Metabolomics

, Volume 7, Issue 1, pp 25–34 | Cite as

Learning to predict cancer-associated skeletal muscle wasting from 1H-NMR profiles of urinary metabolites

  • Roman Eisner
  • Cynthia Stretch
  • Thomas Eastman
  • Jianguo Xia
  • David Hau
  • Sambasivarao Damaraju
  • Russell Greiner
  • David S. Wishart
  • Vickie E. Baracos
Original Article

Abstract

Cancer-associated muscle wasting is associated with reduction in functional status, in response to treatment and in life expectancy. Methods currently used to assess muscle loss involve diagnostic imaging techniques such as computed tomography (CT), which are costly, inconvenient, invasive, time consuming and have limited ability to detect early or slowly evolving wasting. We present a novel approach using single time-point urinary metabolite profiles to determine whether a patient is experiencing muscle wasting. We analyzed 93 random urine samples from patients with cancer using 1H-NMR. Using two successive CT images we assessed their lumbar skeletal muscle area (cm2) to estimate the rate of muscle change (% loss or gain over time) for each patient. The average muscle change over time was −4.71%/100 days in the muscle-losing group and +3.91%/100 days in the comparator group. Bivariate statistics identified metabolites related with muscle loss, including constituents and metabolites of muscle (creatine, creatinine, 3-OH-isovalerate), amino acids (Leu, Ile, Val, Ala, Thr, Tyr, Gln, Ser) and intermediary metabolites. We also applied machine-learning techniques to identify patterns of urinary metabolites that identify which patients are likely to lose muscle mass. We evaluated the predictive performance of 8 machine-learning approaches using fivefold cross validation and permutation testing, and found that SVM provided the best generalization accuracy (82.2%). These results suggest that 1H-NMR analysis of a single random urine sample may be a fast, cheap, safe and inexpensive tool to screen and monitor muscle loss, and that useful classifiers for predicting related metabolic conditions are possible with the methodology presented.

