Skip to main content
SpringerLink
Log in

Metabonomics and Intensive Care

  • Chapter
Annual Update in Intensive Care and Emergency Medicine 2016

Part of the book series: Annual Update in Intensive Care and Emergency Medicine ((AUICEM))

  • 2017 Accesses

Abstract

Metabonomics is “the quantitative measurement over time of the metabolic responses of an individual or population to drug treatment or other intervention” [1], such as a disease process, and provides a ‘top-down’ integrated overview of the biochemistry in a complex system. The metabolic profile is determined by both host genetic and environmental factors [2]. As such, metabonomics has great potential for intensive care medicine, where patients are complex and understanding the relationship of host factors, disease and treatment effects is key to improving care. Approaches that focus on single or small sets of biomarkers may fail to capture this complexity, so metabonomics may have advantages for both understanding diseases and improving diagnostics and treatment monitoring.

Spectroscopic techniques, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry, have been used to determine the global metabolic profiles of numerous types of biological samples. Most commonly, blood and urine are analyzed but any biological specimens, including tissue, cerebrospinal fluid or exhaled breath condensate, can be used [3–6]. Metabonomic methods have been used to evaluate numerous clinically-significant conditions including trauma [7, 8], acute kidney injury (AKI) and monitoring of dialysis [9–11], subarachnoid hemorrhage [12], and acute lung injury (ALI) [13].

The two broad analytical platforms, NMR and mass spectrometry, each have their own strengths and weaknesses and together give complementary information. Data can be acquired that either provides non-targeted global metabolic information, which is useful for initial biomarker discovery, or can be targeted to obtain detailed information on a specific class of metabolites or metabolic processes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Holmes E, Wilson ID, Nicholson JK (2008) Metabolic phenotyping in health and disease. Cell 134:714–717

    Article  CAS  PubMed  Google Scholar 

  2. Nicholson JK, Lindon JC (2008) Systems biology: Metabonomics. Nature 455:1054–1056

    Article  CAS  PubMed  Google Scholar 

  3. Nicholson JK, Connelly J, Lindon JC, Holmes E (2002) Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 1:153–161

    Article  CAS  PubMed  Google Scholar 

  4. Nicholson JK, Lindon JC (2008) Systems biology – Metabonomics. Nature 455:1054–1056

    Article  CAS  PubMed  Google Scholar 

  5. Beckonert O, Keun HC, Ebbels TM et al (2007) Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2:2692–2703

    Article  CAS  PubMed  Google Scholar 

  6. Sofia M, Maniscalco M, de Laurentiis G, Paris D, Melck D, Motta A (2011) Exploring airway diseases by NMR-based metabonomics: a review of application to exhaled breath condensate. J Biomed Biotechnol 2011:403260

    Article  PubMed  PubMed Central  Google Scholar 

  7. Cohen MJ, Serkova NJ, Wiener-Kronish J, Pittet JF, Niemann CU (2010) 1H-NMR-based metabolic signatures of clinical outcomes in trauma patients – beyond lactate and base deficit. The Journal of trauma 69:31–40

    Article  PubMed  Google Scholar 

  8. Mao H, Wang H, Wang B et al (2009) Systemic metabolic changes of traumatic critically ill patients revealed by an NMR-based metabonomic approach. J Proteome Res 8:5423–5430

    Article  CAS  PubMed  Google Scholar 

  9. Sato E, Kohno M, Yamamoto M, Fujisawa T, Fujiwara K, Tanaka N (2011) Metabolomic analysis of human plasma from haemodialysis patients. Eur J Clin Invest 41:241–255

    Article  CAS  PubMed  Google Scholar 

  10. Beger RD, Holland RD, Sun J et al (2008) Metabonomics of acute kidney injury in children after cardiac surgery. Pediatr Nephrol 23:977–984

    Article  PubMed  Google Scholar 

  11. Al-Ismaili Z, Palijan A, Zappitelli M (2011) Biomarkers of acute kidney injury in children: discovery, evaluation, and clinical application. Pediatr Nephrol 26:29–40

    Article  PubMed  Google Scholar 

  12. Dunne VG, Bhattachayya S, Besser M, Rae C, Griffin JL (2005) Metabolites from cerebrospinal fluid in aneurysmal subarachnoid haemorrhage correlate with vasospasm and clinical outcome: a pattern-recognition 1H NMR study. NMR Biomed 18:24–33

