Development and Validation of Estimation Equations for Appendicular Skeletal Muscle Mass in Chinese Community-Dwelling Older Adults

Abstract

Yun Sun,1,* Tongtong Yin,2,* Mengli Li,2,3 Fangfang Wang,2 Jiaying Qi,2 Hui Zhang,2,4 Li Wang,2 Jiehua Zhao,5 Yu Zhang5 1Department of Oncology, Suzhou BenQ Medical Center, Suzhou, Jiangsu, People’s Republic of China; 2School of Nursing, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 3School of Nursing, Hong Kong Polytechnic University, Hongkong, People’s Republic of China; 4School of Nursing, Vocational Health College, Suzhou, Jiangsu, People’s Republic of China; 5Department of Nursing, Suzhou BenQ Medical Center, Suzhou, Jiangsu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yu Zhang; Jiehua Zhao, Email [email protected]; [email protected]: This study aimed to establish equations for estimating muscle mass through anthropometric parameters or together with physical function parameters in the community-dwelling older adults, providing a simple way of muscle mass assessment.Methods: In this cross-sectional descriptive study, a total of 1537 older adults were recruited from the community and accepted the measurements of height, weight, upper arm and calf circumferences, grip strength, and walking speed. Body composition including appendicular skeletal muscle mass (ASM) was measured using bioelectrical impedance analysis (BIA). Participants were randomly divided into the development or validation group. Stepwise multiple linear regression was applied to develop equations in the development group. Thereafter, Pearson correlation coefficients, Bland-Altman plots, paired t-test, intraclass correlation coefficient (ICC) and paired-samples t-tests were used to assess the validity of the equations.Results: All parameters were significantly correlated with ASM (r = 0.195~0.795, P < 0.001) except for the age in the validation group (P = 0.746). The most optimal anthropometric equation was: [adjusted R2 = 0.911, standard error of the estimate (SEE) = 1.311, P < 0.001]. Comparatively speaking, this equation showed high correlation coefficient (r = 0.951, P < 0.001) and ICC (ICC = 0.950, P < 0.001). No significant differences were found between BIA-measured ASM and the estimated ASM. The Bland-Altman plot showed that the mean difference between the estimated ASM and BIA-measured ASM was 0 kg and the limits of agreement of ASM was − 2.70~2.60 kg. Furthermore, inclusion of physical function did not significantly improve the adjusted R2 and SEE.Conclusion: The anthropometric equation offers a practical alternative simple and dependable method for estimating ASM in community-dwelling older adults.Keywords: appendicular muscle mass, anthropometry, physical function, estimation equation, older adults

Keywords

• appendicular muscle mass
• anthropometry
• physical function
• estimation equation
• older adults