Skip to main content
SpringerLink
Log in

Metabonomics and diabetes mellitus

  • Published:
Advances in Therapy Aims and scope Submit manuscript

Abstract

Diabetes mellitus is one of the most serious heterogeneous endocrinologic diseases; more than 90% of those given this diagnosis have type 2 diabetes mellitus, usually accompanied by macrovascular and microvascular complications. Because most cases include a 7- to 10-y latency period before disease onset, however, it is of vital importance that more powerful and practical approaches be developed for early diagnosis and improved prevention and treatment. Metabonomics is a powerful new approach that is especially useful for the treatment of patients with metabolic diseases because it can evaluate the holistic responses of the body to any subtle perturbation with the use of advanced analytical apparatus and chemometric methods that have been applied in the diagnosis and evaluation of type 2 diabetes mellitus. It is a promising approach to the study of complex diseases that should prove useful in the future, along with other -omics techniques performed in the context of systems biology.

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

Access this article

Log in via an institution

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Massimo M, Ettore B, Laura F, et al. Value and significance of new diagnostic criteria of diabetes mellitus in older people.Arch Gerontol Geriatr. 2007;45:103–108.

    Article  Google Scholar 

  2. Zimmet P, Alberti KG, Shaw J. Global and societal implications of the diabetes epidemic.Nature. 2001;414:782–787.

    Article  PubMed  CAS  Google Scholar 

  3. Ideker T, Thorsson V, Ranish JA, et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.Science. 2001;292:929–934.

    Article  PubMed  CAS  Google Scholar 

  4. Nicholson JK, Wilson ID. Opinion: understanding “global” systems biology: metabonomics and the continuum of metabolism.Nat Rev Drug Discov. 2003;2:668–676.

    Article  PubMed  CAS  Google Scholar 

  5. Mitchell SC, Carmichael PL. Metabonomics and the endocrine system.Mol Cell Endocrinol. 2005;244:10–14.

    Article  PubMed  CAS  Google Scholar 

  6. Lindo JC, Holmes E, Nicholson JK. Metabonomics techniques and applications to pharmaceutical research and development.Pharm Res. 2006;23:1075–1087.

    Article  Google Scholar 

  7. Yang J, Xu G, Hong Q, et al. Discrimination of type 2 diabetic patients from healthy controls by using metabonomics methods based on their serum fatty acid profiles.J Chromatogr B. 2004;813:53–58.

    Article  CAS  Google Scholar 

  8. van Doom M, Vogles J, Tas A, et al. Evaluation of metabolite profiles as biomarkers for the pharmacological effects of thiazolidinediones in type 2 diabetes mellitus patients and healthy volunteers.Br J Clin Pharmacol. 2007;63:562–574.

    Article  Google Scholar 

  9. Yi L, He J, Liang Y, et al. Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS-LDA.FEBS Lett. 2006;580:6837–6845.

    Article  PubMed  CAS  Google Scholar 

  10. Mantzoros CS. The role of leptin in human obesity and disease: a review of current evidence.Ann Intern Med. 1999;130:671–680.

    PubMed  CAS  Google Scholar 

  11. Reed MJ, Scribner KA. In-vivo and in-vitro models of type 2 diabetes in pharmaceutical drug discovery.Diabetes Obes Metab. 1999;l:75–86.

    Article  Google Scholar 

  12. Li H, Ni Y, Su M, et al. Pharmacometabonomic phenotyping reveals different responses to xenobiotic intervention in rats.J Proteome Res. 2007;6:1364–1370.

    Article  PubMed  CAS  Google Scholar 

  13. Salek RM, Maguire ML, Bentley E, et al. A metabolomic comparison of urinary changes in type 2 diabetes mellitus in mouse, rat and man.Physiol Genomics. 2007;29:99–108.

    PubMed  CAS  Google Scholar 

  14. Michael S, Hodavance SL. Beyond blood sugar: the potential of NMR-based metabonomics for type 2 human diabetes, and the horse as a possible model.Anal Bioanal Chem. 2007;387: 533–537.

    Article  Google Scholar 

  15. Liu M, Nicholson KJ, Lindon JC. High resolution diffusion and relaxation edited one- and- two-dimensional1H NMR spectroscopy of biological fluids.Anal Chem. 1996;8:3370–3376.

    Article  Google Scholar 

  16. Lindon JC, Holmes E, Nicholson JK. Metabonomics techniques and applications to pharmaceutical research and development.Pharm Res. 2006;6:1075–1087.

