Predicting skeletal muscle mass from dual-energy X-ray absorptiometry in Japanese prepubertal children View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-04-05

AUTHORS

T Midorikawa, M Ohta, Y Hikihara, S Torii, S Sakamoto

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. More... »

PAGES

1218-1222

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URI

http://scigraph.springernature.com/pub.10.1038/ejcn.2017.35

DOI

http://dx.doi.org/10.1038/ejcn.2017.35

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084514107

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28378848


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