Keywords

NMR Muscle wasting Cancer Urine Machine learning 

References

  1. Akcay, M. N., Akcay, G., Solak, S., Balik, A. A., & Aylu, B. (2001). The effect of growth hormone on 24-h urinary creatinine levels in burned patients. Burns, 27, 42–45.CrossRefPubMedGoogle Scholar
  2. Antoun, S., Baracos, V. E., Birdsell, L., Escudier, B., & Sawyer, M. B. (2010). Low body mass index and sarcopenia associated with dose-limiting toxicity of sorafenib in patients with renal cell carcinoma. Annals of Oncology, 21, 1594–1598.CrossRefPubMedGoogle Scholar
  3. Asp, M. L., Tian, M., Wendel, A. A., & Belury, M. A. (2009). Evidence for the contribution of insulin resistance to the development of cachexia in tumor-bearing mice. International Journal of Cancer, 126, 756–763.CrossRefGoogle Scholar
  4. Bertini, I., Calabro, A., De Carli, V., Luchinat, C., Nepi, S., Porfirio, B., et al. (2009). The metabonomic signature of celiac disease. J Proteome Research, 8, 170–177.CrossRefGoogle Scholar
  5. Bidlingmeyer, B. A., Cohen, S. A., & Tarvin, T. L. (1984). Rapid analysis of amino acids using pre-column derivatization. J Chromatography, 336, 93–104.CrossRefGoogle Scholar
  6. Bollard, M. E., Stanley, E. G., Lindon, J. C., Nicholson, J. K., & Holmes, E. (2005). NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR in Biomedicine, 18, 143–162.CrossRefPubMedGoogle Scholar
  7. Cover, T. M., & Thomas, J. A. (2006). Elements of information theory. Hoboken, NJ: Wiley-Interscience.Google Scholar
  8. Craig, A., Cloarec, O., Holmes, E., Nicholson, J. K., & Lindon, J. C. (2006). Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Analytical Chemistry, 78, 2262–2267.CrossRefPubMedGoogle Scholar
  9. Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H. (2006). Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical Chemistry, 78, 4281–4290.CrossRefPubMedGoogle Scholar
  10. Eastman, T. (2010). A disease classifier for metabolic profiles based on metabolic pathway knowledge. MSc Thesis, University of Alberta.Google Scholar
  11. Evans, W. J., Morley, J. E., Argiles, J., Bales, C., Baracos, V., Guttridge, D., et al. (2008). Cachexia: A new definition. Clinical Nutrition, 27, 793–799.CrossRefPubMedGoogle Scholar
  12. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131–163.CrossRefGoogle Scholar
  13. Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction: With 200 full-color illustrations. New York: Springer.Google Scholar
  14. Heymsfield, S. B., Wang, Z., Baumgartner, R. N., & Ross, R. (1997). Human body composition: Advances in models and methods. Annual Review of Nutrition, 17, 527–558.CrossRefPubMedGoogle Scholar
  15. Holmes, E., Foxall, P. J., Nicholson, J. K., Neild, G. H., Brown, S. M., Beddell, C. R., et al. (1994). Automatic data reduction and pattern recognition methods for analysis of 1H nuclear magnetic resonance spectra of human urine from normal and pathological states. Analytical Biochemistry, 220, 284–296.CrossRefPubMedGoogle Scholar
  16. Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., et al. (2008). KEGG for linking genomes to life and the environment. Nucleic Acids Research, 36, D480–D484.CrossRefPubMedGoogle Scholar
  17. Lieffers, J. R., Mourtzakis, M., Hall, K. D., Mccargar, L. J., Prado, C. M., & Baracos, V. E. (2009). A viscerally driven cachexia syndrome in patients with advanced colorectal cancer: Contributions of organ and tumor mass to whole-body energy demands. American Journal of Clinical Nutrition, 89, 1173–1179.CrossRefPubMedGoogle Scholar
  18. Mahadevan, S., Shah, S. L., Marrie, T. J., & Slupsky, C. M. (2008). Analysis of metabolomic data using support vector machines. Analytical Chemistry, 80, 7562–7570.CrossRefPubMedGoogle Scholar
  19. Mitsiopoulos, N., Baumgartner, R. N., Heymsfield, S. B., Lyons, W., Gallagher, D., & Ross, R. (1998). Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. Journal of Applied Physiology, 85, 115–122.PubMedGoogle Scholar
  20. Mourtzakis, M., Prado, C. M., Lieffers, J. R., Reiman, T., Mccargar, L. J., & Baracos, V. E. (2008). A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Applied Physiology, Nutrition and Metabolism, 33, 997–1006.CrossRefGoogle Scholar
  21. Pesarin, F. (2001). Multivariate permutation tests: With applications in biostatistics. Chichester, New York: J. Wiley.Google Scholar
  22. Prado, C. M., Baracos, V. E., Mccargar, L. J., Mourtzakis, M., Mulder, K. E., Reiman, T., et al. (2007). Body composition as an independent determinant of 5-fluorouracil-based chemotherapy toxicity. Clinical Cancer Research, 13, 3264–3268.CrossRefPubMedGoogle Scholar
