Simó-Servat Andreu, Guevara Ernesto, Perea Verónica, Alonso Núria, Quirós Carmen, Puig-Jové Carlos, Barahona María-José
Department of Endocrinology and Nutrition, Hospital Universitari Mútua Terrassa, 08221 Terrassa, Spain.
Department of Medicine, Autonomous University of Barcelona, 08193 Barcelona, Spain.
Biology (Basel). 2023 Jun 19;12(6):884. doi: 10.3390/biology12060884.
Sarcopenia and diabetes contribute to the development of frailty. Therefore, accessible methods, such as muscle ultrasounds (MUSs), to screen for sarcopenia should be implemented in clinical practice.
We conducted a cross-sectional pilot study including 47 patients with diabetes (mean age: 77.72 ± 5.08 years, mean weight: 75.8 kg ± 15.89 kg, and body mass index: 31.19 ± 6.65 kg/m) categorized as frail by the FRAIL Scale or Clinical Frailty Scale and confirmed by Fried's Frailty Phenotype or Rockwood's 36-item Frailty Index. We used the SARC-F questionnaire to identify sarcopenia. The Short Physical Performance Battery (SPPB) and the Timed Up and Go (TUG) tests were used to assess physical performance and the risk of falls, respectively. In addition, other variables were measured: fat-free mass (FFM) and Sarcopenia Risk Index (SRI) with the bioimpedance analysis (BIA); thigh muscle thickness (TMT) of the quadriceps with MUS; and hand-grip strength with dynamometry.
We observed correlations between the SARC-F and FFM (R = -0.4; < 0.002) and hand-grip strength (R = -0.5; < 0.0002), as well as between the TMT and FFM of the right leg (R = 0.4; < 0.02) and the SRI (R = 0.6; < 0.0001). We could predict sarcopenia using a logistic regression model with a ROC curve (AUC = 0.78) including FFM, handgrip strength, and TMT. The optimal cut-off point for maximum efficiency was 1.58 cm for TMT (sensitivity = 71.4% and specificity = 51.5%). However, we did not observe differences in the TMT among groups of greater/less frailty based on the SARC-F, SPPB, and TUG ( > 0.05).
MUSs, which correlated with the BIA (R = 0.4; < 0.02), complemented the diagnosis, identifying regional sarcopenia of the quadriceps in frail patients with diabetes and improving the ROC curve to AUC = 0.78. In addition, a TMT cut-off point for the diagnosis of sarcopenia of 1.58 cm was obtained. Larger studies to validate the MUS technique as a screening strategy are warranted.
肌肉减少症和糖尿病会促使身体虚弱的发展。因此,应在临床实践中采用如肌肉超声(MUS)等可及的方法来筛查肌肉减少症。
我们进行了一项横断面试点研究,纳入了47例糖尿病患者(平均年龄:77.72±5.08岁,平均体重:75.8 kg±15.89 kg,体重指数:31.19±6.65 kg/m²),这些患者根据FRAIL量表或临床衰弱量表被归类为身体虚弱,并经Fried衰弱表型或Rockwood 36项衰弱指数确认。我们使用SARC-F问卷来识别肌肉减少症。简短身体机能测试电池(SPPB)和计时起立行走(TUG)测试分别用于评估身体机能和跌倒风险。此外,还测量了其他变量:通过生物电阻抗分析(BIA)测量去脂体重(FFM)和肌肉减少症风险指数(SRI);用MUS测量股四头肌的大腿肌肉厚度(TMT);用握力计测量握力。
我们观察到SARC-F与FFM(R = -0.4;P < 0.002)和握力(R = -0.5;P < 0.0002)之间存在相关性,以及右腿的TMT与FFM(R = 0.4;P < 0.02)和SRI(R = 0.6;P < 0.0001)之间存在相关性。我们可以使用包含FFM、握力和TMT的逻辑回归模型及受试者工作特征曲线(AUC = 0.78)来预测肌肉减少症。TMT的最大效率最佳截断点为1.58 cm(敏感性 = 71.4%,特异性 = 51.5%)。然而,基于SARC-F、SPPB和TUG,我们未观察到在身体虚弱程度较高/较低组之间TMT存在差异(P > 0.05)。
与BIA相关(R = 0.4;P < 0.02)的MUS补充了诊断,识别出糖尿病虚弱患者股四头肌的局部肌肉减少症,并将受试者工作特征曲线提高到AUC = 0.78。此外,获得了诊断肌肉减少症的TMT截断点为1.58 cm。有必要进行更大规模的研究来验证MUS技术作为一种筛查策略。