Metabonomic Profiling In Traumatic Critically Ill Patients

Progression of critically ill patients from the systemic inflammatory response syndrome (SIRS) to the multiple organ dysfunction syndrome (MODS) acounts for more than 75% of deaths in adult surgical intensive care units. Currently there is no practical technique in clinic to predict the progression. This study explored a NMR-based metabonomic method as a diagnostic tool for the prediction because the progression was accompanied by metabolic changes. Pattern recognition methods were used to analyze serum 1D 1H-NMR spectra in traumatic patient with SIRS and MODS. By using unsupervised principal component analysis (PCA) and supervised partial least squares-discriminant analysis (PLS-DA), critically ill patients could be distinguished from healthy controls. After a noise reduction by orthogonal signal correction (OSC), PLS-DA was able to clearly discriminate SIRS and MODS. The corresponding coefficients indicated that spectra responsible for the discrimination were located inδ3.06~3.86 NMR integral regions from SIRS, mainly composed of sugar and amino acid signals, andδ1.18~1.3 andδ4.02~ 4.1 integral regions of MODS, principally consisted of various proton signals of fatty acyl chains and glycerol backbone of lipids, along with creatinine and lactate. The results were consistent with the clinical observations that carbohydrate and amino acid levels changes in the early course of critical illness (SIRS stage) and significant disturbances in fat metabolism and development of organ abnormalities become the characteristics in the late stage (MODS). These data suggested that NMR-based metabonomic approach may be developed to predict the disease progress in critically ill patients.