Metabonomics-Based Study of Clinical Urine Samples in Suboptimal Health with Different Syndromes

Objective. To explore the urinary biochemistry features of syndromes of traditional Chinese medicine (TCM) such as syndrome of stagnation of liver Qi, spleen deficiency, liver Qi stagnation, and spleen deficiency (LSSDS) in sub-optimal health status (SHS). Methods. 12 cases for each syndrome group i...

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Bibliographic Details
Main Authors: Hai-Zhen Cui, Li-Min Wang, Xin Zhao, Yue-Yun Liu, Shao-Xian Wang, Xiao-Hong Li, You-Ming Jiang, Jia-Xu Chen
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Evidence-Based Complementary and Alternative Medicine
Online Access:http://dx.doi.org/10.1155/2013/509134
Description
Summary:Objective. To explore the urinary biochemistry features of syndromes of traditional Chinese medicine (TCM) such as syndrome of stagnation of liver Qi, spleen deficiency, liver Qi stagnation, and spleen deficiency (LSSDS) in sub-optimal health status (SHS). Methods. 12 cases for each syndrome group in SHS were selected, 12 subjects were used as a normal control group, and 1H NMR detection was, respectively, carried out, and the data was corrected by the orthogonal signal correction (OSC) and then adopted a partial least squares (PLS) method for discriminate analysis. Results. The OSC-PLS (ctr) analysis results of the nuclear overhauser enhancement spectroscopy (NOESY) detection indicated that the syndromes in SHS could be differentiated, and there were significant differences in the levels of metabolites of the urine samples of the four groups; the biomarkers of LSSDS in SHS were found out. The contents of citric acid (2.54 and 2.66), trimethylamineoxide (3.26), and hippuric acid (3.98, 7.54, 7.58, 7.62, 7.66, 7.82, and 7.86) in the urine samples of LSSDS group were lower than that of the normal control group. Conclusion. There are differences in the 1H-NMR metabolic spectrum of the urine samples of the four groups, and the specific metabolic products of the LSSDS in SHS can be identified from metabonomics analysis.
ISSN:1741-427X
1741-4288