BACKGROUND: In this article (the second of two companion studies), we report whether bioelectrical impedance analysis (BIA) can be used to predict muscle mass in patients with chronic kidney disease (CKD), and whether using this predicted muscle mass can improve the estimation of glomerular filtration rate (GFR). METHODS: Seventy five non-diabetic patients with CKD (mean age +/- SD, 65.1 +/- 12.0 years; mean GFR 45.9 +/- 28.8 ml/min/1.73 m2) underwent body composition analysis by dual energy X-ray absorptiometry to provide a criterion marker of skeletal muscle mass (appendicular lean mass, ALM). Validity of a published BIA equation to predict ALM was evaluated and a new BIA equation was generated (ALM(BIA)) and cross-validated by the leave-one-out procedure. Renal inulin clearance provided a criterion measure of GFR (GFR(inu)). The performance of the equation including ALM(BIA) to estimate GFR(inu) was compared with demographic variables as used in the modification of diet in renal disease (MDRD) equation, by determining bias, limits of agreement and accuracy. RESULTS: The previously published BIA equation to predict ALM was not valid in these patients with CKD. In contrast, our new ALM(BIA) equation cross-validated successfully. Compared with the MDRD demographic variables, using ALM(BIA) to predict GFR(inu) improved estimation performance, showing reduced bias (4.3 vs 15.6 ml/min) and improved limits of agreement (41.1 vs 59.2 ml/min) and accuracy (69.7 vs 39.4% of patients' predicted GFR did not deviate by more than 30% of GFR(inu)). CONCLUSIONS: ALM(BIA) provides a clinically obtainable and valid method to predict muscle mass in patients with CKD, and using ALM(BIA) improves the estimation of GFR(inu). Researchers developing future GFR estimation equations should consider including ALM(BIA).
BACKGROUND: In this article (the second of two companion studies), we report whether bioelectrical impedance analysis (BIA) can be used to predict muscle mass in patients with chronic kidney disease (CKD), and whether using this predicted muscle mass can improve the estimation of glomerular filtration rate (GFR). METHODS: Seventy five non-diabeticpatients with CKD (mean age +/- SD, 65.1 +/- 12.0 years; mean GFR 45.9 +/- 28.8 ml/min/1.73 m2) underwent body composition analysis by dual energy X-ray absorptiometry to provide a criterion marker of skeletal muscle mass (appendicular lean mass, ALM). Validity of a published BIA equation to predict ALM was evaluated and a new BIA equation was generated (ALM(BIA)) and cross-validated by the leave-one-out procedure. Renal inulin clearance provided a criterion measure of GFR (GFR(inu)). The performance of the equation including ALM(BIA) to estimate GFR(inu) was compared with demographic variables as used in the modification of diet in renal disease (MDRD) equation, by determining bias, limits of agreement and accuracy. RESULTS: The previously published BIA equation to predict ALM was not valid in these patients with CKD. In contrast, our new ALM(BIA) equation cross-validated successfully. Compared with the MDRD demographic variables, using ALM(BIA) to predict GFR(inu) improved estimation performance, showing reduced bias (4.3 vs 15.6 ml/min) and improved limits of agreement (41.1 vs 59.2 ml/min) and accuracy (69.7 vs 39.4% of patients' predicted GFR did not deviate by more than 30% of GFR(inu)). CONCLUSIONS: ALM(BIA) provides a clinically obtainable and valid method to predict muscle mass in patients with CKD, and using ALM(BIA) improves the estimation of GFR(inu). Researchers developing future GFR estimation equations should consider including ALM(BIA).
Authors: James B Wetmore; Robyn A Honea; Eric D Vidoni; Ammar Almehmi; Jeffrey M Burns Journal: Nephrol Dial Transplant Date: 2010-11-23 Impact factor: 5.992