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Estimation of Total Body Skeletal Muscle Mass in Chinese Adults: Prediction Model by Dual-Energy X-Ray Absorptiometry

Zhao, Xinyu; Wang, ZiMian; Zhang, Junyi; Hua, Jianming; He, Wei; Zhu, Shankuan

Background: There are few reports on total body skeletal muscle mass (SM) in Chinese. The objective of this study is to establish a prediction model of SM for Chinese adults.

Methodology: Appendicular lean soft tissue (ALST) was measured by dual energy X-ray absorptiometry (DXA) and SM by magnetic resonance image (MRI) in 66 Chinese adults (52 men and 14 women). Images of MRI were segmented into compartments including intermuscular adipose tissue (IMAT) and IMAT-free SM. Regression was used to fit the prediction model SM = c + k × ALST. Age and gender were adjusted in the fitted model. The piece-wise linear function was performed to further explore the effect of age on SM. ‘Leave-One-Out Cross Validation’ was utilized to evaluate the prediction performance. The significance of observed differences between predicted and actual SM was tested by t test and the level of agreement was assessed by the method of Bland and Altman.

Results: Men had greater ALST and IMAT-free SM than women. ALST was the primary predictor and highly correlated with IMAT-free SM (R2 = 0.94, SEE = 1.11 kg, P<0.001). Age was an additional predictor (SM prediction model with age adjusted R2 = 0.95, SEE = 1.05 kg, P<0.001). There was a piece-wise linear relationship between age and IMAT-free SM: IMAT-free SM = 1.21×ALST−0.98, (Age <45 years) and IMAT-free SM = 1.21×ALST−0.98−0.04× (Age−45), (Age ≥45years). The prediction performance of this age-adjusted model was good due to ‘Leave-One-Out Cross Validation’. No significant difference between measured and predicted IMAT-free SM was detected.

Conclusion: Previous SM prediction model developed in multi-ethnic groups underestimated SM by 2.3% and 3.4% for Chinese men and women. A new prediction model by DXA has been established to predict SM in Chinese adults.


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Academic Units
College of Physicians and Surgeons
Public Library of Science
Published Here
February 27, 2017