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The dynamic range of the human metabolome revealed by challenges
Abstract
Metabolic challenge protocols, such as the oral glucose tolerance test, can uncover early alterations in metabolism preceding chronic diseases. Nevertheless, most metabolomics data accessible today reflect the fasting state. To analyze the dynamics of the human metabolome in response to environmental stimuli, we submitted 15 young healthy male volunteers to a highly controlled 4 d challenge protocol, including 36 h fasting, oral glucose and lipid tests, liquid test meals, physical exercise, and cold stress. Blood, urine, exhaled air, and breath condensate samples were analyzed on up to 56 time points by MS‐and NMR‐based methods, yielding 275 metabolic traits with a focus on lipids and amino acids. Here, we show that physiological challenges increased interindividual variation even in phenotypically similar volunteers, revealing metabotypes not observable in baseline metabolite profiles; volunteer‐specific metabolite concentrations were consistently reflected in various biofluids; and readouts from a systematic model of β‐oxidation (e.g. , acetylcarnitine/palmitylcarnitine ratio) showed significant and stronger associations with physiological parameters (e.g. , fat mass) than absolute metabolite concentrations, indicating that systematic models may aid in understanding individual challenge responses. Due to the multitude of analytical methods, challenges and sample types, our freely available metabolomics data set provides a unique reference for future metabolomics studies and for verification of systems biology models.—Krug, S., Kastenmüller, G., Stückler, F., Rist, M. J., Skurk, T., Sailer, M., Raffler, J., Römisch‐Margl, W., Adamski, J., Prehn, C., Frank, T., Engel, K‐H., Hofmann, T., Luy, B., Zimmermann, R., Moritz, F., Schmitt‐Kopplin, P., Krumsiek, J., Kremer, W., Huber, F., Oeh, U., Theis, F. J., Szymczak, W., Hauner, H., Suhre, K., Daniel, H. The dynamic range of the human metabolome revealed by challenges. FASEB J. 26, 2607‐2619 (2012). www.fasebj.org
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“This effect, however, may reflect mitochondrial metabolism under acute dietary challenge (48) rather than effects of long-term habitual diet as assessed in our study.”
From Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes (2015) · cites this paper as [48]
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Metabolite profiles and the risk of developing diabetes
Wang, T. J., Larson, M. G., Vasan, R. S., Cheng, S., Rhee, E. P., McCabe, E., Lewis, G. D., Fox, C. S., Jacques, P. F., Fernandez, C., et al. (2011) · Nature Medicine · doi:10.1038/nm.2307 · Cited by 3,143
Lindinger, W., Hansel, A., & Jordan, A. (1998) · International Journal of Mass Spectrometry and Ion Processes · doi:10.1016/s0168-1176(97)00281-4 · Cited by 1,766
Human metabolic individuality in biomedical and pharmaceutical research
CARDIoGRAM, Suhre, K., Shin, S.-Y., Petersen, A.-K., Mohney, R. P., Meredith, D., Wägele, B., Altmaier, E., Deloukas, P., Erdmann, J., et al. (2011) · Nature · doi:10.1038/nature10354 · Cited by 1,017
Self-citationTargeted Profiling: Quantitative Analysis of 1 H NMR Metabolomics Data
Weljie, A. M., Newton, J., Mercier, P., Carlson, E., & Slupsky, C. M. (2006) · Analytical Chemistry · doi:10.1021/ac060209g · Cited by 902
THE INCIDENCE OF ALKAPTONURIA : A STUDY IN CHEMICAL INDIVIDUALITY.
