Ye Shen; Freeman Miller; Christopher M Modlesky Journal: J Clin Densitom Date: 2018-12-15 Impact factor: 2.617