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Analyzing metabolomics-based challenge tests

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Abstract

Challenge tests are used to assess the resilience of human beings to perturbations by analyzing responses to detect functional abnormalities. Well known examples are allergy tests and glucose tolerance tests. Increasingly, metabolomics analysis of blood or serum samples is used to analyze the biological response of the individual to these challenges. The information content of such metabolomics challenge test data involves both the disturbance and restoration of homeostasis on a metabolic level and is thus inherently different from the analysis of steady state data. It opens doors to study the variation of resilience between individuals beyond the classical biomarkers; preferably in terms of underlying biological processes. We review challenge tests in which metabolomics was used to analyze the biological response. Specifically, we describe strategies to perform statistical analyses on the responses and we will show some examples of these strategies applied to a postprandial challenge that was used to study a diet with anti-inflammatory properties. Finally we discuss open issues and give recommendation for further research.

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References

  • (2009). What is health? The ability to adapt. Lancet, 373(9666):781

  • (2011). Standards of medical care in diabetes—2011. Diabetes Care, 34(Suppl 1), S11–S61.

  • Anderson, T. (2003). An introduction to multivariate statistical analysis. New York: Wiley.

    Google Scholar 

  • Arbes, J. S. J., Gergen, P. J., Elliott, L., & Zeldin, D. C. (2005). Prevalences of positive skin test responses to 10 common allergens in the US population: Results from the third National Health and Nutrition Examination Survey. Journal of Allergy and Clinical Immunology, 116(2), 377–383.

    Article  PubMed  Google Scholar 

  • Bakker, G. C., van Erk, M. J., Pellis, L., Wopereis, S., Rubingh, C. M., Cnubben, N. H., et al. (2010). An antiinflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: A nutrigenomics approach. American Journal of Clinical Nutrition, 91(4), 1044–1059.

    Article  CAS  PubMed  Google Scholar 

  • Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17(3), 166–173.

    Article  CAS  Google Scholar 

  • Bennett, S. M. A., Agrawal, A., Elasha, H., Heise, M., Jones, N. P., Walker, M., et al. (2004). Rosiglitazone improves insulin sensitivity, glucose tolerance and ambulatory blood pressure in subjects with impaired glucose tolerance. Diabetic Medicine, 21(5), 415–422.

    Article  CAS  PubMed  Google Scholar 

  • Bergman, R. N., Ider, Y. Z., Bowden, C. R., & Cobelli, C. (1979). Quantitative estimation of insulin sensitivity. American Journal of Physiology, 236(6), E667–E677.

    CAS  PubMed  Google Scholar 

  • Bondia-Pons, I., Nordlund, E., Mattila, I., Katina, K., Aura, A. M., Kolehmainen, M., et al. (2011). Postprandial differences in the plasma metabolome of healthy Finnish subjects after intake of a sourdough fermented endosperm rye bread versus white wheat bread. Nutrition Journal, 10, 116.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Boutayeb, A., & Chetouani, A. (2006). A critical review of mathematical models and data used in diabetology. Biomedical Engineering Online, 5(1), 43.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Bouwman, J., Vogels, J. T. W. E., Wopereis, S., Rubingh, C. M., Bijlsma, S., & van Ommen, B. (2012). Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes. BMC Medical Genomics, 5, 1.

    Article  PubMed Central  PubMed  Google Scholar 

  • Bro, R. (1998). Multi-way analysis in the food industry. PhD thesis, University of Amsterdam, Amsterdam.

  • Canguilhem, G. (1991). The normal and the pathological. London: MIT Press.

    Google Scholar 

  • Casella, G. (2008). Statistical design. New York: Springer.

    Book  Google Scholar 

  • Cavalieri, D., & De Filippo, C. (2005). Bioinformatic methods for integrating whole-genome expression results into cellular networks. Drug Discovery Today, 10(10), 727–734.