Garrod, A. (1902) · The Lancet · doi:10.1016/s0140-6736(01)41972-6 · Cited by 800
Metabolomics-based methods for early disease diagnostics
Gowda, G. A. N., Zhang, S., Gu, H., Asiago, V. M., Shanaiah, N., & Raftery, D. (2008) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.8.5.617 · Cited by 705
Gall, W. E., Beebe, K. D., Lawton, K. A., Adam, K., Mitchell, M. W., Nakhle, P. J., Ryals, J. A., Milburn, M. V., Nannipieri, M., Camastra, S., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010883 · Cited by 702
A genome-wide perspective of genetic variation in human metabolism
Illig, T., Gieger, C., Zhai, G., Römisch-Margl, W., Wang-Sattler, R., Prehn, C., Altmaier, E., Kastenmüller, G., Kato, B. S., Mewes, H.-W., et al. (2010) · Nature Genetics · doi:10.1038/ng.507 · Cited by 653
Metabolomics Reveals Metabolic Biomarkers of Crohn's Disease
Jansson, J. K., Willing, B. P., Lucio, M., Fekete, A., Dicksved, J., Halfvarson, J., Tysk, C., & Schmitt‐Kopplin, P. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006386 · Cited by 529
Control of mitochondrial β-oxidation flux
Eaton, S. D. (2002) · Progress in Lipid Research · doi:10.1016/s0163-7827(01)00024-8 · Cited by 420
Bartlett, K., & Eaton, S. D. (2004) · European Journal of Biochemistry · doi:10.1046/j.1432-1033.2003.03947.x · Cited by 411
Metabolomic profiling to develop blood biomarkers for Parkinson's disease
Bogdanov, M. B., Matson, W. R., Wang, L., Matson, T., Saunders-Pullman, R. J., Bressman, S., & Beal, M. F. (2008) · Brain · doi:10.1093/brain/awm304 · Cited by 402
Shaham, O., Wei, R., Wang, T. J., Ricciardi, C., Lewis, G. D., Vasan, R. S., Carr, S. A., Thadhani, R., Gerszten, R. E., & Mootha, V. K. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.50 · Cited by 399
Noland, R. C., Koves, T. R., Seiler, S. E., Lum, H., Lust, R. M., Ilkayeva, O., Stevens, R. D., Hegardt, F. G., & Muoio, D. M. (2009) · Journal of Biological Chemistry · doi:10.1074/jbc.m109.032888 · Cited by 317
Römisch‐Margl, W., Prehn, C. P., Bogumil, R., Röhring, C., Suhre, K., & Adamski, J. (2011) · Metabolomics · doi:10.1007/s11306-011-0293-4 · Cited by 292
Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits
Zhao, X., Fritsche, J., Wang, J., Chen, J., Rittig, K., Schmitt‐Kopplin, P., Fritsche, A., Häring, H., Schleicher, E. D., Xu, G., et al. (2010) · Metabolomics · doi:10.1007/s11306-010-0203-1 · Cited by 210
MassTRIX: mass translator into pathways
Suhre, K., & Schmitt‐Kopplin, P. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn194 · Cited by 203
Rashed, M. S. (2001) · Journal of Chromatography B Biomedical Sciences and Applications · doi:10.1016/s0378-4347(01)00100-1 · Cited by 178
Altmaier, E., Ramsay, S. L., Graber, A., Mewes, H., Weinberger, K. M., & Suhre, K. (2008) · Endocrinology · doi:10.1210/en.2007-1747 · Cited by 124
Wopereis, S., Rubingh, C. M., van Erk, M. J., Verheij, E. R., van Vliet, T., Cnubben, N. H. P., Smilde, A. K., van der Greef, J., van Ommen, B., & Hendriks, H. F. J. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004525 · Cited by 123
Maher, A. D., Zirah, S., Holmes, E. C., & Nicholson, J. K. (2007) · Analytical Chemistry · doi:10.1021/ac070212f · Cited by 112
β‐Aminoisobutyric Acid Prevents Diet‐induced Obesity in Mice With Partial Leptin Deficiency
Begriche, K., Massart, J., Abbey‐Toby, A., Igoudjil, A., Lettéron, P., & Fromenty, B. (2008) · Obesity · doi:10.1038/oby.2008.337 · Cited by 99
The carnitine acyltransferases: modulators of acyl-CoA-dependent reactions
Ramsay, R. R. (2000) · Biochemical Society Transactions · doi:10.1042/bst0280182 · Cited by 89
Metabolomics of prolonged fasting in humans reveals new catabolic markers
Rubio‐Aliaga, I., de Roos, B., Duthie, S. J., Crosley, L. K., Mayer, C., Horgan, G. W., Colquhoun, I. J., Gall, G. L., Huber, F., Kremer, W., et al. (2010) · Metabolomics · doi:10.1007/s11306-010-0255-2 · Cited by 75
Lucio, M., Fekete, Á., Weigert, C., Wägele, B., Zhao, X., Chen, J., Fritsche, A., Häring, H., Schleicher, E. D., Xu, G., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013317 · Cited by 70
The incidence of alkaptonuria: a study in chemical individuality. 1902 [classical article].