    Article  Google Scholar 

  17. Lindon JC, Nicholson JK, Holmes E, et al. Contemporary issue in toxicology, the role of metabonomics in toxicology and its evaluation by the COMET project.Toxicol Applied Pharm. 2003;187:137–146.

    Article  CAS  Google Scholar 

  18. Ebbles T, Keun H, Beckonert O, et al. Toxicity classification from metabonomic data using a density superposition approach: “CLOUDS.”Anal Chim Acta. 2003;490:109–122.

    Article  Google Scholar 

  19. Qiu Y, Su M, Liu Y, et al. Application of ethy chloroformate derivatization for gas chromatographymass spectrometry based metabonomic profiling.Anal Chim Acta. 2007;583:277–283.

    Article  PubMed  CAS  Google Scholar 

  20. Plumb RS, Stumpf CL, Gorenstein MV, et al. Metabonomics: the use of electrospray mass spectrometry coupled to reverse-phase liquid chromatography shows potential for screening of rat urine in drug development.Rapid Commun Mass Spectrum. 2002;16:1991–1996.

    Article  CAS  Google Scholar 

  21. Jiye A, Trygg J, Gullberg J, et al. Extraction and GC/MS analysis of the human blood plasma metabolome.Anal Chem. 2005;77:8086–8094.

    Article  Google Scholar 

  22. Wang C, Kong H, Guan Y, et al. Plasma phospholipid metabolic profiling and biomarkers of type 2 diabetes mellitus based on high-performance liquid chromatography/electrospray mass spectrometry and multivariate statistical analysis.Anal Chem. 2005;77:4108–4116.

    Article  PubMed  CAS  Google Scholar 

  23. Stoyanova A, Nicholls AW, Nicholson JK, et al. Automatic alignment of individual peaks in large high-resolution spectral data sets.J Magn Res. 2004;170:329–335.

    Article  CAS  Google Scholar 

  24. Ullsten S, Danielsson R, Backstrom D, et al. Urine profiling using capillary electrophoresismass spectrometry and multivariate data analysis.J Chromatogr A. 2006;1117:87–93.

    Article  PubMed  CAS  Google Scholar 

  25. Moller DE. New drug targets for type 2 diabetes and the metabolic syndrome.Nature. 2001;414:821–827.

    Article  PubMed  CAS  Google Scholar 

  26. Nikolsky Y, Nikolskaya T, Bugrim A. Biological networks and analysis of experimental data in drugs discovery.Drug Discov Today. 2005;10:653–662.

    Article  PubMed  CAS  Google Scholar 

  27. Heishi M, Ichihara J, Teramoto R, et al. Global gene expression analysis in liver of obese diabeticdb/db mice treated with metformin.Diabetologia. 2006;49:1647–1655.

    Article  PubMed  CAS  Google Scholar 

  28. Matsumura T, Suzuki T, Kada N. Differential serum proteomic analysis in a model of metabolic disease.Biochem Biophys Res Commun. 2006;351:965–971.

    Article  PubMed  CAS  Google Scholar 

  29. Dumas ME, Barton RH, Toye A, et al. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice.Proc Natl Acad Sci U S A. 2006;130:12511–12516.

    Article  Google Scholar 

  30. Fridman E, Pichersky E. Metabonomics, genomics, proteomics, and the identification of enzymes and their substrates and products.Curr Opin Plant Biol. 2005;8:242–248.

    Article  PubMed  CAS  Google Scholar 

  31. Kussmann M, Affolter M. OMICS-driven biomarker discovery in nutrition and health.J Biotech. 2006;124:758–787.

    Article  CAS  Google Scholar 

  32. Nebert DW, Vesell ES. Can personalized drug therapy be achieved? A closer look at pharmacometabonomics.TRENDS Pharm Sci. 2006;27:580–586.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Physiology, Qiqihar Medical University, P. R. of China

    Peng Chen

  2. Medicine Research Institute, Qiqihar Medical University, 249 Heping Road Qiqihar, 161042, P. R. of China

    Jicheng Liu

Authors
  1. Peng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jicheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jicheng Liu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, P., Liu, J. Metabonomics and diabetes mellitus. Adv Therapy 24, 1036–1045 (2007). https://doi.org/10.1007/BF02877709

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02877709

Keywords

Search

Navigation