A pilot metabolic profiling study in serum of patients with chronic kidney disease based on (1) H-NMR-spectroscopy.
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%.


CONCLUSIONS
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.
The Department of Biomedical Engineering, Medical school, Shenzhen University, Guangdong, P.R. China.
Suwen
S
Qi
Xin
X
Ouyang
Linqian
L
Wang
Wujian
W
Peng
Jinli
J
Wen
Yong
Y
Dai
D000328:Adult; D016022:Case-Control Studies; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D009682:Magnetic Resonance Spectroscopy; D008297:Male; D055442:Metabolome; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D010865:Pilot Projects; D025341:Principal Component Analysis; D011522:Protons; D051436:Renal Insufficiency, Chronic / Q000097:blood*; D015203:Reproducibility of Results
D011522:Protons