    Article  CAS  PubMed  Google Scholar 

  • Curtis, R. K., Oresic, M., & Vidal-Puig, A. (2005). Pathways to the analysis of microarray data. Trends in Biotechnology, 23(8), 429–435.

    Article  CAS  PubMed  Google Scholar 

  • Davidian, M., & Giltinan, D. M. (2003). Nonlinear models for repeated measurement data: An overview and update. Journal of Agricultural, Biological, and Environmental Statistics, 8(4), 387–419.

    Article  Google Scholar 

  • de Graaf, A. A., Freidig, A. P., De Roos, B., Jamshidi, N., Heinemann, M., Rullmann, J. A., et al. (2009). Nutritional systems biology modeling: From molecular mechanisms to physiology. PLOS Computational Biology, 5(11), e1000554.

    Article  PubMed Central  PubMed  Google Scholar 

  • de la Fuente, A., Bing, N., Hoeschele, I., & Mendes, P. (2004). Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics, 20(18), 3565–3574.

    Article  PubMed  Google Scholar 

  • Dillon, W. R. G. M. (1984). Multivariate analysis. New York: Wiley.

    Google Scholar 

  • Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., et al. (2007). Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proceedings of the National Academy of Sciences of the United States of America, 104(6), 1777–1782.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Elliott, R., Pico, C., Dommels, Y., Wybranska, I., Hesketh, J., & Keijer, J. (2007). Nutrigenomic approaches for benefit-risk analysis of foods and food components: Defining markers of health. British Journal of Nutrition, 98, 1095–1100.

    Article  CAS  PubMed  Google Scholar 

  • Gille, C., Bolling, C., Hoppe, A., Bulik, S., Hoffmann, S., Hubner, K., et al. (2010). HepatoNet1: A comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Molecular Systems Biology, 6, 411.

    Article  PubMed Central  PubMed  Google Scholar 

  • Heikkila, H. J. (1999). New models for pharmacokinetic data based on a generalized Weibull distribution. Journal of Biopharmaceutical Statistics, 9(1), 89–107.

    Article  CAS  PubMed  Google Scholar 

  • Ho, J., Larson, M., Vasan, R., Ghorbani, A., Cheng, S., Rhee, E., et al. (2013). Metabolite profiles during oral glucose challenge. Diabetes, 62(8), 2689–2698.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Hodgson, A., Randell, R., Boon, N., Garczarek, U., Mela, D., Jeukendrup, A., et al. (2013). Metabolic response to green tea extract during rest and moderate-intensity exercise. Journal of Nutritional Biochemistry, 24, 325–334.

    Article  CAS  PubMed  Google Scholar 

  • Hoefsloot, H. C. J., Smit, S., & Smilde, A. K. (2008). A classification model for the Leiden proteomics competition. Statistical Applications in Genetics and Molecular Biology, 7(2):Article8.

  • Huber, M., Knottnerus, J. A., Green, L., van der Horst, H., Jadad, A. R., Kromhout, D., et al. (2011). How should we define health? British Medical Journal, 343, d4163.

    Article  PubMed  Google Scholar 

  • Huisinga, W., Solms, A., Fronton, L., & Pilari, S. (2012). Modeling interindividual variability in physiologically based pharmacokinetics and its link to mechanistic covariate modeling. CPT: Pharmacometrics & Systems Pharmacology, 1, e4.

    CAS  Google Scholar 

  • Jansen, J. J., Hoefsloot, H. C. J., van der Greef, J., Timmerman, M. E., Westerhuis, J. A., & Smilde, A. K. (2005). ASCA: Analysis of multivariate data obtained from an experimental design. Journal of Chemometrics, 19, 469–481.

    Article  CAS  Google Scholar 

  • Jawetz, E., & Meyer, K. F. (1943). Avirulent strains of Pasteurella pestis. The Journal of Infectious Diseases, 73(2), 124–143.

    Article  Google Scholar 

  • Jolliffe, I. T. (1986). Principal component analysis. Berlin: Springer.

