Bed-side measures for diagnosis of low muscle mass, sarcopenia, obesity, and sarcopenic obesity in patients with chronic kidney disease under non-dialysis-dependent, dialysis dependent and kidney transplant therapy.
Bed-side measures for diagnosis of low muscle mass, sarcopenia, obesity, and sarcopenic obesity in patients with chronic kidney disease under non-dialysis-dependent, dialysis dependent and kidney transplant therapy.
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在非依赖性依赖性，透析依赖性和肾脏移植疗法下，患有慢性肾脏疾病的患者诊断低肌肉，肌肉减少症，肥胖和肌肉减少肥胖症的床侧测量。
DOI：
10.1371/journal.pone.0242671
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Cuadrado GB
中科院分区：
文献类型：
--
作者：
Bellafronte NT;Sizoto GR;Vega-Piris L;Chiarello PG;Cuadrado GB
在慢性肾脏疾病(CKD)中，肌肉衰竭和肌肉源性肥胖与较高的死亡率相关。我们评估了与慢性肾脏病患者低肌肉质量、骨质疏松症、肥胖和骨质疏松性肥胖相关的床边测量/指数，并提出了每个参数的临界值。根据欧洲萨科普减少症工作组对老年人肉质营养不良的诊断，修订了应用双能X射线吸收法(DXA)和握力(HGS)的共识，根据国际临床密度学协会的说法，肥胖。人体测量参数包括小腿(CC)、腰围(WC)和腰围/身高(WC/H)，生物电阻抗数据包括附件脱脂质量(AFFM)和脂肪质量指数(FMI)。ROC分析和曲线下面积(AUC)用于性能分析。AFFM和CC对低肌块的诊断效果最好-AFFM AUC女性为0.96，男性为0.94，CC AUC女性为0.89，男性为0.85。FMI和WC/H是诊断肥胖的最佳参数--FMI AUC女性为0.99，男性为0.96；WC/H AUC女性为0.94，男性为0.95。女性的敏感度和特异度分别为AFFM≤15.87(90%；96%)，CC≤35.5(76%；94%)，FMI>；12.58(100%；93%)，WC/H>；0.66(91%；84%)；男性AFFM≤21.43(98%；84%)，CC≤37(88%；69%)，FMI>；8.82(93%；88%)，WC/H>；0.60(95%；80%)。AFFM+HGS诊断骨质疏松症的敏感性和特异性分别为85%和99%，男性分别为100%和99%，CC+HGS分别为85%和99%，男性分别为100%和100%，FMI+AFFM诊断骨质疏松症的敏感性和特异性分别为75%和97%，75%和95%。测试的床边措施/指标表现出优异的性能。
Muscle depletion and sarcopenic obesity are related to a higher morbimortality risk in chronic kidney disease (CKD). We evaluated bed-side measures/indexes associated with low muscle mass, sarcopenia, obesity, and sarcopenic obesity in CKD and proposed cutoffs for each parameter. Sarcopenia was diagnosed according to the European Working Group on Sarcopenia in Older People revised consensus applying dual energy X-ray absorptiometry (DXA) and hand grip strength (HGS), and obesity according to the International Society for Clinical Densitometry. Anthropometric parameters including calf (CC) and waist (WC) circumferences and WC/height (WC/H); bioelectrical impedance data including appendicular fat free mass (AFFM) and fat mass index (FMI) were assessed. ROC analysis and area under the curve (AUC) were applied for performance analyses. AFFM and CC presented the best performances for low muscle mass diagnosis–AFFM AUC for women was 0.96 and for men, 0.94, and CC AUC for women was 0.89 and for men, 0.85. FMI and WC/H were the best parameters for obesity diagnosis–FMI AUC for women was 0.99 and for men, 0.96, and WC/H AUC for women was 0.94 and for men, 0.95. The cutoffs (sensibility and specificity, respectively) for women were AFFM≤15.87 (90%; 96%), CC≤35.5 (76%; 94%), FMI>12.58 (100%; 93%), and WC/H>0.66 (91%; 84%); and for men, AFFM≤21.43 (98%; 84%), CC≤37 (88%; 69%), FMI>8.82 (93%; 88%), and WC/H>0.60 (95%; 80%). Sensibility and specificity for sarcopenia diagnosis were for AFFM+HGS in women 85% and 99% and in men, 100% and 99%; for CC+HGS in women 85% and 99% and in men, 100% and 100%; and for sarcopenic obesity were for FMI+AFFM in women 75% and 97% and in men, 75% and 95%. The tested bed-side measures/indexes presented excellent performance.
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作者：
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