Akın S, Mucuk S, Öztürk A, Mazıcıoğlu M, Göçer Ş, Arguvanlı S, Şafak E D
Department of Internal Medicine, Division of Geriatrics, Erciyes School of Medicine, Erciyes University, Melikgazi, Kayseri, Turkey.
Department of Nursing, Faculty of Health Sciences, Erciyes University, Melikgazi, Kayseri, Turkey.
Eur J Clin Nutr. 2015 Oct;69(10):1087-90. doi: 10.1038/ejcn.2015.42. Epub 2015 Mar 18.
BACKGROUND/OBJECTIVES: The aim of this study was to determine the prevalence of muscle strength-based sarcopenia and to determine possible predictors.
SUBJECTS/METHODS: This is a cross-sectional population-based study in the community-dwelling Turkish elderly. Anthropometric measurements, namely body height, weight, triceps skin fold (TSF), mid upper arm circumference (MUAC), waist circumference (WC) and calf circumference (CC), were noted. The midarm muscle circumference (MAMC) was calculated by using MUAC and TSF measurement. Sarcopenia was assessed, adjusted for body mass index (BMI) and gender, according to muscle strength. Physical performance was determined by 4 m walking speed (WS; m/s). The receiver operating curve analysis was performed to determine cut-offs of CC, MAMC and 4 m WS.
A total of 879 elderly subjects, 50.1% of whom were female, were recruited. The mean handgrip strength (HGS) and s.d. was 24.2 (8.8) kg [17.9 (4.8) female, 30.6 (7.1) male]. The muscle function-dependent sarcopenia was 63.4% (female 73.5%, male 53.2%). The muscle mass-dependent sarcopenia for CC (<31 cm) and MAMC(<21.1 cm in males, <19.9 cm in females) was 6.7% and 7.3%, respectively. The prevalence of low 4 m WS (≤ 0.8 m/s) was 81.8% (91.3% in females and 72.3% in males, respectively). We compared MAMC, CC and 4 m WS and found that AUC for 4 m WS was the best predictor of sarcopenia.
An adequate muscle mass may not mean a reliable muscle function. Muscle function may describe sarcopenia better compared with muscle mass. The CC, MAMC and 4 m WS cut-offs may be used to assess sarcopenia in certain age groups.
背景/目的：本研究旨在确定基于肌肉力量的肌肉减少症的患病率，并确定可能的预测因素。
对象/方法：这是一项基于社区的土耳其老年人群横断面研究。记录了人体测量数据，即身高、体重、肱三头肌皮褶厚度（TSF）、上臂中部周长（MUAC）、腰围（WC）和小腿周长（CC）。通过MUAC和TSF测量计算上臂中部肌肉周长（MAMC）。根据肌肉力量评估肌肉减少症，并根据体重指数（BMI）和性别进行调整。通过4米步行速度（WS；米/秒）确定身体性能。进行受试者工作特征曲线分析以确定CC、MAMC和4米WS的临界值。
共招募了879名老年受试者，其中50.1%为女性。平均握力（HGS）及标准差为24.2（8.8）千克[女性为17.9（4.8），男性为30.6（7.1）]。依赖肌肉功能的肌肉减少症为63.4%（女性为73.5%，男性为53.2%）。CC（<31厘米）和MAMC（男性<21.1厘米，女性<19.9厘米）依赖肌肉质量的肌肉减少症分别为6.7%和7.3%。低4米WS（≤0.8米/秒）的患病率为81.8%（女性为91.3%，男性为72.3%）。我们比较了MAMC、CC和4米WS，发现4米WS的AUC是肌肉减少症的最佳预测指标。
足够的肌肉质量并不意味着可靠的肌肉功能。与肌肉质量相比，肌肉功能可能更能描述肌肉减少症。CC、MAMC和4米WS的临界值可用于评估特定年龄组的肌肉减少症。
