Department of Human Health and Nutritional Sciences, University of Guelph, 50 Stone Road West, Guelph, ON, Canada.
Gait Posture. 2012 Feb;35(2):180-5. doi: 10.1016/j.gaitpost.2011.09.003. Epub 2011 Oct 6.
The purpose of this study was to assess the relationship of the fat free mass index (FFMI), an indicator of sarcopenia in older adults, to anthropometric, gait, balance, and strength measures. We hypothesized that strength, balance, and mobility measures will correlate, and could be used to predict FFMI in older adults. Thirty-three older adults (81.5±7.9 years) participated. Fat free mass (FFM) was measured using Air-Displacement Plethysmography (ADP). Anthropometric measures, maximum handgrip (MG) and quadriceps strength were quantified. Clinical tests included the Berg Balance Scale (BBS), Dynamic Gait Index (DGI), and the Timed-up and Go (TUG) test. Finally, variability measures in gait and balance were calculated. Means, standard deviations (SD), correlations and multiple linear regression statistical analyses were then performed using functional predictor variables for FFMI. In total, 54.5% males and 36.3% females in our population were classified sarcopenic. FFMI correlated only to waist circumference (Total population (Pop), R(2)=0.649 p<0.01; Sarcopenics (Sarc), R(2)=0.636, p<0.05) and maximum grip strength (Pop, R(2)=0.633, p<0.01; Sarc, R(2)=0.771, p<0.01), nullifying our hypothesis. Multiple linear regression analyses revealed waist circumference, maximum handgrip strength, greater variability of time spent in double support, and anterior-posterior balance variability predicted 70.7% of the variance within the population. Results demonstrate a successful predictor model for FFMI based on a combination of strength, circumference and gait/balance variance measures. The ability to predict FFMI based on these variables will facilitate the diagnosis of sarcopenia in older adults.
本研究旨在评估瘦体重指数(FFMI)与老年人人体测量学、步态、平衡和力量指标的关系。我们假设力量、平衡和移动性指标将相互关联,并可用于预测老年人的 FFMI。共有 33 名老年人(81.5±7.9 岁)参与。使用空气置换体积描记法(ADP)测量瘦体重(FFM)。量化了人体测量指标、最大握力(MG)和股四头肌力量。临床测试包括 Berg 平衡量表(BBS)、动态步态指数(DGI)和计时起立行走(TUG)测试。最后,计算了步态和平衡的变异性指标。然后使用 FFMI 的功能预测变量进行平均值、标准差(SD)、相关性和多元线性回归统计分析。在我们的人群中,总共有 54.5%的男性和 36.3%的女性被归类为肌肉减少症。FFMI 仅与腰围(总人口(Pop),R²=0.649,p<0.01;肌肉减少症(Sarc),R²=0.636,p<0.05)和最大握力(Pop,R²=0.633,p<0.01;Sarc,R²=0.771,p<0.01)相关,否定了我们的假设。多元线性回归分析显示,腰围、最大握力、双支撑时间变异性增加以及前后平衡变异性可预测人群中 70.7%的方差。结果表明,基于力量、周长和步态/平衡变异性测量的组合,FFMI 具有成功的预测模型。基于这些变量预测 FFMI 的能力将有助于诊断老年人的肌肉减少症。