    Article  CAS  PubMed  Google Scholar 

  13. Stringer KA, Serkova NJ, Karnovsky A, Guire K, Paine R, Standiford TJ (2011) Metabolic consequences of sepsis-induced acute lung injury revealed by plasma (1)H-nuclear magnetic resonance quantitative metabolomics and computational analysis. Am J Physiol Lung Cell Mol Physiol 300:L4–L11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dumas ME, Maibaum EC, Teague C et al (2006) Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. Anal Chem 78:2199–2208

    Article  CAS  PubMed  Google Scholar 

  15. Lenz EM, Wilson ID (2007) Analytical strategies in metabonomics. J Proteome Res 6:443–458

    Article  CAS  PubMed  Google Scholar 

  16. Izquierdo-Garcia JL, Nin N, Ruiz-Cabello J et al (2011) A metabolomic approach for diagnosis of experimental sepsis. Intensive Care Med 37:2023–2032

    Article  CAS  Google Scholar 

  17. Shin JH, Yang JY, Jeon BY et al (2011) (1)H NMR-based metabolomic profiling in mice infected with Mycobacterium tuberculosis. J Proteome Res 10:2238–2247

    Article  CAS  PubMed  Google Scholar 

  18. Li Y, Hou M, Wang JG et al (2012) Changes of lymph metabolites in a rat model of sepsis induced by cecal ligation and puncture. J Trauma Acute Care Surg 73:1545–1552

    Article  PubMed  Google Scholar 

  19. Steelman SM, Johnson P, Jackson A, Schulze J, Chowdhary BP (2014) Serum metabolomics identifies citrulline as a predictor of adverse outcomes in an equine model of gut-derived sepsis. Physiol Genomics 46:339–347

    Article  CAS  PubMed  Google Scholar 

  20. Langley RJ, Tipper JL, Bruse S et al (2014) Integrative “omic” analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from systemic inflammatory response syndromes. Am J Respir Crit Care Med 190:445–455

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Antunes LC, Arena ET, Menendez A et al (2011) Impact of salmonella infection on host hormone metabolism revealed by metabolomics. Infect Immun 79:1759–1769

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ghosh S, Sengupta A, Sharma S, Sonawat HM (2013) Metabolic perturbations of kidney and spleen in murine cerebral malaria: (1)H NMR-based metabolomic study. PloS One 8:e73113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chen L, Fan J, Li Y et al (2014) Modified Jiu Wei Qiang Huo decoction improves dysfunctional metabolomics in influenza A pneumonia-infected mice. Biomed Chromatogr 28:468–474

    Article  CAS  PubMed  Google Scholar 

  24. Xu PB, Lin ZY, Meng HB et al (2008) A metabonomic approach to early prognostic evaluation of experimental sepsis. J Infect 56:474–481

    Article  PubMed  Google Scholar 

  25. Lam CW, Law CY, To KK et al (2014) NMR-based metabolomic urinalysis: a rapid screening test for urinary tract infection. Clin Chim Acta 436:217–223

    Article  CAS  PubMed  Google Scholar 

  26. Lam CW, Law CY, Sze KH, To KK (2015) Quantitative metabolomics of urine for rapid etiological diagnosis of urinary tract infection: evaluation of a microbial-mammalian co-metabolite as a diagnostic biomarker. Clin Chim Acta 438:24–28

    Article  CAS  PubMed  Google Scholar 

  27. Gupta A, Dwivedi M, Mahdi AA, Khetrapal CL, Bhandari M (2012) Broad identification of bacterial type in urinary tract infection using (1)h NMR spectroscopy. J Proteome Res 11:1844–1854

    Article  CAS  PubMed  Google Scholar 

  28. Gupta A, Dwivedi M, Mahdi AA, Gowda GA, Khetrapal CL, Bhandari M (2009) 1H-nuclear magnetic resonance spectroscopy for identifying and quantifying common uropathogens: a metabolic approach to the urinary tract infection. BJU Int 104:236–244

    Article  CAS  PubMed  Google Scholar 

  29. Nevedomskaya E, Pacchiarotta T, Artemov A et al (2012) (1)H NMR-based metabolic profiling of urinary tract infection: combining multiple statistical models and clinical data. Metabolomics 8:1227–1235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Coen M, O'Sullivan M, Bubb WA, Kuchel PW, Sorrell T (2005) Proton nuclear magnetic resonance-based metabonomics for rapid diagnosis of meningitis and ventriculitis. Clin Infect Dis 41:1582–1590

