Accuracy of surrogate methods to estimate skeletal muscle mass in non-dialysis dependent patients with chronic kidney disease and in kidney transplant recipients.

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. 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. 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. 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.

Barreto Silva MI ，Menna Barreto APM ，Pontes KSDS ，Costa MSD ，Rosina KTC ，Souza E ，Bregman R ，Prado CM ，Klein MRST ... -  《-》

Bioelectrical impedance analysis as an alternative to dual-energy x-ray absorptiometry in the assessment of fat mass and appendicular lean mass in patients with obesity.

Obesity is a challenge for bioelectrical impedance analysis (BIA) estimations of skeletal muscle and fat mass (FM), and none of the equations used for appendicular lean mass (ALM) have been developed for people with obesity. By using different equations and proposing a new equation, this study aimed to assess the estimation of FM and ALM using BIA compared with dual-energy x-ray absorptiometry (DXA) as a reference method in a cohort of people with severe obesity. This cross-sectional study compared a multifrequency BIA (TANITA MC-780A) versus DXA for body composition assessment in adult patients with severe obesity (body mass index [BMI] of >35 kg/m2). Comparisons between measured (DXA) and predicted (BIA) data for FM and ALM were performed using the original proprietary equations of the device and the equations proposed by Kyle, Sergi, and Yamada. Bland-Altman plots were drawn to evaluate the agreement between DXA and BIA, calculating bias and limits of agreement (LOA). Reliability was analyzed using intraclass correlation coefficient (ICC). Stepwise multiple regression analysis was used to derive a new equation to predict ALM in patients with obesity and was validated in a subsample of our cohort. In this study, 115 patients (72.4% women) with severe obesity (mean BMI of 46.1 [5.2] kg/m2) were included (mean age 43.5 [8.6] y). FMDXA was 61.4 (10.1) kg, FMBIA was 57.9 (10.3) kg, and ICC was 0.925 (P < 0.001). Bias was -3.4 (4.4) kg (-5.2%), and LOA was -14.0, +7.3 kg. Using the proprietary equations, ALMDXA was 21.8 (4.7) kg and ALMBIA was 29.0 (6.8) kg with an ICC 0.868, bias +7.3 (4.0) kg (+34.1%) and LOA -0.5, +15.1. When applying other equations for ALM, the ICC for Sergi, et al. was 0.880, the ICC for Kyle, et al. was 0.891, and the best ICC estimation for Yamada, et al. was 0.914 (P < 0.001). Bias was +2.8 (2.8), +4.1 (2.9), and +2.7 (2.8) kg, respectively. The best-fitting regression equation to predict ALMDXA in our population derived from a development cohort (n = 77) was: ALM = 13.861 + (0.259 x H2/Z) - (0.085 x age) - (3.983 x sex [0 = men; 1 = women]). When applied to our validation cohort (n = 38), the ICC was 0.864, and the bias was the lowest compared with the rest of the equations +0.3 (+0.5) kg (+2.7%) LOA -5.4, +6.0 kg. BIA using multifrequency BIA in people with obesity is reliable enough for the estimation of FM, with good correlation and low bias to DXA. Regarding the estimation of ALM, BIA showed a good correlation with DXA, although it overestimated ALM, especially when proprietary equations were used. The use of equations developed using the same device improved the prediction, and our new equation showed a low bias for ALM.

Ballesteros-Pomar MD ，González-Arnáiz E ，Pintor-de-la Maza B ，Barajas-Galindo D ，Ariadel-Cobo D ，González-Roza L ，Cano-Rodríguez I ... -  《-》

Anthropometry-based Equations to Estimate Body Composition: A Suitable Alternative in Renal Transplant Recipients and Patients With Nondialysis Dependent Kidney Disease?

Chronic kidney disease (CKD) patients and renal transplant recipients (RTRs) are characterized by aberrant body composition such as muscle wasting and obesity. It is still unknown which is the most accurate method to estimate body composition in CKD. We investigated the validity of the Hume equation and bioelectrical impedance analysis (BIA) as an estimate of body composition against dual-energy X-ray absorptiometry (DXA) in a cohort of nondialysis dependent (NDD)-CKD and RTRs. This was a cross-sectional study with agreement analysis of different assessments of body composition conducted in 61 patients (35 RTRs and 26 NDD-CKD) in a secondary care hospital setting in the UK. Body composition (lean mass [LM], fat mass [FM], and body fat% [BF%]) was assessed using multifrequency BIA and DXA, and estimated using the Hume formula. Method agreement was assessed by intraclass correlation coefficient (ICC), regression, and plotted by Bland and Altman analysis. Both BIA and the Hume formula were able to accurately estimate body composition against DXA. In both groups, the BIA overestimated LM (1.7-2.1 kg, ICC .980-.984) and underestimated FM (1.3-2.1 kg, ICC .967-.972) and BF% (3.1-3.8%, ICC .927-.954). The Hume formula also overestimated LM (3.5-3.6 kg, ICC .950-.960) and underestimated BF% (1.9-2.1%, ICC .808-.859). Hume-derived FM was almost identical to DXA in both groups (-0.3 to 0.1 kg, ICC .947-.960). Our results demonstrate, in RTR and NDD-CKD patients, that the Hume formula, whose estimation of body composition is based only upon height, body mass, age, and sex, may reliably predict the same parameters obtained by DXA. In addition, BIA also provided similar estimates versus DXA. Thus, the Hume formula and BIA could provide simple and inexpensive means to estimate body composition in renal disease.

