Early Prediction of Sepsis by Using Metabolomics

Overview

Sepsis is a serious medical condition associated with a high incidence and mortality rate. It is the leading cause of death in ICU worldwide. Nowadays sepsis was redefined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. Despite the progress made in the pathogenesis of sepsis and advances achieved in medical interventions, the management of sepsis remains a challenge for clinicians. The core problem that precludes the promotion in the management of sepsis is the lack of early and precise prediction. The metabolic profiles will be significantly changed when body suffers from sepsis even though the organ function remains normal, thus making it possible to predict sepsis in the early stage through the detection of the metabolites.

In the past the studies on the prediction and prognosis of sepsis were focused on the levels of protein and gene, and few researches have been done in the field of metabolomics. Actually the changes in the functioning of biological systems can be reflected not only by the specifically expressed protein but also by the metabolism. The metabolic characteristic of biological fluids will be significantly changed when body suffers from pathological or physiological stimulation, and these changes can reveal the disease state or severity while the organ function remains normal due to the compensatory action. Therefore it is possible to predict the occurrence, development and prognosis of sepsis in the early stage through the detection of the concentrations or ratios of these metabolites. The traditional methods are difficult to adapt to the detection of such complicated changes in metabolic profiles when sepsis occurs, thus there is a huge demand of novel means for the metabolic profiling in the inflammatory reaction. By now there have been several researches involved in the metabolic profiling in severe wound, but few have been done in the field of critical care medicine. The metabolic profiles in sepsis patients have remained unclear so far. Therefore it is of great significance to develop a metabolic profiling approach for the measurement and interpretation of the metabolites from the biological samples of sepsis patients and to establish a mode for the metabolomics-based early prediction of sepsis. Several qualitative and quantitative detection methods will be employed to achieve the goal of metabolic profiling in patients with or without sepsis. Principal Components Analysis (PCA) and Partial Least Squares Discrimination Analysis (PLS-DA) methodology will be applied to understand the metabolic profile. The data will be analyzed by Mestrec and MestReNova software package and the Human Metabolome Database (HMDB) searching to identify the biomarkers for the prediction of sepsis. The biomarkers that changed the most will be verified by Liquid Chromatography-Mass Spectrometry (LC-MS).