A metabonomic approach to early prognostic evaluation of experimental sepsis by 1H NMR and pattern recognition

This study proposes an NMR‐based metabonomic approach to early prognostic evaluation of sepsis. Forty septic rats receiving cecal ligation and puncture (CLP) were divided into the surviving group and nonsurviving group on day 6, while 20 sham‐operated rats served as the control group. Serum samples were collected from septic and sham‐operated rats at 12 h after surgery and analyzed using 1H NMR spectroscopy. Orthogonal partial least squares (OPLS) were applied and showed clustering according to predefined groups, indicating that NMR‐based metabolic profiling could reveal pathologic characteristics in the serum of sham‐operated, surviving, and nonsurviving septic rats. In addition, six characteristic metabolites including lactate, alanine, acetate, acetoacetate, hydroxybutyrate, and formate, which are mainly involved in energy metabolism, changed markedly in septic rats, especially in the nonsurvivors. Using these metabolites, a predictive model for prognostic evaluation of sepsis was constructed using a radial basis function neural network (RBFNN) with a prediction accuracy of about 87% by test samples. The results indicated that the NMR‐based metabonomic approach is a potential technique for the early prognostic evaluation of sepsis. Copyright © 2009 John Wiley & Sons, Ltd.

[1]  U. Sungurtekin,et al.  Usefulness of procalcitonin for diagnosis of sepsis in the intensive care unit , 2002, Critical care.

[2]  J. Scribante,et al.  Gastric intramucosal pH and blood lactate in severe sepsis , 1997, Anaesthesia.

[3]  France Gauvin,et al.  Serum procalcitonin and C-reactive protein levels as markers of bacterial infection: a systematic review and meta-analysis. , 2004, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[4]  I. Chaudry,et al.  Sepsis and septic shock--a review of laboratory models and a proposal. , 1980, The Journal of surgical research.

[5]  Xin Lu,et al.  LC-MS-based metabonomics analysis. , 2008, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[6]  G. Clermont,et al.  Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care , 2001, Critical care medicine.

[7]  Elaine Holmes,et al.  Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis , 2004, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.

[8]  G. Jacobsen,et al.  Early lactate clearance is associated with improved outcome in severe sepsis and septic shock* , 2004, Critical care medicine.

[9]  Jianghua Feng,et al.  1H NMR analysis for metabolites in serum and urine from rats administrated chronically with La(NO3)3. , 2002, Analytical biochemistry.

[10]  K. Reinhart,et al.  Direct costs of severe sepsis in three German intensive care units based on retrospective electronic patient record analysis of resource use , 2002, Intensive Care Medicine.

[11]  E. Ivers,et al.  Early Goal-Directed Therapy in the Treatment of Severe Sepsis and Septic Shock , 2001 .

[12]  Yaseen Arabi,et al.  Assessment of six mortality prediction models in patients admitted with severe sepsis and septic shock to the intensive care unit: a prospective cohort study , 2003, Critical care.

[13]  G. Ebrahim,et al.  Strategies for the treatment of sepsis. , 2005, Journal of tropical pediatrics.

[14]  J. Lindon,et al.  NMR‐based metabonomic approaches for evaluating physiological influences on biofluid composition , 2005, NMR in biomedicine.

[15]  L. Pacifico,et al.  Reliability of procalcitonin concentrations for the diagnosis of sepsis in critically ill neonates. , 1998, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[16]  J. Revelly,et al.  Lactate and glucose metabolism in severe sepsis and cardiogenic shock* , 2005, Critical care medicine.

[17]  Juan Carlos Cobas,et al.  Nuclear magnetic resonance data processing. MestRe‐C: A software package for desktop computers , 2003 .

[18]  Xin-ru Liu,et al.  A metabonomic approach to early prognostic evaluation of experimental sepsis. , 2008, The Journal of infection.

[19]  O. Ljungqvist,et al.  Derangements in mitochondrial metabolism in intercostal and leg muscle of critically ill patients with sepsis-induced multiple organ failure. , 2006, American journal of physiology. Endocrinology and metabolism.

[20]  Huifeng Wu,et al.  Comparison of metabolic profiles from serum from hepatotoxin-treated rats by nuclear-magnetic-resonance-spectroscopy-based metabonomic analysis. , 2005, Analytical biochemistry.

