AIM: To study the metabolic profiling of serum samples from compensated and decompensated cirrhosis patients. METHODS: A pilot metabolic profiling study was conducted using three groups: compensated cirrhosis patients...AIM: To study the metabolic profiling of serum samples from compensated and decompensated cirrhosis patients. METHODS: A pilot metabolic profiling study was conducted using three groups: compensated cirrhosis patients (n = 30), decompensated cirrhosis patients (n = 30) and healthy controls (n = 30). A 1H nuclear magnetic resonance (NMR)-based metabonomics approach was used to obtain the serum metabolic profiles of the samples. The acquired data were processed by multivariate principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA). RESULTS: The OPLS-DA model was capable of distinguishing between decompensated and compensated cirrhosis patients, with an R2Y of 0.784 and a Q2Y of 0.598. Twelve metabolites, such as pyruvate, phenylala-nine and succinate, were identified as the most influential factors for the difference between the two groups. The validation of the diagnosis prediction showed that the accuracy of the OPLS-DA model was 85% (17/20). CONCLUSION: 1H NMR spectra combined with pattern recognition analysis techniques offer a new way to diagnose compensated and decompensated cirrhosis in the future.展开更多
文摘AIM: To study the metabolic profiling of serum samples from compensated and decompensated cirrhosis patients. METHODS: A pilot metabolic profiling study was conducted using three groups: compensated cirrhosis patients (n = 30), decompensated cirrhosis patients (n = 30) and healthy controls (n = 30). A 1H nuclear magnetic resonance (NMR)-based metabonomics approach was used to obtain the serum metabolic profiles of the samples. The acquired data were processed by multivariate principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA). RESULTS: The OPLS-DA model was capable of distinguishing between decompensated and compensated cirrhosis patients, with an R2Y of 0.784 and a Q2Y of 0.598. Twelve metabolites, such as pyruvate, phenylala-nine and succinate, were identified as the most influential factors for the difference between the two groups. The validation of the diagnosis prediction showed that the accuracy of the OPLS-DA model was 85% (17/20). CONCLUSION: 1H NMR spectra combined with pattern recognition analysis techniques offer a new way to diagnose compensated and decompensated cirrhosis in the future.
