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Metabolomics

, Volume 6, Issue 1, pp 109–118 | Cite as

Urinary metabolomics as a potentially novel diagnostic and stratification tool for knee osteoarthritis

  • Xin Li
  • Songbing Yang
  • Yunping Qiu
  • Tie Zhao
  • Tianlu Chen
  • Mingming Su
  • Lixi Chu
  • Aiping Lv
  • Ping Liu
  • Wei JiaEmail author
Original Article

Abstract

Metabolomics has been used as a tool in disease diagnosis and phenotype prediction. A urinary metabolomic study based on GC–MS in combination with multivariate statistics was used here to classify between knee osteoarthritis (OA) and healthy controls. OPLS-DA of the spectral data showed distinct metabolic profile variations between OA patients and healthy controls and between two OA phenotypes. Differential metabolites reveal up-regulated TCA cycle associated with OA and histamine metabolism disorders accompanied with knee effusion symptoms. This metabolomic method is potentially applicable as a novel strategy for OA diagnosis and patient stratification.

Keywords

Metabolomics Osteoarthritis Phenotype Stratification Biomarker Histamine 

Abbreviations

BMI

Body mass index

COX-2

Cyclooxygenase 2

CS

Citrate synthase

GC–MS

Gas chromatography–mass spectrometry

HAC

Human articular chondrocytes

HDC

Histidine decarboxylase

KL

Kellgren–Lawrence

MCs

Mast cells

MRI

Magnetic resonance imaging

NMR

Nuclear magnetic resonance

NSAIDs

Nonsteroidal anti-inflammatory drugs

OA

Osteoarthritis

OPLS-DA

Orthogonal partial least squares projection to latent structure-discriminant analysis

PCA

Principle component analysis

TCA

Tricarboxylic acid

TIC

Total ion current

VIP

Variable importance in the projection

Notes

Acknowledgements

This work was mainly supported by research grant from a National Basic Research Program of China (Program 973, Project Number 2007CB914700) and Research Grant No. 2006DFA02700 and partly supported by E-institutes of Shanghai Municipal Education Commission, Project Number E03008. The authors would especially like to thank all the study participants who made this research possible.

