Improved prenatal assessment of kidney disease using multiple ultrasound features.

BACKGROUND AND HYPOTHESIS: Congenital anomalies of the kidney and the urinary tract (CAKUT), often discovered in utero, cover a wide spectrum of outcomes ranging from normal postnatal kidney function to fetal death. The current ultrasound workup does not allow for an accurate assessment of the outcome. The present study aimed to significantly improve the ultrasound-based prediction of postnatal kidney survival in CAKUT. METHODS: Histological analysis of kidneys of 15 CAKUT fetuses was performed to better standardize the ultrasound interpretation of dysplasia and cysts. Ultrasound images of 140 CAKUT fetuses with 2-year postnatal follow-up were annotated for amniotic fluid volume and kidney number, size, dysplasia and/or cysts using standardized ultrasound readout. Association of ultrasound features and clinical data (sex and age at diagnosis) with postnatal kidney function was studied using logistic regression. Amniotic fluid proteome associated to kidney dysplasia or cysts was characterized by mass spectrometry. RESULTS: Histologically, poor ultrasound corticomedullary differentiation was associated to dysplastic lesions and ultrasound hyperechogenicity was associated to the presence of microcysts. Of all ultrasound and clinical parameters, reduced amniotic volume, dysplasia and cysts were the best predictors of poor outcome (Odd ratio = 57 [95%CI: 11-481], 20 [3-225] and 7 [1-100], respectively). Their combination into an algorithm improved prediction of postnatal kidney function compared to amniotic volume alone (area under the ROC curve = 0.92 [0.86-0.98] in a 10-fold cross validation). Dysplasia and cysts were correlated (Cramer's V coefficient = 0.44, p<0.0001), but amniotic fluid proteome analysis revealed that they had distinct molecular origin (extracellular matrix and cell contacts versus cellular death, respectively), probably explaining the additivity of their predictive performances. CONCLUSION: Antenatal clinical advice for CAKUT pregnancies can be improved by a more standardized and combined interpretation of ultrasound data.

A capillary electrophoresis coupled to mass spectrometry pipeline for long term comparable assessment of the urinary metabolome.

Although capillary electrophoresis coupled to mass spectrometry (CE-MS) has potential application in the field of metabolite profiling, very few studies actually used CE-MS to identify clinically useful body fluid metabolites. Here we present an optimized CE-MS setup and analysis pipeline to reproducibly explore the metabolite content of urine. We show that the use of a beveled tip capillary improves the sensitivity of detection over a flat tip. We also present a novel normalization procedure based on the use of endogenous stable urinary metabolites identified in the combined metabolome of 75 different urine samples from healthy and diseased individuals. This method allows a highly reproducible comparison of the same sample analyzed nearly 130 times over a range of 4 years. To demonstrate the use of this pipeline in clinical research we compared the urinary metabolome of 34 newborns with ureteropelvic junction (UPJ) obstruction and 15 healthy newborns. We identified 32 features with differential urinary abundance. Combination of the 32 compounds in a SVM classifier predicted with 76% sensitivity and 86% specificity UPJ obstruction in a separate validation cohort of 24 individuals. Thus, this study demonstrates the feasibility to use CE-MS as a tool for the identification of clinically relevant urinary metabolites.

Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = -0.8031; p<0.0001 and ρ = -0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = -0.6557; p = 0.0001 and ρ = -0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.