Plasma Levels of Middle Molecules to Estimate Residual Kidney Function in Haemodialysis without Urine Collection.

BACKGROUND: Residual Kidney Function (RKF) is associated with survival benefits in haemodialysis (HD) but is difficult to measure without urine collection. Middle molecules such as Cystatin C and ß2-microglobulin accumulate in renal disease and plasma levels have been used to estimate kidney function early in this condition. We investigated their use to estimate RKF in patients on HD. DESIGN: Cystatin C, ß2-microglobulin, urea and creatinine levels were studied in patients on incremental high-flux HD or hemodiafiltration(HDF). Over sequential HD sessions, blood was sampled pre- and post-session 1 and pre-session 2, for estimation of these parameters. Urine was collected during the whole interdialytic interval, for estimation of residual GFR (GFRResidual = mean of urea and creatinine clearance). The relationships of plasma Cystatin C and ß2-microglobulin levels to GFRResidual and urea clearance were determined. RESULTS: Of the 341 patients studied, 64% had urine output>100 ml/day, 32.6% were on high-flux HD and 67.4% on HDF. Parameters most closely correlated with GFRResidual were 1/ß2-micoglobulin (r2 0.67) and 1/Cystatin C (r2 0.50). Both these relationships were weaker at low GFRResidual. The best regression model for GFRResidual, explaining 67% of the variation, was: GFRResidual = 160.3 · (1/ß2m) - 4.2. Where ß2m is the pre-dialysis ß2 microglobulin concentration (mg/L). This model was validated in a separate cohort of 50 patients using Bland-Altman analysis. Areas under the curve in Receiver Operating Characteristic analysis aimed at identifying subjects with urea clearance≥2 ml/min/1.73 m2 was 0.91 for ß2-microglobulin and 0.86 for Cystatin C. A plasma ß2-microglobulin cut-off of ≤19.2 mg/L allowed identification of patients with urea clearance ≥2 ml/min/1.73 m2 with 90% specificity and 65% sensitivity. CONCLUSION: Plasma pre-dialysis ß2-microglobulin levels can provide estimates of RKF which may have clinical utility and appear superior to cystatin C. Use of cut-off levels to identify patients with RKF may provide a simple way to individualise dialysis dose based on RKF.

Removal and rebound kinetics of cystatin C in high-flux hemodialysis and hemodiafiltration.

BACKGROUND AND OBJECTIVES: Cystatin C is a 13.3 kD middle molecule of similar size to ß2-microglobulin and a marker of GFR in CKD. This study aimed to determine cystatin C kinetics in hemodialysis to understand whether blood concentrations may predict residual renal function and middle-molecule clearance. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Cystatin C removal and rebound kinetics were studied in 24 patients on high-flux hemodialysis or hemodiafiltration. To determine whether cystatin C concentrations are predictable, an iterative two-pool mathematical model was applied. RESULTS: Cystatin C was cleared effectively, although less than ß2-microglobulin (reduction ratios ± SD are 39% ± 11 and 51% ± 11). Cystatin C rebounded to 95% ± 5% of predialysis concentration by 12 hours postdialysis. The two-pool kinetic model showed excellent goodness of fit. Modeled extracellular cystatin C pool volume is smaller than that predicted, comprising 25.5% ± 9.2 of total body water. Iterated parameters, including nonrenal clearance, showed wide interindividual variation. Modeled nonrenal clearance was substantially higher than renal clearance in this population at 25.1 ± 6.6 ml/min per 1.73 m(2) body surface area. CONCLUSIONS: Plasma cystatin C levels may be used to measure middle-molecule clearance. Levels rebound substantially postdialysis and plateau in the interdialytic period. At low GFR, nonrenal clearance predominates over renal clearance, and its interindividual variation will limit use of cystatin C to predict residual renal function in advanced kidney disease.

Disease-specific predictive formulas for energy expenditure in the dialysis population.

OBJECTIVE: Metabolic rate is poorly understood in advanced kidney disease because direct measurement is expensive and time-consuming. Predictive equations for resting energy expenditure (REE) are needed based on simple bedside parameters. Algorithms derived for normal individuals may not be valid in the renal population. We aimed to develop predictive equations for REE specifically for the dialysis population. DESIGN: Two-hundred subjects on maintenance dialysis underwent a comprehensive metabolic assessment including REE from indirect calorimetry. Parameters predicting REE were identified, and regression equations developed and validated in 20 separate subjects. RESULTS: Mean REE was 1,658 ± 317 kCal/day (males) and 1,380 ± 287 kCal/day (females). Weight and height correlated positively with REE (r(2) = 0.54 and 0.31) and negatively with age older than 65 years (r(2) = 0.18). The energy cost of a unitary kilogram of body weight increased nonlinearly for lower body mass index (BMI). Existing equations derived in normal individuals underestimated REE (bias 50-114 kCal/day for 3 equations). The novel derived equation was REE(kCal/day) = -2.497·Age·Factorage+0.011·height(2.023) + 83.573·Weight(0.6291) + 68.171·Factorsex, where Factorage = 1 if 65 years or older and 0 if younger than 65, and Factorsex = 1 if male and 0 if female. This algorithm performed at least as well as those developed for normals in terms of limits of agreement and reduced bias. In validation with the Bland-Altman technique, bias was not significant for our algorithm (-22 ± 96 kCal/day). The 95% limits of agreement were +380 to -424 kCal/day. CONCLUSION: Existing equations for REE derived from normal individuals are not valid in the dialysis population. The relatively increased REE in those with low BMI implies the need for higher dialysis doses in this subgroup. This disease-specific algorithm may be useful clinically and as a research tool to predict REE.