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.
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.
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.
Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17(3), 166–173.
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.
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.
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.
Boutayeb, A., & Chetouani, A. (2006). A critical review of mathematical models and data used in diabetology. Biomedical Engineering Online, 5(1), 43.
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.
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.
Casella, G. (2008). Statistical design. New York: Springer.
Cavalieri, D., & De Filippo, C. (2005). Bioinformatic methods for integrating whole-genome expression results into cellular networks. Drug Discovery Today, 10(10), 727–734.
Curtis, R. K., Oresic, M., & Vidal-Puig, A. (2005). Pathways to the analysis of microarray data. Trends in Biotechnology, 23(8), 429–435.
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.
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.
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.
Dillon, W. R. G. M. (1984). Multivariate analysis. New York: Wiley.
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.
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.
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.
Heikkila, H. J. (1999). New models for pharmacokinetic data based on a generalized Weibull distribution. Journal of Biopharmaceutical Statistics, 9(1), 89–107.
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.
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.
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.
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.
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.
Jawetz, E., & Meyer, K. F. (1943). Avirulent strains of Pasteurella pestis. The Journal of Infectious Diseases, 73(2), 124–143.
Jolliffe, I. T. (1986). Principal component analysis. Berlin: Springer.
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.
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.
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.
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.
Lindstrom, M. L., & Bates, D. M. (1990). Nonlinear mixed effects models for repeated measures data. Biometrics, 46(3), 673–687.
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.
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.
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.
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.
Nam, D., & Kim, S.-Y. (2008). Gene-set approach for expression pattern analysis. Briefings in Bioinformatics, 9(3), 189–197.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Sheiner, L. B., & Ludden, T. M. (1992). Population pharmacokinetics/dynamics. The Annual Review of Pharmacology and Toxicology, 32, 185–209.
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.
Smilde, A. K., Bro, R., & Geladi, P. (2004). Multi-way analysis. Applications in the chemical sciences. Chichester: Wiley.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Xiong, H., & Choe, Y. (2008). Dynamical pathway analysis. BMC Systems Biology, 2, 9.
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.
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.
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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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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DOI: https://doi.org/10.1007/s11306-014-0673-7