[21]  J. H. Shaw,et al.  Energy metabolism in trauma and sepsis: the role of fat. , 1983, Progress in clinical and biological research.

[22]  L A Aarden,et al.  Increased plasma levels of interleukin-6 in sepsis. , 1989, Blood.

[23]  S. Wold,et al.  Orthogonal projections to latent structures (O‐PLS) , 2002 .

[24]  R. Kleinpell,et al.  Incidence, pathogenesis, and management of sepsis: an overview. , 2006, AACN advanced critical care.

[25]  P. Rehak,et al.  Procalcitonin for early prediction of survival outcome in postoperative critically ill patients with severe sepsis. , 2006, British journal of anaesthesia.

[26]  John C. Lindon,et al.  Metabonomics: metabolic processes studied by NMR spectroscopy of biofluids , 2000 .

[27]  J. Ni,et al.  Metabolic profiling of serum from gadolinium chloride-treated rats by 1H NMR spectroscopy. , 2007, Analytical biochemistry.

[28]  K. Reinhart,et al.  Diagnostic value and prognostic implications of serum procalcitonin after cardiac surgery: a systematic review of the literature , 2006, Critical care.

[29]  H. Michie,et al.  Metabolism of Sepsis and Multiple Organ Failure , 1996, World Journal of Surgery.

[30]  Mika Ala-Korpela,et al.  Critical evaluation of 1H NMR metabonomics of serum as a methodology for disease risk assessment and diagnostics , 2008, Clinical chemistry and laboratory medicine.

[31]  C. Roussos,et al.  Neutrophil CD64 expression and serum IL-8: sensitive early markers of severity and outcome in sepsis. , 2006, Cytokine.

[32]  I. Kurt,et al.  High C-reactive protein and low cholesterol levels are prognostic markers of survival in severe sepsis. , 2007, Journal of clinical anesthesia.

[33]  K. Reinhart,et al.  Sepsis: definitions and diagnosis. , 1998, International journal of clinical practice. Supplement.

[34]  J. Hibbert,et al.  Except for alanine, muscle protein catabolism is not influenced by alterations in glucose metabolism during sepsis. , 1995, Archives of surgery.

[35]  Marco Novelli,et al.  Mitochondrial dysfunction in a long-term rodent model of sepsis and organ failure. , 2004, American journal of physiology. Regulatory, integrative and comparative physiology.

[36]  Julian L Griffin,et al.  Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis. , 2003, Current opinion in chemical biology.

[37]  B. Davis,et al.  Neutrophil CD64 is an improved indicator of infection or sepsis in emergency department patients. , 2006, Archives of pathology & laboratory medicine.

[38]  H. Hotelling.  The Generalization of Student’s Ratio , 1931 .

[39]  J. Siewert,et al.  [Indicators for early prediction of outcome in sepsis]. , 2005, Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen.

[40]  Haralambos Sarimveis,et al.  A classification technique based on radial basis function neural networks , 2006, Adv. Eng. Softw..

[41]  D. Morgan,et al.  A comparison of the Ranson, Glasgow, and APACHE II scoring systems to a multiple organ system score in predicting patient outcome in pancreatitis. , 2005, American journal of surgery.

[42]  C. A. Murthy,et al.  On visualization and aggregation of nearest neighbor classifiers , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  W. Weckwerth,et al.  Metabolomics: from pattern recognition to biological interpretation. , 2005, Drug discovery today.

[44]  E. Rackow,et al.  Interpretation of blood lactate concentrations in patients with sepsis , 1998, The Lancet.

[45]  L. G. Thijs,et al.  Increased plasma levels of interleukin-6 in sepsis [see comments] , 1989 .

[46]  S. Lowry,et al.  An overview of mortality risk prediction in sepsis. , 1995, Critical care medicine.

[47]  P Franchimont,et al.  Cytokine serum level during severe sepsis in human IL-6 as a marker of severity. , 1992, Annals of surgery.

[48]  Warren Hs.  Strategies for the treatment of sepsis. , 1997 .

[49]  A. Algra,et al.  Prognostic scoring systems to predict outcome in peritonitis and intra‐abdominal sepsis , 1997, The British journal of surgery.