    Book  Google Scholar 

  • Krug, S., Kastenmüller, G., Stückler, F., Rist, M. J., Skurk, T., Sailer, M., et al. (2012). The dynamic range of the human metabolome revealed by challenges. FASEB Journal, 26(6), 2607–2619.

    Article  CAS  PubMed  Google Scholar 

  • Lee, D. K. C., Haggart, K., & Lipworth, B. J. (2004). Reproducibility of response to nasal lysine-aspirin challenge in patients with aspirin-induced asthma. The Annals of Allergy, Asthma & Immunology, 93(2), 185–188.

    Article  Google Scholar 

  • Lehtonen, H. M., Lindstedt, A., Jarvinen, R., Sinkkonen, J., Graca, G., Viitanen, M., et al. (2013). H-1 NMR-based metabolic fingerprinting of urine metabolites after consumption of lingonberries (Vaccinium vitis-idaea) with a high-fat meal. Food Chemistry, 138, 982–990.

    Article  CAS  PubMed  Google Scholar 

  • Lin, S. H., Yang, Z., Liu, H. D., Tang, L. H., & Cai, Z. W. (2011). Beyond glucose: Metabolic shifts in responses to the effects of the oral glucose tolerance test and the high-fructose diet in rats. Molecular Biosystems, 7, 1537–1548.

    Article  CAS  PubMed  Google Scholar 

  • Lindstrom, M. L., & Bates, D. M. (1990). Nonlinear mixed effects models for repeated measures data. Biometrics, 46(3), 673–687.

    Article  CAS  PubMed  Google Scholar 

  • Liu, X. D., Xie, L., Han, K. Q., & Liu, G. Q. (1996). Weibull function fits to pharmacokinetic data of ribavirin in man. The European Journal of Drug Metabolism and Pharmacokinetics, 21(3), 227–231.

    Article  CAS  Google Scholar 

  • Makroglou, A., Li, J., & Kuang, Y. (2006). Mathematical models and software tools for the glucose-insulin regulatory system and diabetes: An overview. Applied Numerical Mathematics, 56(3–4), 559–573.

    Article  Google Scholar 

  • Matysik, S., Martin, J., Bala, M., Scherer, M., Schaffler, A., & Schmitz, G. (2011). Bile acid signaling after an oral glucose tolerance test. Chemistry and Physics of Lipids, 164, 525–529.

    Article  CAS  PubMed  Google Scholar 

  • Miyazaki, Y., He, H., Mandarino, L. J., & DeFronzo, R. A. (2003). Rosiglitazone improves downstream insulin receptor signaling in type 2 diabetic patients. Diabetes, 52(8), 1943–1950.

    Article  CAS  PubMed  Google Scholar 

  • Nam, D., & Kim, S.-Y. (2008). Gene-set approach for expression pattern analysis. Briefings in Bioinformatics, 9(3), 189–197.

    Article  PubMed  Google Scholar 

  • Nieman, D. C., Gillitt, N. D., Henson, D. A., Sha, W., Shanely, R. A., Knab, A. M., et al. (2012). Bananas as an energy source during exercise: A metabolomics approach. Plos One, 7, e37479.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Peeters, K. A. B. M., Lamers, R.-J. A. N., Penninks, A. H., Knol, E. F., Bruijnzeel-Koomen, C. A. F. M., van Nesselrooij, J. H. J., et al. (2011). A search for biomarkers as diagnostic tools for food allergy: A pilot study in peanut-allergic patients. International Archives of Allergy and Immunology, 155(1), 23–30.

    Article  CAS  PubMed  Google Scholar 

  • Pellis, L., van Erk, M. J., van Ommen, B., Bakker, G. C. M., Hendriks, H. F. J., Cnubben, N. H. P., et al. (2012). Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status. Metabolomics, 8(2), 347–359.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Piotrovskii, V. K. (1987). Pharmacokinetic stochastic model with Weibull-distributed residence times of drug molecules in the body. The European Journal of Clinical Pharmacology, 32(5), 515–523.