Garrod, A. E. (2003) · PubMed · Cited by 63
Inborn errors of metabolism: a clinical overview
Martins, A. M. (1999) · Sao Paulo Medical Journal · doi:10.1590/s1516-31801999000600006 · Cited by 46
An Overview of Newborn Screening
Levy, P. A. (2010) · Journal of Developmental & Behavioral Pediatrics · doi:10.1097/dbp.0b013e3181eedf01 · Cited by 42
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High‐Throughput Metabolomics by 1D NMR
Vignoli, A., Ghini, V., Meoni, G., Licari, C., Takis, P. G., Tenori, L., Turano, P., & Luchinat, C. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201804736 · Cited by 383
Kuehnbaum, N. L., & Britz‐McKibbin, P. (2013) · Chemical Reviews · doi:10.1021/cr300484s · Cited by 371
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Krumsiek, J., Mittelstraß, K., Do, K. T., Stückler, F., Ried, J. S., Adamski, J., Peters, A., Illig, T., Kronenberg, F., Friedrich, N., et al. (2015) · Metabolomics · doi:10.1007/s11306-015-0829-0 · Cited by 293
Metadata
- Authors
- Krug, S., Kastenmüller, G., Stückler, F., Rist, M. J., Skurk, T., Sailer, M., Raffler, J., Römisch‐Margl, W., Adamski, J., Prehn, C., Frank, T., Engel, K., Hofmann, T., Luy, B., Zimmermann, R., Moritz, F., Schmitt‐Kopplin, P., Krumsiek, J., Kremer, W., Huber, F., Oeh, U., Theis, F. J., Szymczak, W., Hauner, H., Suhre, K., & Daniel, H.
- Affiliations
- Technical University of Munich (DE)
- Helmholtz Munich (DE)
- Department of General Food Technology Technische Universität München Freising‐Weihenstephan Germany
- Department of Food Chemistry and Molecular Sensory Science Technische Universität München Freising‐Weihenstephan Germany
- Karlsruhe Institute of Technology (DE)
- University of Regensburg (DE)
- LipoFIT Analytic GmbH Regensburg Germany
- TUM Klinikum (DE)
- Corresponding author
- Skurk, Thomas
- ORCID
- Kastenmüller, Gabi
- Rist, Manuela J.
- Skurk, Thomas
- Sailer, Manuela
- Raffler, Johannes
- Römisch‐Margl, Werner
- Adamski, Jerzy
- Prehn, Cornelia
- Engel, Karl‐Heinz
- Luy, Burkhard
- Zimmermann, Ralf
- Moritz, Franco
- Schmitt‐Kopplin, Philippe
- Krumsiek, Jan
- Kremer, Werner
- Theis, Fabian J.
- Hauner, Hans
- Suhre, Karsten
- Daniel, Hannelore
- Venue
- The FASEB Journal
- Publisher
- Wiley
- Published
- 2012-03-16
- Volume
- 26
- Issue
- 6
- Pages
- 2607-2619
- ISSN
- 0892-6638 (ISSN-L)
- 1530-6860
- OpenAlex
- W2096486669
- Cited by
- 327
- References
- 29
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 19 · 2023: 11 · 2022: 22 · 2021: 21 · 2020: 36 · 2019: 13 · 2018: 25 · 2017: 29 · 2016: 27 · 2015: 30 · 2014: 34 · 2013: 35 · 2012: 7
- FWCI
- 13.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The FASEB Journal (publishedVersion)
- PubMed (publishedVersion)
- University of Regensburg Publication Server (University of Regensburg) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Site cant be reached (submittedVersion)
- Funders
- Studienstiftung des Deutschen Volkes
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
- Keywords
- fasting, oral glucose tolerance test, physical activity, cold stress, metabolic phenotypes, fatty acid beta-oxidation, fat mass, lipids, amino acids, exhaled breath condensate, urinary metabolites, blood metabolites, systems biology models
- MeSH terms
- Adult, Breath Tests, Carnitine, Cold Temperature, Fasting, Fatty Acids, Glucose Tolerance Test, Humans, Lipids, Male, Models, Biological, Magnetic Resonance Spectroscopy, Oxidation-Reduction, Stress, Physiological, Exercise, Lipid Metabolism, Metabolomics, Metabolome
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