References

  1. Akdis, C. A., & Blaser, K. (2003). Histamine in the immune regulation of allergic inflammation. Journal of Allergy and Clinical Immunology, 112(1), 15–22.CrossRefPubMedGoogle Scholar
  2. Altman, R., Asch, E., Bloch, D., Bole, G., Borenstein, D., Brandt, K., et al. (1986). Development of criteria for the classification and reporting of osteoarthritis. Classification of osteoarthritis of the knee. Diagnostic and therapeutic criteria committee of the American rheumatism association. Arthritis and Rheumatism, 29(8), 1039–1049.CrossRefPubMedGoogle Scholar
  3. Ardawi, M. S., & Newsholme, E. A. (1984). Metabolism of ketone bodies, oleate and glucose in lymphocytes of the rat. Biochemical Journal, 221(1), 255–260.PubMedGoogle Scholar
  4. Atherton, H. J., Jones, O. A., Malik, S., Miska, E. A., & Griffin, J. L. (2008). A comparative metabolomic study of NHR-49 in Caenorhabditis elegans and PPAR-alpha in the mouse. FEBS Letters, 582(12), 1661–1666.CrossRefPubMedGoogle Scholar
  5. Beger, R., Schnackenberg, L., Holland, R., Li, D., & Dragana, Y. (2006). Metabonomic models of human pancreatic cancer using 1D proton NMR spectra of lipids in plasma. Metabolomics, 2(3), 125–134.CrossRefGoogle Scholar
  6. Blanco, F. J., Lopez-Armada, M. J., & Maneiro, E. (2004). Mitochondrial dysfunction in osteoarthritis. Mitochondrion, 4(5–6), 715–728.CrossRefPubMedGoogle Scholar
  7. Brindle, J. T., Antti, H., Holmes, E., Tranter, G., Nicholson, J. K., Bethell, H. W., et al. (2002). Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nature Medicine, 8(12), 1439–1444.CrossRefPubMedGoogle Scholar
  8. Bylesjo, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006). OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20, 341–351.CrossRefGoogle Scholar
  9. Causton, D. R. (1987). A Biologist’s Advanced Mathematics. In U. Aa (Ed.), London.Google Scholar
  10. Damyanovich, A. Z., Staples, J. R., Chan, A. D., & Marshall, K. W. (1999). Comparative study of normal and osteoarthritic canine synovial fluid using 500 MHz 1H magnetic resonance spectroscopy. Journal of Orthopaedic Research, 17(2), 223–231.CrossRefPubMedGoogle Scholar
  11. Denkert, C., Budczies, J., Kind, T., Weichert, W., Tablack, P., Sehouli, J., et al. (2006). Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Research, 66(22), 10795–10804.CrossRefPubMedGoogle Scholar
  12. Dequeker, J. (1985). The relationship between osteoporosis and osteoarthritis. Clinics in Rheumatic Diseases, 11(2), 271–296.PubMedGoogle Scholar
  13. Dunn, W., Broadhurst, D., Deepak, S., Buch, M., McDowell, G., Spasic, I., et al. (2007). Serum metabolomics reveals many novel metabolic markers of heart failure, including pseudouridine and 2-oxoglutarate. Metabolomics, 3(4), 413–426CrossRefGoogle Scholar
  14. Dunn, W. B., & Ellis, D. I. (2005). Metabolomics: Current analytical platforms and methodologies. Trends in Analytical Chemistry, 24(4), 285–294.CrossRefGoogle Scholar
  15. Dvorak, A. M. (1998). Histamine content and secretion in basophils and mast cells. Progress in Histochemistry and Cytochemistry, 33(3–4), III–IX. 169-320.PubMedGoogle Scholar
  16. Ellis, D. I., Dunn, W. B., Griffin, J. L., Allwood, J. W., & Goodacre, R. (2007). Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics, 8(9), 1243–1266.CrossRefPubMedGoogle Scholar
  17. Garstang, S. V., & Stitik, T. P. (2006). Osteoarthritis: Epidemiology, risk factors, and pathophysiology. American Journal of Physical Medicine & Rehabilitation/Association of Academic Physiatrists, 85(11 Suppl), S2–11. quiz S12-14.Google Scholar
  18. Griffin, J. L., Mann, C. J., Scott, J., Shoulders, C. C., & Nicholson, J. K. (2001). Choline containing metabolites during cell transfection: An insight into magnetic resonance spectroscopy detectable changes. FEBS Letters, 509(2), 263–266.CrossRefPubMedGoogle Scholar
  19. Handley, C. J., Speight, G., Leyden, K. M., & Lowther, D. A. (1980). Extracellular matrix metabolism by chondrocytes. 7. Evidence that l-glutamine is an essential amino acid for chondrocytes and other connective tissue cells. Biochimica et Biophysica Acta, 627(3), 324–331.PubMedGoogle Scholar
  20. Hart, D. J., Mootoosamy, I., Doyle, D. V., & Spector, T. D. (1994). The relationship between osteoarthritis and osteoporosis in the general population: The Chingford Study. Annals of the Rheumatic Diseases, 53(3), 158–162.CrossRefPubMedGoogle Scholar
  21. Hinman, R. S., & Crossley, K. M. (2007). Patellofemoral joint osteoarthritis: An important subgroup of knee osteoarthritis. Rheumatology (Oxford, England), 46(7), 1057–1062.CrossRefGoogle Scholar
  22. Holmes, E., Tsang, T. M., Huang, J. T., Leweke, F. M., Koethe, D., Gerth, C. W., et al. (2006). Metabolic profiling of CSF: Evidence that early intervention may impact on disease progression and outcome in schizophrenia. PLoS Medicine, 3(8), e327.CrossRefPubMedGoogle Scholar
  23. Huber, M., Trattnig, S., & Lintner, F. (2000). Anatomy, biochemistry, and physiology of articular cartilage. Investigative Radiology, 35(10), 573–580.CrossRefPubMedGoogle Scholar
  24. Jolliffe, I. T. (1986). Principal component analysis. New York: Springer.Google Scholar
  25. Kellgren, J. H., & Lawrence, J. S. (1957). Radiological assessment of osteo-arthrosis. Annals of the Rheumatic Diseases, 16(4), 494–502.CrossRefPubMedGoogle Scholar
  26. Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621.CrossRefGoogle Scholar
