Quantitative trait loci for urinary albumin in crosses between C57BL/6J and A/J inbred mice in the presence and absence of Apoe.

We investigated the effect of apolipoprotein E (Apoe) on albuminuria in the males of two independent F2 intercrosses between C57BL/6J and A/J mice, using wild-type inbred strains in the first cross and B6-Apoe(-/-) animals in the second cross. In the first cross, we identified three quantitative trait loci (QTL): chromosome (Chr) 2 [LOD 3.5, peak at 70 cM, confidence interval (C.I.) 28-88 cM]; Chr 9 (LOD 2.0, peak 5 cM, C.I. 5-25 cM); and Chr 19 (LOD 1.9, peak 49 cM, C.I. 23-54 cM). The Chr 2 and Chr 19 QTL were concordant with previously found QTL for renal damage in rat and human. The Chr 9 QTL was concordant with a locus found in rat. The second cross, testing only Apoe(-/-) progeny, did not identify any of these loci, but detected two other loci on Chr 4 (LOD 3.2, peak 54 cM, C.I. 29-73 cM) and Chr 6 (LOD 2.6, peak 33 cM, C.I. 11-61 cM), one of which was concordant with a QTL found in rat. The dependence of QTL detection on the presence of Apoe and the concordance of these QTL with rat and human kidney disease QTL suggest that Apoe plays a role in renal damage.

Predictive performance of renal function equations in renal transplant recipients: an analysis of patient factors in bias.

Creatinine-based equations are available to estimate GFR. After renal transplantation body composition usually changes, thus specific validation is required for transplant recipients. Nine equations were compared with iothalamate glomerular filtration rate (GFR) at 1 year after transplantation in 798 recipients. Equations were analyzed for precision, bias and accuracy. Sources of bias were analyzed by univariate and multivariate analysis, with body mass index (BMI), age and sex as independent variables and bias as dependent variable. Four hundred and seventy-eight patients were studied to assess whether the equations can be used to monitor renal function over time. Predictive performance was modest for all equations. MDRD and Jelliffe 2 were the best predictors of GFR. Bias was significantly related to BMI, age and gender in most equations. Multivariate analysis confirmed their independent contribution to the bias of MDRD, Jelliffe 2 and most other equations. Over time, bias was relatively stable at group level, but predictive performance in individuals was modest. The predictive performance of renal function equations is modest in renal transplants, which hampers their use for accurate assessment of renal function in the individual. The role of patient factors in the systematic error suggests that development of better equations should be feasible by better incorporation of these factors.