Wilkinson TJ ，Richler-Potts D ，Nixon DGD ，Neale J ，Smith AC ... -  《-》

Performance of Bioelectrical Impedance and Anthropometric Predictive Equations for Estimation of Muscle Mass in Chronic Kidney Disease Patients.

Background: Patients with chronic kidney disease (CKD) are vulnerable to loss of muscle mass due to several metabolic alterations derived from the uremic syndrome. Reference methods for body composition evaluation are usually unfeasible in clinical settings. Aims: To evaluate the accuracy of predictive equations based on bioelectrical impedance analyses (BIA) and anthropometry parameters for estimating fat free mass (FFM) and appendicular FFM (AFFM), compared to dual energy X-ray absorptiometry (DXA), in CKD patients. Methods: We performed a longitudinal study with patients in non-dialysis-dependent, hemodialysis, peritoneal dialysis and kidney transplant treatment. FFM and AFFM were evaluated by DXA, BIA (Sergi, Kyle, Janssen and MacDonald equations) and anthropometry (Hume, Lee, Tian, and Noori equations). Low muscle mass was diagnosed by DXA analysis. Intra-class correlation coefficient (ICC), Bland-Altman graphic and multiple regression analysis were used to evaluate equation accuracy, linear regression analysis to evaluate bias, and ROC curve analysis and kappa for reproducibility. Results: In total sample and in each CKD group, the predictive equation with the best accuracy was AFFMSergi (men, n = 137: ICC = 0.91, 95% CI = 0.79-0.96, bias = 1.11 kg; women, n = 129: ICC = 0.94, 95% CI = 0.92-0.96, bias = -0.28 kg). AFFMSergi also presented the best performance for low muscle mass diagnosis (men, kappa = 0.68, AUC = 0.83; women, kappa = 0.65, AUC = 0.85). Bias between AFFMSergi and AFFMDXA was mainly affected by total body water and fat mass. None of the predictive equations was able to accurately predict changes in AFFM and FFM, with all ICC lower than 0.5. Conclusion: The predictive equation with the best performance to asses muscle mass in CKD patients was AFFMSergi, including evaluation of low muscle mass diagnosis. However, assessment of changes in body composition was biased, mainly due to variations in fluid status together with adiposity, limiting its applicability for longitudinal evaluations.

Bellafronte NT ，Vega-Piris L ，Cuadrado GB ，Chiarello PG ... -  《Frontiers in Nutrition》

Development and validation of a simple anthropometric equation to predict appendicular skeletal muscle mass.

A limited number of studies have developed simple anthropometric equations that can be implemented for predicting muscle mass in the local community. Several studies have suggested calf circumference as a simple and accurate surrogate maker for muscle mass. We aimed to develop and cross-validate a simple anthropometric equation, which incorporates calf circumference, to predict appendicular skeletal muscle mass (ASM) using dual-energy X-ray absorptiometry (DXA). Furthermore, we conducted a comparative validity assessment of our equation with bioelectrical impedance analysis (BIA) and two previously reported equations using similar variables. ASM measurements were recorded for 1262 participants (837 men, 425 women) aged 40 years or older. Participants were randomly divided into the development or validation group. Stepwise multiple linear regression was applied to develop the DXA-measured ASM prediction equation. Parameters including age, sex, height, weight, waist circumference, and calf circumference were incorporated as predictor variables. Total error was calculated as the square root of the sum of the square of the difference between DXA-measured and predicted ASMs divided by the total number of individuals. The most optimal ASM prediction equation developed was: ASM (kg) = 2.955 × sex (men = 1, women = 0) + 0.255 × weight (kg) - 0.130 × waist circumference (cm) + 0.308 × calf circumference (cm) + 0.081 × height (cm) - 11.897 (adjusted R2 = 0.94, standard error of the estimate = 1.2 kg). Our equation had smaller total error and higher intraclass correlation coefficient (ICC) values than those for BIA and two previously reported equations, for both men and women (men, total error = 1.2 kg, ICC = 0.91; women, total error = 1.1 kg, ICC = 0.80). The correlation between DXA-measured ASM and predicted ASM by the present equation was not significantly different from the correlation between DXA-measured ASM and BIA-measured ASM. The equation developed in this study can predict ASM more accurately as compared to equations where calf circumference is used as the sole variable and previously reported equations; it holds potential as a reliable and an effective substitute for estimating ASM.

Kawakami R ，Miyachi M ，Tanisawa K ，Ito T ，Usui C ，Midorikawa T ，Torii S ，Ishii K ，Suzuki K ，Sakamoto S ，Higuchi M ，Muraoka I ，Oka K ... -  《-》