AUTHOR=Bellafronte Natália Tomborelli , Vega-Piris Lorena , Cuadrado Guillermina Barril , Chiarello Paula Garcia 

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

JOURNAL=Frontiers in Nutrition

VOLUME=8

YEAR=2021

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2021.683393

DOI=10.3389/fnut.2021.683393

ISSN=2296-861X

ABSTRACT=<p><bold>Background:</bold> 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.</p><p><bold>Aims:</bold> 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.</p><p><bold>Methods:</bold> 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.</p><p><bold>Results:</bold> In total sample and in each CKD group, the predictive equation with the best accuracy was AFFM<sub>Sergi</sub> (men, <italic>n</italic> = 137: ICC = 0.91, 95% CI = 0.79–0.96, bias = 1.11 kg; women, <italic>n</italic> = 129: ICC = 0.94, 95% CI = 0.92–0.96, bias = −0.28 kg). AFFM<sub>Sergi</sub> 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 AFFM<sub>Sergi</sub> and AFFM<sub>DXA</sub> 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.</p><p><bold>Conclusion:</bold> The predictive equation with the best performance to asses muscle mass in CKD patients was AFFM<sub>Sergi</sub>, 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.</p>