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Metabolomics is a useful approach to explore systemic metabolic variation and to elucidate disease mechanisms. In this study, human plasma metabolic profiles of coronary heart disease (CHD) patients and healthy controls were obtained by gas chromatography-mass spectrometry (GC-MS). A relatively new pattern recognition method, the Monte Carlo tree (MCTree) approach, was used to explore metabolic differences between CHD patients and healthy controls. In this way, CHD patients with different severity of coronary atherosclerosis were classified by the corresponding metabolic profiles. Furthermore, important metabolites contributing to the classification were screened and identified by their mass spectra. Several potential biomarkers were discussed in some detail. The results demonstrated that the proposed method might be a useful tool for discovering metabolic abnormalities and potential biomarkers for diseases.

Repaglinide is a short-acting insulin secretagogue, commonly used for the treatment of type 2 diabetes. In this paper, metabolomics were first applied to research of dynamic urine metabolic profiling and biomarkers of type 2 diabetic KK-Ay mice treated with repaglinide based on GC-MS. Twenty diabetic KK-Ay mice were randomly assigned to four groups and fed with repaglinide for 6, 9, 12, and 14 weeks, respectively. Five C57BL/6 J mice were used as the healthy control group and fed with water as contrast. The PCA scores plot of the identified 41 metabolites showed that as treating time went on, the diabetic groups got closer to the healthy group. Furthermore, five marker metabolites, d-Glucose, d-Galactose, 1,5-Anhydro-d-glucitol, myo-inositol and tartaric acid were screened out, which have similar change footprints of the whole metabolic profiles. The results demonstrated that repaglinide not only regulates the sugars and polyalcohol but also the organic acid in the organism. This work has illustrated the ...

In this paper, metabolic fingerprints were obtained from 102 nasopharyngeal carcinoma (NPC) patients and 107 healthy adults by gas chromatography-mass spectrometry (GC/MS). Partial least squares-discriminant analysis (PLS-DA) has revealed a pattern recognition discriminating the patients from controls, which sensitivity is 89.72% (96/107) and specificity is 85.29% (87/102). Furthermore, double-blind experiment was carried out and satisfactory results were obtained (total correct rate 87.30%). In addition, metabolites that most strongly influence this separation were obtained. The results indicated that a metabonomic approach is feasible and efficient and deserves further evaluation as a potential novel strategy for the detection of nasopharyngeal carcinoma.