A GC-based metabonomics investigation of type 2 diabetes by organic acids metabolic profile

“Metabonomics” method requires the development of rapid, advanced analytical tools and GC will play an important role for its special advantage. In this study we show the application of GC-based metabonomics to investigate the control and type 2 diabetes (DM2) patients by urinary organic acids metabolic profile. After peak matching, multivariate statistical analysis methods: principal components analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used. The results showed that there was a relationship between organic acids metabolic profiles and DM2, and PLS-DA can distinguish the DM2 patients from the control. Five organic acids as potential biomarkers were identified.