    Article  CAS  PubMed  Google Scholar 

  31. Su L, Huang Y, Zhu Y et al (2014) Discrimination of sepsis stage metabolic profiles with an LC/MS-MS-based metabolomics approach. BMJ Open Respir Res 1:e000056

    Article  PubMed  PubMed Central  Google Scholar 

  32. Langley RJ, Tsalik EL, van Velkinburgh JC et al (2013) An integrated clinico-metabolomic model improves prediction of death in sepsis. Sci Transl Med 5:195ra195

    Article  Google Scholar 

  33. Mickiewicz B, Duggan GE, Winston BW, Doig C, Kubes P, Vogel HJ (2014) Metabolic profiling of serum samples by 1H nuclear magnetic resonance spectroscopy as a potential diagnostic approach for septic shock. Crit Care Med 42:1140–1149

    Article  CAS  PubMed  Google Scholar 

  34. Schmerler D, Neugebauer S, Ludewig K, Bremer-Streck S, Brunkhorst FM, Kiehntopf M (2012) Targeted metabolomics for discrimination of systemic inflammatory disorders in critically ill patients. J Lipid Res 53:1369–1375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Fanos V, Caboni P, Corsello G et al (2014) Urinary (1)H-NMR and GC-MS metabolomics predicts early and late onset neonatal sepsis. Early Hum Dev 90(Suppl 1):S78–S83

    Article  CAS  PubMed  Google Scholar 

  36. Mickiewicz B, Vogel HJ, Wong HR, Winston BW (2013) Metabolomics as a novel approach for early diagnosis of pediatric septic shock and its mortality. Am J Respir Crit Care Med 187:967–976

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Dong F, Wang B, Zhang L, Tang H, Li J, Wang Y (2012) Metabolic response to Klebsiella pneumoniae infection in an experimental rat model. PloS One 7:e51060

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Slupsky CM, Cheypesh A, Chao DV et al (2009) Streptococcus pneumoniae and Staphylococcus aureus pneumonia induce distinct metabolic responses. J Proteome Res 8:3029–3036

    Article  CAS  PubMed  Google Scholar 

  39. Laiakis EC, Morris GA, Fornace AJ, Howie SR (2010) Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study. PloS One 9:5

    Google Scholar 

  40. Slupsky CM, Rankin KN, Fu H et al (2009) Pneumococcal pneumonia: potential for diagnosis through a urinary metabolic profile. J Proteome Res 8:5550–5558

    Article  CAS  PubMed  Google Scholar 

  41. Seymour CW, Yende S, Scott MJ et al (2013) Metabolomics in pneumonia and sepsis: an analysis of the GenIMS cohort study. Intensive Care Med 39:1423–1434

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Blaise BJ, Gouel-Cheron A, Floccard B, Monneret G, Allaouchiche B (2013) Metabolic phenotyping of traumatized patients reveals a susceptibility to sepsis. Anal Chem 85:10850–10855

    Article  CAS  PubMed  Google Scholar 

  43. Rogers AJ, McGeachie M, Baron RM et al (2014) Metabolomic derangements are associated with mortality in critically ill adult patients. PloS One 9:e87538

    Article  PubMed  PubMed Central  Google Scholar 

  44. Evans CR, Karnovsky A, Kovach MA, Standiford TJ, Burant CF, Stringer KA (2014) Untargeted LC-MS metabolomics of bronchoalveolar lavage fluid differentiates acute respiratory distress syndrome from health. J Proteome Res 13:640–649

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Section of Anaesthetics, Pain Medicine & Intensive Care, Department of Surgery & Cancer, Charing Cross Hospital / Imperial College London, London, UK

    D. Antcliffe & A. C. Gordon

Authors
  1. D. Antcliffe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. C. Gordon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. C. Gordon .

Editor information

Editors and Affiliations

  1. Université libre de Bruxelles Dept. of Intensive Care, Erasme Hospital, Brussels, Belgium

    Jean-Louis Vincent

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Antcliffe, D., Gordon, A.C. (2016). Metabonomics and Intensive Care. In: Vincent, JL. (eds) Annual Update in Intensive Care and Emergency Medicine 2016. Annual Update in Intensive Care and Emergency Medicine. Springer, Cham. https://doi.org/10.1007/978-3-319-27349-5_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27349-5_28

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27348-8

  • Online ISBN: 978-3-319-27349-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation