[(How) can progression of chronic kidney diseases be retarded?] / (Wie) kann die Progression chronischer Nierenerkrankungen verlangsamt werden?

(How) can progression of chronic kidney diseases be retarded? Abstract. A variety of etiologically diverse chronic kidney diseases (CKD) may not only progress to end stage renal disease, but also increase cardiovascular morbidity and overall mortality. Once a critical mass of renal tissue has been irreversibly scarred, maladaptive mechanisms lead to progressive loss of the remaining kidney tissue, independent of the initial mechanism causing kidney injury. Medical treatments that retard the progression of CKD by directly and specifically inhibiting profibrotic processes unfortunately do not yet exist. The best established treatment intervention to retard CKD progression consists of ACE inhibitors and angiotensin receptor blockers in patients with relevant proteinuria. Recent data support alkali supplementation. Furthermore, moderation of salt consumption and smoking cessation are probably beneficial. Finally, "second hits" leading to acute kidney injury episodes should be avoided. Apart from these unspecific measures, it is essential to identify renal diseases amenable to cause-specific treatments. The possibilities as well as the limits of treatment options to retard progression of CKD will be critically discussed in this article.

Quantification of excretory renal function and urinary protein excretion by determination of body cell mass using bioimpedance analysis.

BACKGROUND: Creatinine clearance (CrCl) based on 24 h urine collection is an established method to determine glomerular filtration rate (GFR). However, its measurement is cumbersome and the results are frequently inaccurate. The aim of this study was to develop an alternative method to predict CrCl and urinary protein excretion based on plasma creatinine and the quantification of muscle mass through bioimpedance analysis (BIA). METHODS: In 91 individuals with normal and impaired renal function CrCl was measured from 24 h urine excretion and plasma creatinine concentration. A model to predict 24 h-creatininuria was developed from various measurements assessing muscle mass such as body cell mass (BCM) and fat free mass (FFM) obtained by BIA, skinfold caliper and other techniques (training group, N = 60). Multivariate regression analysis was performed to predict 24 h-creatininuria and to calculate CrCl. A validation group (N = 31) served to compare predicted and measured CrCl. RESULTS: Overall (accuracy, bias, precision, correlation) the new BIA based prediction model performed substantially better compared with measured CrCl (P15 = 87 %, bias = 0, IQR of differences = 7.9 mL/min/1.73 m(2), R = 0.972) versus established estimation formulas such as the 4vMDRD (P15 = 6 %, bias = -8.3 mL/min/1.73 m(2), IQR = 13.7 mL/min/1.73 m(2), R = 0.935), CKD-EPI (P15 = 29 %, bias = -7.0 mL/min/1.73 m(2), IQR = 12.1 mL/min/1.73 m(2), R = 0.932, Cockcroft-Gault equations (P15 = 55 %, bias = -4.4 mL/min/1.73 m(2), IQR = 9.0 mL/min/1.73 m(2), R = 0.920). The superiority of the new method over established prediction formulas was most obvious in a subgroup of individuals with BMI > 30 kg/m(2) and in a subgroup with CrCl > 60 mL/min/1.73 m(2). Moreover, 24 h urinary protein excretion could be estimated accurately by normalization with 24 h-creatininuria derived from BIA based BCM. CONCLUSION: Prediction of CrCl based on estimated urinary creatinine excretion determined from measurement of BCM by BIA technique is both accurate and convenient to quantify renal function in normal and diseased states. This new method may become particularly helpful for the evaluation of patients with borderline renal insufficiency and/or with abnormal body composition.