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  • Title: Accuracy of surrogate methods to estimate skeletal muscle mass in non-dialysis dependent patients with chronic kidney disease and in kidney transplant recipients.
    Author: Barreto Silva MI, Menna Barreto APM, Pontes KSDS, Costa MSD, Rosina KTC, Souza E, Bregman R, Prado CM, Klein MRST.
    Journal: Clin Nutr; 2021 Jan; 40(1):303-312. PubMed ID: 32536581.
    Abstract:
    BACKGROUND & AIMS: Bioelectrical impedance analysis (BIA) and anthropometric predictive equations have been proposed to estimate whole-body (SMM) and appendicular skeletal muscle mass (ASM) as surrogate for dual energy X-ray absorptiometry (DXA) in distinct population groups. However, their accuracy in estimating body composition in non-dialysis dependent patients with chronic kidney disease (NDD-CKD) and kidney transplant recipients (KTR) is unknown. The aim of this study was to investigate the accuracy and reproducibility of BIA and anthropometric predictive equations in estimating SMM and ASM compared to DXA, in NDD-CKD patients and KTR. METHODS: A cross-sectional study including adult NDD-CKD patients and KTR, with body mass index (BMI) ≥18.5 kg/m2. ASM and estimated SMM were evaluated by DXA, BIA (Janssen, Kyle and MacDonald equations) and anthropometry (Lee and Baumgartner equations). Low muscle mass (LowMM) was defined according to cutoffs proposed by guidelines for ASM, ASM/height2 and ASM/BMI. The best performing equation as surrogate for DXA, considering both groups of studied patients, was defined based in the highest Lin's concordance correlation coefficient (CCC) value, the lowest Bland-Altman bias (<1.5 kg) combined with the narrowest upper and lower limits of agreement (LoA), and the highest Cohen's kappa values for the low muscle mass diagnosis. RESULTS: Studied groups comprised NDD-CKD patients (n = 321: males = 55.1%; 65.4 ± 13.1 years; eGFR = 28.8 ± 12.7 ml/min) and KTR (n = 200: males = 57.7%; 47.5 ± 11.3 years; eGFR = 54.7 ± 20.7 ml/min). In both groups, the predictive equations presenting the best accuracy compared to DXA were SMM-BIA-Janssen (NDD-CKD patients: CCC = 0.88, 95%CI = 0.83-0.92; bias = 0.0 kg; KTR: CCC = 0.89, 95%CI = 0.86-0.92, bias = -1.2 kg) and ASM-BIA-Kyle (NDD-CKD patients: CCC = 0.87, 95%CI = 0.82-0.90, bias = 0.7 kg; KTR: CCC = 0.89, 95%CI = 0.86-0.92, bias = -0.8 kg). In NDD-CKD patients and KTR, LowMM frequency was similar according to ASM-BIA-Kyle versus ASM-DXA. The reproducibility and inter-agreement to diagnose LowMM using ASM/height2 and ASM/BMI estimated by BIA-Kyle equation versus DXA was moderate (kappa: 0.41-0.60), in both groups. Whereas female patients showed higher inter-agreement (AUC>80%) when ASM/BMI index was used, male patients presented higher AUC (70-74%; slightly <80%) for ASM/height2 index. CONCLUSIONS: The predictive equations with best performance to assess muscle mass in both NDD-CKD patients and KTR was SMM-BIA by Janssen and ASM-BIA by Kyle. The reproducibility to diagnose low muscle mass, comparing BIA with DXA, was high using ASM/BMI in females and ASM/height2 in males in both groups.
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