Background: Chronic kidney disease (CKD) is the end point of a number of renal and systemic diseases. The metabolomics with a highly multiplexed and efficient manner is a challenging goal in nephrology.
Methods: A (1) H-NMR based metabolomics approach was applied to establish a human CKD serum metabolic profile. Serum samples were obtained from CKD patients with four stages (N= 80) and healthy controls (N= 28). The data acquired by CMPG spectrum were further processed by pattern recognition (PR) analysis. Principal components analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was capable of clustering the disease groups and establishing disease-specific metabolites profile.
Results: The classification models could grade CKD patients with considerably high value of Q(2) and R(2) . The significant endogenous metabolites that contributed to distinguish CKD in different stages included the products of glycolysis (glucose, lactate), amino acids (valine, alanine, glutamate, glycine), organic osmolytes (betaine, myo-inositol, taurine, glycerophosphcholine), and so on. Based on these metabolites, the model for diagnosing patients with CKD achieved the sensitivity and specificity of 100%.
Conclusion: The study illustrated that serum metabolic profile was altered in response to renal dysfunction and the progression of CKD. The identified metabolic biomarkers may provide useful information for the diagnosis of CKD, especially in early stages.
© 2012 Wiley Periodicals, Inc.