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Multivariate classification analysis of metabolomic data for candidate biomarker discovery in type 2 diabetes mellitus

Abstract

Recent advances in genomics, metabolomics and proteomics have made it possible to interrogate disease pathophysiology and drug response on a systems level. The analysis and interpretation of the complex data obtained using these techniques is potentially fertile but equally challenging. We conducted a small clinical trial to explore the application of metabolomics data in candidate biomarker discovery. Specifically, serum and urine samples from patients with type 2 diabetes mellitus (T2DM) were profiled on metabolomics platforms before and after 8 weeks of treatment with one of three commonly used oral antidiabetic agents, the sulfonyurea glyburide, the biguanide metformin, or the thiazolidinedione rosiglitazone. Multivariate classification techniques were used to detect serum or urine analytes, obtained at baseline (pre-treatment) that could predict a significant treatment response after 8 weeks. Using this approach, we identified three analytes, measured at baseline, that were associated with response to a thiazolidinedione after 8 weeks of treatment. Although larger and longer-term studies are required to validate any of the candidate biomarkers, pharmacometabolomic profiling, in combination with multivariate classification, is worthy of further exploration as an adjunct to clinical decision making regarding treatment selection and for patient stratification within clinical trials.

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Acknowledgements

We thank Lipomics Technologies Inc. for generating fatty acid data, the Netherlands Organisation for Applied Scientific Research (TNO) for serum NMR, BG Medicine Inc. for polar and lipid metabolite LC and GC MS and Drs Brian C Sweatman, Rachel Ball, Azmina Mather and Baljit Sall for acquisition of urine NMR data. We would also like to thank Dr. Chris Keefer, James Robert, Robert Vermeulen and Nikheel Kolatkar for valuable review of this manuscript.

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Correspondence to Yang Qiu.

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Qiu, Y., Rajagopalan, D., Connor, S.C. et al. Multivariate classification analysis of metabolomic data for candidate biomarker discovery in type 2 diabetes mellitus. Metabolomics 4, 337 (2008). https://doi.org/10.1007/s11306-008-0123-5

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Keywords

  • Classification
  • Biomarker
  • Metabolomics
  • Pharmacometabolomics
  • Metabonomics
  • NMR