  27. Lamers, R. J., van Nesselrooij, J. H., Kraus, V. B., Jordan, J. M., Renner, J. B., Dragomir, A. D., et al. (2005). Identification of an urinary metabolite profile associated with osteoarthritis. Osteoarthritis and Cartilage/OARS, Osteoarthritis Research Society, 13(9), 762–768.CrossRefPubMedGoogle Scholar
  28. Loeser, R. F. (2006). Molecular mechanisms of cartilage destruction: Mechanics, inflammatory mediators, and aging collide. Arthritis and Rheumatism, 54(5), 1357–1360.CrossRefPubMedGoogle Scholar
  29. Malone, D. G., Irani, A. M., Schwartz, L. B., Barrett, K. E., & Metcalfe, D. D. (1986). Mast cell numbers and histamine levels in synovial fluids from patients with diverse arthritides. Arthritis and Rheumatism, 29(8), 956–963.CrossRefPubMedGoogle Scholar
  30. Murphy, L., Schwartz, T. A., Helmick, C. G., Renner, J. B., Tudor, G., Koch, G., et al. (2008). Lifetime risk of symptomatic knee osteoarthritis. Arthritis and Rheumatism, 59(9), 1207–1213.CrossRefPubMedGoogle Scholar
  31. Ni, Y., Su, M., Qiu, Y., Chen, M., Liu, Y., Zhao, A., et al. (2007). Metabolic profiling using combined GC-MS and LC-MS provides a systems understanding of aristolochic acid-induced nephrotoxicity in rat. FEBS Letters, 581(4), 707–711.CrossRefPubMedGoogle Scholar
  32. Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). Metabonomics: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica; The Fate of Foreign Compounds in Biological Systems, 29(11), 1181–1189.PubMedGoogle Scholar
  33. Nuki, G. (1999). Osteoarthritis: A problem of joint failure. Zeitschrift fur Rheumatologie, 58(3), 142–147.CrossRefPubMedGoogle Scholar
  34. Pelletier, J. P., Martel-Pelletier, J., & Abramson, S. B. (2001). Osteoarthritis, an inflammatory disease: Potential implication for the selection of new therapeutic targets. Arthritis and Rheumatism, 44(6), 1237–1247.CrossRefPubMedGoogle Scholar
  35. Qiu, Y., Rajagopalan, D., Connor, S., Damian, D., Zhu, L., Handzel, A., et al. (2008). Multivariate classification analysis of metabolomic data for candidate biomarker discovery in type 2 diabetes mellitus. Metabolomics, 4(4), 337–346.CrossRefGoogle Scholar
  36. Qiu, Y., Su, M., Liu, Y., Chen, M., Gu, J., Zhang, J., et al. (2007). Application of ethyl chloroformate derivatization for gas chromatography-mass spectrometry based metabonomic profiling. Analytica Chimica Acta, 583(2), 277–283.CrossRefPubMedGoogle Scholar
  37. Rozen, S., Cudkowicz, M. E., Bogdanov, M., Matson, W. R., Kristal, B. S., Beecher, C., et al. (2005). Metabolomic analysis and signatures in motor neuron disease. Metabolomics, 1(2), 101–108.CrossRefPubMedGoogle Scholar
  38. Santini, M. T., Rainaldi, G., Romano, R., Ferrante, A., Clemente, S., Motta, A., et al. (2004). MG-63 human osteosarcoma cells grown in monolayer and as three-dimensional tumor spheroids present a different metabolic profile: A 1 H-NMR study. FEBS Letters, 557(1–3), 148–154.CrossRefPubMedGoogle Scholar
  39. Tetlow, L. C., & Woolley, D. E. (2003). Histamine stimulates the proliferation of human articular chondrocytes in vitro and is expressed by chondrocytes in osteoarthritic cartilage. Annals of the Rheumatic Diseases, 62(10), 991–994.CrossRefPubMedGoogle Scholar
  40. Tetlow, L. C., & Woolley, D. E. (2005). Histamine, histamine receptors (H1 and H2), and histidine decarboxylase expression by chondrocytes of osteoarthritic cartilage: An immunohistochemical study. Rheumatology International, 26(2), 173–178.CrossRefPubMedGoogle Scholar
  41. van Doorn, M., Vogels, J., Tas, A., van Hoogdalem, E. J., Burggraaf, J., Cohen, A., et al. (2007). Evaluation of metabolite profiles as biomarkers for the pharmacological effects of thiazolidinediones in type 2 diabetes mellitus patients and healthy volunteers. British Journal of Clinical Pharmacology, 63(5), 562–574.CrossRefPubMedGoogle Scholar
  42. Weljie, A. M., Dowlatabadi, R., Miller, B. J., Vogel, H. J., & Jirik, F. R. (2007). An inflammatory arthritis-associated metabolite biomarker pattern revealed by 1H NMR spectroscopy. Journal of Proteome Research, 6(9), 3456–3464.CrossRefPubMedGoogle Scholar
  43. Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2, 37–52.CrossRefGoogle Scholar
  44. Yi, L. Z., He, J., Liang, Y. Z., Yuan, D. L., & Chau, F. T. (2006). Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS-LDA. FEBS Letters, 580(30), 6837–6845.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Xin Li
    • 1
  • Songbing Yang
    • 2
  • Yunping Qiu
    • 1
  • Tie Zhao
    • 1
  • Tianlu Chen
    • 1
  • Mingming Su
    • 1
  • Lixi Chu
    • 3
  • Aiping Lv
    • 4
  • Ping Liu
    • 4
  • Wei Jia
    • 1
    • 5
    Email author
  1. 1.School of PharmacyShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.School of Acupuncture and ManipulationShanghai University of Traditional Chinese MedicineShanghaiPeople’s Republic of China
  3. 3.Department of Orthopaedics and TraumatologyYueyang Hospital of Integrated Chinese and Western Medicine Affiliated to Shanghai University of Traditional Chinese MedicineShanghaiPeople’s Republic of China
  4. 4.E-Institute of Chinese Traditional Internal Medicine, Shanghai Municipal Education CommissionShanghai University of Traditional Chinese MedicineShanghaiPeople’s Republic of China
  5. 5.Department of NutritionUniversity of North Carolina at GreensboroKannapolisUSA

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