Increased urinary osmolyte excretion indicates chronic kidney disease severity and progression rate.

Background: Chronic kidney disease (CKD) is a recognized global health problem. While some CKD patients remain stable after initial diagnosis, others can rapidly progress towards end-stage renal disease (ESRD). This makes biomarkers capable of detecting progressive forms of CKD extremely valuable, especially in non-invasive biofluids such as urine. Screening for metabolite markers using non-targeted metabolomic techniques like nuclear magnetic resonance spectroscopy is increasingly applied to CKD research. Methods: A cohort of CKD patients (n = 227) with estimated glomerular filtration rates (eGFRs) ranging from 9.4-130 mL/min/1.73 m2 was evaluated and urine metabolite profiles were characterized in relation to declining eGFR. Nested in this cohort, a retrospective subset (n = 57) was investigated for prognostic metabolite markers of CKD progression, independent of baseline eGFR. A transcriptomic analysis of murine models of renal failure was performed to validate selected metabolomic findings. Results: General linear modeling revealed 11 urinary metabolites with significant associations to reduced eGFR. Linear modelling specifically showed that increased urine concentrations of betaine (P < 0.05) and myo-inositol (P < 0.05) are significant prognostic markers of CKD progression. Conclusions: Renal organic osmolytes, betaine and myo-inositol play a critical role in protecting renal cells from hyperosmotic stress. Kidney tissue transcriptomics of murine preclinical experimentation identified decreased expression of Slc6a12 and Slc5a11 mRNA in renal tissue consistent with defective tubular transport of these osmolytes. Imbalances in renal osmolyte regulation lead to increased renal cell damage and thus more progressive forms of CKD. Increases in renal osmolytes in urine could provide clinical diagnostic and prognostic information on CKD outcomes.

NMR-based metabonomics detection of differences in the metabolism of hydrolysed versus intact protein of similar amino acid profile.

BACKGROUND: Proton nuclear magnetic resonance (NMR)-based metabonomics has only recently been applied to nutritional research. The limitation of any analytical technique is its sensitivity in detecting the smallest variation. Alterations in nutrition often produce only subtle metabolic modulations. The objective of this study was to determine if NMR-based metabonomics could detect variations in the metabolic profile of urine from pigs digesting either native casein (NC) or the same casein that had been enzymatically hydrolysed (EHC). NMR permits simultaneous detection of a large number of metabolites, thus allowing detection of unanticipated metabolic fluctuations that may otherwise have gone undetected with the use of only targeted analysis. RESULTS: Partial least squares discriminant analysis identified significantly (P < 0.05) higher urinary excretions of leucine, valine, taurine and glycine by pigs on the EHC-based diet. CONCLUSION: NMR-based metabonomics is a sensitive method that can uncover unanticipated metabolic changes brought about by physicochemical changes to the feedstock (i.e. hydrolysis). The data show a lower efficiency of retention by the kidney of some amino acids following ingestion of a hydrolysed protein.