    Article  CAS  Google Scholar 

  • Rhee, E. P., Cheng, S., Larson, M. G., Walford, G. A., Lewis, G. D., McCabe, E., et al. (2011). Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. Journal of Clinical Investigation, 121, 1402–1411.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Rodriguez, L., Roberts, L. D., Larosa, J., Heinz, N., Gerszten, R., Nurko, S., et al. (2013). Relationship between postprandial metabolomics and colon motility in children with constipation. Neurogastroenterology and Motility, 25, 420–426.

    Article  CAS  PubMed  Google Scholar 

  • Roy, A., & Parker, R. S. (2007). Dynamic modeling of exercise effects on plasma glucose and insulin levels. The Journal of Diabetes Science and Technology, 1(3), 338–347.

    Article  Google Scholar 

  • Rubingh, C. M., van Erk, M. J., Wopereis, S., van Vliet, T., Verheij, E. R., Cnubben, N. H. P., et al. (2011). Discovery of subtle effects in a human intervention trial through multilevel modeling. Chemometrics and Intelligent Laboratory Systems, 106(1), 108–114.

    Article  CAS  Google Scholar 

  • Rubio-Aliaga, I., de Roos, B., Duthie, S. J., Crosley, L. K., Mayer, C., Horgan, G., et al. (2011). Metabolomics of prolonged fasting in humans reveals new catabolic markers. Metabolomics, 7, 375–387.

    Article  CAS  Google Scholar 

  • Schnackenberg, L. K., Sun, J., & Beger, R. D. (2009). Metabolomics in systems toxicology: Towards personalized medicine. John Wiley and Sons Ltd.

  • Searle, S. R. (1971). Linear models. New York: Wiley.

    Google Scholar 

  • Shaham, O., Wei, R., Wang, T. J., Ricciardi, C., Lewis, G. D., Vasan, R. S., et al. (2008). Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Molecular Systems Biology, 4, 214.

    Article  PubMed Central  PubMed  Google Scholar 

  • Sheiner, L. B., & Ludden, T. M. (1992). Population pharmacokinetics/dynamics. The Annual Review of Pharmacology and Toxicology, 32, 185–209.

    Article  CAS  Google Scholar 

  • Skurk, T., Rubio-Aliaga, I., Stamfort, A., Hauner, H., & Daniel, H. (2011). New metabolic interdependencies revealed by plasma metabolite profiling after two dietary challenges. Metabolomics, 7, 388–399.

    Article  CAS  Google Scholar 

  • Smilde, A. K., Bro, R., & Geladi, P. (2004). Multi-way analysis. Applications in the chemical sciences. Chichester: Wiley.

    Book  Google Scholar 

  • Smilde, A. K., Jansen, J. J., Hoefsloot, H. C. J., Lamers, R.-J. A. N., van der Greef, J., & Timmerman, M. E. (2005). ANOVA-simultaneous component analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics, 21(13), 3043–3048.

    Article  CAS  PubMed  Google Scholar 

  • Spégel, P., Danielsson, A., Bacos, K., Nagorny, C., Moritz, T., Mulder, H., et al. (2010). Metabolomic analysis of a human oral glucose tolerance test reveals fatty acids as reliable indicators of regulated metabolism. Metabolomics, 6, 56–66. doi:10.1007/s11306-009-0177-z.

    Article  Google Scholar 

  • Thompson, G. A., & Toothaker, R. D. (2004). Urinary excretion: Does it accurately reflect relative differences in bioavailability/systemic exposure when renal clearance is nonlinear? Pharmaceutical Research, 21(5), 781–784.

    Article  CAS  PubMed  Google Scholar 

  • Van Batenburg, M. F., Coulier, L., van Eeuwijk, F., Smilde, A. K., & Westerhuis, J. A. (2011). New figures of merit for comprehensive functional genomics data: The metabolomics case. Analytical Chemistry, 83(9), 3267–3274.

