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

Metabolomics volume 7, pages 25–34 (2011)Cite this 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.

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References

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Bidlingmeyer, B. A., Cohen, S. A., & Tarvin, T. L. (1984). Rapid analysis of amino acids using pre-column derivatization. J Chromatography, 336, 93–104.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Cover, T. M., & Thomas, J. A. (2006). Elements of information theory. Hoboken, NJ: Wiley-Interscience.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Eastman, T. (2010). A disease classifier for metabolic profiles based on metabolic pathway knowledge. MSc Thesis, University of Alberta.

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131–163.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Pesarin, F. (2001). Multivariate permutation tests: With applications in biostatistics. Chichester, New York: J. Wiley.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann Publishers.

    Google Scholar 

  • Ross, R. (2003). Advances in the application of imaging methods in applied and clinical physiology. Acta Diabetologica, 40(Suppl 1), S45–S50.

    Article  PubMed  Google Scholar 

  • Saude, E. J., & Sykes, B. D. (2007). Urine stability for metabolomic studies: Effects of preparation and storage. Metabolomics, 3, 19–27.

    Article  CAS  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Wagner, A., & Fell, D. A. (2001). The small world inside large metabolic networks. Proceedings of the Royal Society of Biological Science, 268, 1803–1810.

    Article  CAS  Google Scholar 

  • 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.

    CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Wishart, D. S. (2007). Current progress in computational metabolomics. Briefings in Bioinformatics, 8, 275–284.

    Article  Google Scholar 

  • Wishart, D. S. (2008). Quantitative metabolomics using NMR. Trends in Analytical Chemistry, 27, 228–237.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Boston, MA: Morgan Kaufman.

    Google Scholar 

Download references

Acknowledgments

The authors wish to thank the Alberta Cancer Foundation (ACF), the Cross Cancer Institute, the Alberta Ingenuity Fund (AIF), the Alberta Advanced Education and Technology (AAET), the Canadian Institutes for Health Research (CIHR) and the Alberta Ingenuity Centre for Machine Learning (AICML) for financial support.

Financial or material support

This work was supported by grants from the Alberta Cancer Board and Alberta Cancer Foundation, the Alberta Ingenuity Fund, the Alberta Ingenuity Centre for Machine Learning, the Natural Sciences and Engineering Research Council of Canada, and Genome Canada.

Author disclosures

R. Eisner, C. Stretch, T. Eastman, J. Xia, D. Hau, S. Damaraju, R. Greiner, D.S. Wishart and V.E. Baracos have no conflicts of interest.

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Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, AB, Canada

    Roman Eisner, Thomas Eastman, Russell Greiner & David S. Wishart

  2. Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada

    Jianguo Xia, David Hau & David S. Wishart

  3. Department of Oncology, Division of Palliative Care Medicine, University of Alberta, 11560 University Avenue, Edmonton, AB, T6G 1Z2, Canada

    Cynthia Stretch & Vickie E. Baracos

  4. Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada

    Sambasivarao Damaraju

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  1. Roman Eisner
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  2. Cynthia Stretch
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  3. Thomas Eastman
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  4. Jianguo Xia
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  6. Sambasivarao Damaraju
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  7. Russell Greiner
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  8. David S. Wishart
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  9. Vickie E. Baracos
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Correspondence to Vickie E. Baracos.

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Roman Eisner and Cynthia Stretch contributed equally to this work.

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Eisner, R., Stretch, C., Eastman, T. et al. Learning to predict cancer-associated skeletal muscle wasting from 1H-NMR profiles of urinary metabolites. Metabolomics 7, 25–34 (2011). https://doi.org/10.1007/s11306-010-0232-9

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  • DOI: https://doi.org/10.1007/s11306-010-0232-9

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