Grip Strength and Demographic Variables Estimate Appendicular Muscle Mass Better Than Bioelectrical Impedance in Taiwanese Older Persons

生物电阻抗分析 医学 人体测量学 握力 瘦体质量 线性回归 双重能量 脂肪团 肌萎缩 身体素质 物理疗法 体质指数 统计 核医学 数学 内科学 体重 骨矿物 骨质疏松症
作者
Ming‐Yen Hsiao,Ke‐Vin Chang,Wei‐Ting Wu,Kuo‐Chin Huang,Der‐Sheng Han
出处
期刊:Journal of the American Medical Directors Association [Elsevier BV]
卷期号:22 (4): 760-765 被引量:15
标识
DOI:10.1016/j.jamda.2020.08.003
摘要

Objectives This study aimed to develop an equation model combining physical fitness and anthropometric parameters and compare its results with those of bioelectrical impedance analysis (BIA)-measured lean mass (LM) using dual-energy X-ray absorptiometry (DXA)-measured appendicular muscle mass (AMM) as reference. Design Observational analysis. Setting and Participants Healthy community-dwelling older subjects. Methods A total of 1020 participants were randomly allocated to the development group (development group, n = 510) or the cross-validation group (validation group, n = 510). Body composition was measured using both DXA and BIA, and physical fitness parameters, including grip strength, timed stepping test, sit-to-stand test, flexibility, and walking speed were also assessed. A prediction equation model of AMM by stepwise linear regression analysis that included or excluded 1 independent variable at each step, based on the P value of significance (P < .05), was developed. Results Using weight, sex, height, and handgrip strength as independent variables, the equation AMM = −9.833 + 0.397 × weight (kg) + 4.433 × sex + 0.121 × height (cm) + 0.061 × handgrip strength (kg) best predicts DXA-measured AMM (adjusted R2 = 0.914, SEE = 2.062, P < .001). The predicted AMM was more highly correlated with DXA-measured AMM than the commonly used BIA-measured LM ( R 2 = 0.9158 and 0.8427, respectively, both P < .001). Using DXA-measured AMM as reference, the Bland-Altman plot showed mean differences of −0.03 kg and −0.12 kg, with limits of agreement of −3.98 to 3.92 kg and −5.97 to 5.73 kg for the predicted AMM and BIA-measured AMM, respectively. Conclusions and Implications The proposed equation offers a practical alternative method for estimating AMM that is less facility-dependent and more easy to use and affordable than instrumental studies.
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