    Article  PubMed  Google Scholar 

  • van der Greef, J., Hankemeier, T., & McBurney, R. N. (2006). Metabolomics-based systems biology and personalized medicine: Moving towards n = 1 clinical trials? Pharmacogenomics, 7(7), 1087–1094.

    Article  PubMed  Google Scholar 

  • van Ommen, B., Keijer, J., Kleemann, R., Elliott, R., Drevon, C. A., McArdle, H., et al. (2008). The challenges for molecular nutrition research 2: Quantification of the nutritional phenotype. Genes and Nutrition, 3, 51–59.

    Article  PubMed Central  PubMed  Google Scholar 

  • Verbeke, G. & Molenberghs, G. (2009). Linear mixed models for longitudinal data. Springer series in statistics. Springer, New York. Includes bibliographical references (p. [523]-553) and index.

  • Vis, D. J., Westerhuis, J. A., Smilde, A. K., & van der Greef, J. (2007). Statistical validation of megavariate effects in ASCA. BMC Bioinformatics, 8, 322.

    Article  PubMed Central  PubMed  Google Scholar 

  • Wang, C., Lv, L., Yang, Y., Chen, D., Liu, G., Chen, L., et al. (2011). Glucose fluctuations in subjects with normal glucose tolerance, impaired glucose regulation and newly-diagnosed Type 2 diabetes mellitus. Clinical Endocrinology (Oxford), 76(6), 810–815.

    Article  Google Scholar 

  • Westerhuis, J. A., Derks, E. P. P. A., Hoefsloot, H. C. J., & Smilde, A. K. (2007). Grey component analysis. Journal of Chemometrics, 21(10–11), 474–485.

    Article  CAS  Google Scholar 

  • WHO. (2006). Constitution of the World Health Organization.

  • Wopereis, S., Rubingh, C. M., van Erk, M. J., Verheij, E. R., van Vliet, T., Cnubben, N. H. P., et al. (2009). Metabolic profiling of the response to an oral glucose tolerance test detects subtle metabolic changes. PLoS One, 4(2), e4525.

    Article  PubMed Central  PubMed  Google Scholar 

  • Xia, J. G., Mandal, R., Sinelnikov, I. V., Broadhurst, D., & Wishart, D. S. (2012). MetaboAnalyst 2.0-a comprehensive server for metabolomic data analysis. Nucleic Acids Research, 40(W1), W127–W133.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Xiong, H., & Choe, Y. (2008). Dynamical pathway analysis. BMC Systems Biology, 2, 9.

    Article  PubMed Central  PubMed  Google Scholar 

  • Zhao, X., Peter, A., Fritsche, J., Elcnerova, M., Fritsche, A., Häring, H.-U., et al. (2009). Changes of the plasma metabolome during an oral glucose tolerance test: Is there more than glucose to look at? The American Journal of Physiology - Endocrinology and Metabolism, 296(2), E384–E393.

    Article  CAS  Google Scholar 

  • Zivkovic, A. M., Wiest, M. M., Nguyen, U., Nording, M. L., Watkins, S. M., & German, J. B. (2009). Assessing individual metabolic responsiveness to a lipid challenge using a targeted metabolomic approach. Metabolomics, 5(2), 209–218.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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Acknowledgments

Gooitzen Zwanenburg is acknowledged for kindly providing the minimal glucose model results. The research was funded by the Netherlands Metabolomics Centre (NMC), which is a part of The Netherlands Genomics Initiative/Netherlands Organization for Scientific Research.

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Correspondence to Daniel J. Vis.

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Margriet M. W. B. Hendriks and Age K. Smilde have equally contributed to this study.

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Vis, D.J., Westerhuis, J.A., Jacobs, D.M. et al. Analyzing metabolomics-based challenge tests. Metabolomics 11, 50–63 (2015). https://doi.org/10.1007/s11306-014-0673-7

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