  23. Prado, C. M., Lieffers, J. R., Mccargar, L. J., Reiman, T., Sawyer, M. B., Martin, L., et al. (2008). Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: A population-based study. Lancet Oncology, 9, 629–635.CrossRefPubMedGoogle Scholar
  24. Prado, C. M., Baracos, V. E., Mccargar, L. J., Reiman, T., Mourtzakis, M., Tonkin, K., et al. (2009). Sarcopenia as a determinant of chemotherapy toxicity and time to tumor progression in metastatic breast cancer patients receiving capecitabine treatment. Clinical Cancer Research, 15, 2920–2926.CrossRefPubMedGoogle Scholar
  25. Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann Publishers.Google Scholar
  26. Ross, R. (2003). Advances in the application of imaging methods in applied and clinical physiology. Acta Diabetologica, 40(Suppl 1), S45–S50.CrossRefPubMedGoogle Scholar
  27. Saude, E. J., & Sykes, B. D. (2007). Urine stability for metabolomic studies: Effects of preparation and storage. Metabolomics, 3, 19–27.CrossRefGoogle Scholar
  28. Shen, W., Punyanitya, M., Wang, Z., Gallagher, D., St-Onge, M. P., Albu, J., et al. (2004a). Total body skeletal muscle and adipose tissue volumes: Estimation from a single abdominal cross-sectional image. Journal of Applied Physiology, 97, 2333–2338.CrossRefPubMedGoogle Scholar
  29. Shen, W., Punyanitya, M., Wang, Z., Gallagher, D., St-Onge, M. P., Albu, J., et al. (2004b). Visceral adipose tissue: Relations between single-slice areas and total volume. American Journal of Clinical Nutrition, 80, 271–278.PubMedGoogle Scholar
  30. Slupsky, C. M., Rankin, K. N., Wagner, J., Fu, H., Chang, D., Weljie, A. M., et al. (2007). Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Analytical Chemistry, 79, 6995–7004.CrossRefPubMedGoogle Scholar
  31. Tan, B. H., Birdsell, L. A., Martin, L., Baracos, V. E., & Fearon, K. C. (2009). Sarcopenia in an overweight or obese patient is an adverse prognostic factor in pancreatic cancer. Clinical Cancer Research, 15(22), 6973–6979.CrossRefPubMedGoogle Scholar
  32. Wagner, A., & Fell, D. A. (2001). The small world inside large metabolic networks. Proceedings of the Royal Society of Biological Science, 268, 1803–1810.CrossRefGoogle Scholar
  33. Walsh, M. C., Brennan, L., Malthouse, J. P., Roche, H. M., & Gibney, M. J. (2006). Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans. American Journal of Clinical Nutrition, 84(3), 531–539.PubMedGoogle Scholar
  34. Wang, X., Hu, Z., Hu, J., Du, J., & Mitch, W. E. (2006). Insulin resistance accelerates muscle protein degradation: Activation of the ubiquitin-proteosome pathway by defects in muscle cell signaling. Endocrinology, 147, 4160–4168.CrossRefPubMedGoogle Scholar
  35. Weljie, A. M., Newton, J., Mercier, P., Carlson, E., & Slupsky, C. M. (2006). Targeted profiling: Quantitative analysis of 1H NMR metabolomics data. Analytical Chemistry, 78, 4430–4442.CrossRefPubMedGoogle Scholar
  36. Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., Vis, D. J., Smilde, A. K., Van Velzen, E. J. J., et al. (2008). Assessment of PLSDA cross validation. Metabolomics, 4, 81–89.CrossRefGoogle Scholar
  37. Wishart, D. S. (2007). Current progress in computational metabolomics. Briefings in Bioinformatics, 8, 275–284.CrossRefGoogle Scholar
  38. Wishart, D. S. (2008). Quantitative metabolomics using NMR. Trends in Analytical Chemistry, 27, 228–237.CrossRefGoogle Scholar
  39. Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., et al. (2007). HMDB: The Human Metabolome Database. Nucleic Acids Research, 35(Database issue), D521–D526.CrossRefPubMedGoogle Scholar
  40. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Boston, MA: Morgan Kaufman.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Roman Eisner
    • 1
  • Cynthia Stretch
    • 3
  • Thomas Eastman
    • 1
  • Jianguo Xia
    • 2
  • David Hau
    • 2
  • Sambasivarao Damaraju
    • 4
  • Russell Greiner
    • 1
  • David S. Wishart
    • 1
    • 2
  • Vickie E. Baracos
    • 3
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  2. 2.Department of Biological SciencesUniversity of AlbertaEdmontonCanada
  3. 3.Department of Oncology, Division of Palliative Care MedicineUniversity of AlbertaEdmontonCanada
  4. 4.Department of Laboratory Medicine and PathologyUniversity of AlbertaEdmontonCanada

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