#258 Does muscle quality affect the accuracy of eGFR?

Abstract

Abstract Background and Aims Appropriate assessment of glomerular filtration rate (GFR) is the key to manage chronic kidney disease (CKD). The clearance of exogenous tracers, such as inulin and iothalamate, is the best way to measure GFR. However, because this approach is time-consuming and expensive, it is only applied in a specific condition. Several equations to estimate GFR, including Cockcroft-Gault, MDRD, and CKD-EPI, can be applied with easily obtained information about patient's age, sex, and serum creatinine as an endogenous marker, and are used in clinical practice. Because serum creatinine is considerably influenced by muscle mass, these equations still have limitations. Therefore, estimated GFR (eGFR) should be carefully interpreted in patients with sarcopenia; renal function in patients with sarcopenia are prone to be overestimated. Recently, the importance of muscle quality as well as muscle mass has been emphasized in assessing sarcopenia. Muscle quality can be assessed by measuring phase angle (PhA) using bioelectrical impedance analysis (BIA) and is associated with poor prognosis in patients with heart failure. Although muscle quality potentially has an impact on creatinine generation, the association between muscle quality and GFR is unknown. The aim of this study was to evaluate the influence of muscle quality assessed by PhA on the accuracy of eGFR. Method In this single-center and cross-sectional study, 112 patients with CKD were enrolled between April 2021 and October 2022. Among these, four patients diagnosed as acute kidney injury were excluded from the analysis. Renal functions were evaluated in two approaches by calculating eGFR using an equation for Japanese and more precisely by CCr × 0.715 mL/min using 24-h urine. PhA was measured using MC-780A-N (TANITA), where the smaller value indicates worsened muscle quality. The renal function overestimation index (ROI) was defined as eGFR / CCr × 0.715 and the subjects were classified as i) underestimated group (group U); ROI ≦ 1, ii) overestimated group (group O); ROI > 1. The correlations of ROI to age, body mass index (BMI), skeletal muscle mass (SMM), eGFR and PhA were analyzed using Spearman's rank correlation coefficient. To determine the influencing factor for ROI, a multiple linear regression analysis including age, gender, BMI, SMM, and PhA as explanatory variables were performed. Furthermore, the cut-off value of PhA for overestimation of renal function was determined. Results The patient's characteristics were 56% male, 69.5 (26-89) years old, and eGFR 38.1 (3.8-114.3) mL/min/1.73 m2. PhA was significantly lower in the group O compared to the group U (Fig. 1). ROI correlated with age, BMI, SMM and PhA (age; r = 0.502 and p = 0.007, BMI; r = −0.558 and p = 0.002, SMM; r = −0.773 and p < 0.001, eGFR; r = −0.205 and p = 0.295, PhA: r = −0.812 and p < 0.001). Furthermore, multiple regression analysis showed that PhA independently affected ROI (Age; Stdβ = 0.108 and p = 0.464, Sex; Stdβ = 0.045 and p = 0.773, BMI; Stdβ = −0.109 and p = 0.491, SMM; Stdβ = −0.281 and p = 0.307, PhA; Stdβ = −0.49 and p = 0.032). The receiver operating characteristic (ROC) curve of PhA for differentiating overestimate and underestimate showed an area under the curve of 0.960. The cut-off value of PhA was determined at 5.00 with a sensitivity of 94.1% and a specificity of 90.9% (Fig. 2). Conclusion This study demonstrated that PhA independently influences the assessment of renal function by eGFR and that GFR may be overestimated in patient with low muscle quality.