Progression of
critically ill patients from
Systemic Inflammatory Response Syndrome (SIRS) to
Multiple Organ Dysfunction Syndrome (
MODS) accounts for more than 75% of deaths in adult
surgical intensive care units. Currently, there is no practical clinical technique to predict the progression of SIRS or
MODS. In this report, we describe an NMR-based metabonomic method to aid detection of these conditions based on abnormal metabolic signatures. We applied pattern recognition methods to analyze one-dimensional (1)H NMR spectra of SIRS and
MODS patient sera. By using Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA), we could distinguish
critically ill patients (n = 52) from healthy controls (n = 26). After noise reduction by Orthogonal Signal Correction (OSC), PLS-DA was also able to clearly discriminate SIRS and
MODS patients. The corresponding coefficients indicated that spectra responsible for the discrimination were located in delta3.06-3.86 NMR integral regions from SIRS, mainly composed of
sugars, amino acids and
glutamine signals, and delta1.18-1.3 and delta4.02-4.1 integral regions of
MODS serum samples, principally consisted of various
proton signals of fatty acyl chains and
glycerol backbone of
lipids, along with
creatinine and
lactate. Our results are 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 suggest that NMR-based metabonomic approach can be developed to diagnose the disease progress of
critically ill patients.