Department of Oncology, Suzhou BenQ Medical Center, Suzhou, Jiangsu, People's Republic of China.
School of Nursing, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, People's Republic of China.
Clin Interv Aging. 2024 Feb 16;19:265-276. doi: 10.2147/CIA.S440967. eCollection 2024.
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.
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 -test, intraclass correlation coefficient (ICC) and paired-samples -tests were used to assess the validity of the equations.
All parameters were significantly correlated with ASM ( = 0.1950.795, < 0.001) except for the age in the validation group ( = 0.746). The most optimal anthropometric equation was: [adjusted = 0.911, standard error of the estimate (SEE) = 1.311, < 0.001]. Comparatively speaking, this equation showed high correlation coefficient ( = 0.951, < 0.001) and ICC (ICC = 0.950, < 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 -2.702.60 kg. Furthermore, inclusion of physical function did not significantly improve the adjusted and SEE.
The anthropometric equation offers a practical alternative simple and dependable method for estimating ASM in community-dwelling older adults.
本研究旨在为社区老年人建立通过人体测量参数或与身体功能参数相结合来估计肌肉量的方程，提供一种简单的肌肉量评估方法。
在这项横断面描述性研究中，共有 1537 名老年人从社区中招募并接受了身高、体重、上臂和小腿围、握力和步行速度的测量。使用生物电阻抗分析（BIA）测量身体成分，包括四肢骨骼肌量（ASM）。参与者被随机分为开发或验证组。在开发组中应用逐步多元线性回归来建立方程。之后，使用 Pearson 相关系数、Bland-Altman 图、配对 t 检验、组内相关系数（ICC）和配对样本 t 检验来评估方程的有效性。
除验证组中的年龄（ = 0.746）外，所有参数均与 ASM 显著相关（ = 0.1950.795， < 0.001）。最佳的人体测量方程是：[调整 = 0.911，估计标准误差（SEE）= 1.311， < 0.001]。相对而言，该方程显示出较高的相关系数（ = 0.951， < 0.001）和 ICC（ICC = 0.950， < 0.001）。BIA 测量的 ASM 与估计的 ASM 之间无显著差异。Bland-Altman 图显示，估计的 ASM 与 BIA 测量的 ASM 之间的平均差异为 0kg，AS 的一致性界限为-2.702.60kg。此外，纳入身体功能并不能显著改善调整和 SEE。
人体测量方程为社区居住的老年人提供了一种简单实用的替代方法来估计 ASM。
