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Comparative analysis of plasma metabolomics response to metabolic challenge tests in healthy subjects and influence of the FTO obesity risk allele

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Abstract

The measurement of metabolites during intravenous or nutritional challenges may improve the identification of novel metabolic signatures which are not detectable in the fasting state. Here, we comprehensively characterized the plasma metabolomics response to five defined challenge tests and explored their use to identify interactions with the FTO rs9939609 obesity risk genotype. Fifty-six non-diabetic male participants of the KORA S4/F4 cohort, including 25 homozygous carriers of the FTO risk allele (AA genotype) and 31 carriers of the TT genotype were recruited. Challenges comprised an oral glucose tolerance test, a standardized high-fat high-carbohydrate meal and a lipid tolerance test, as well as an intravenous glucose tolerance test and a euglycemic hyperinsulinemic clamp. Blood was sampled for biochemical and metabolomics measurement before and during the challenges. Plasma samples were analyzed using a mass spectrometry-based metabolomics approach targeting 163 metabolites. Linear mixed-effects models and cluster analysis were performed. In both genotype groups, we observed significant challenge-induced changes for all major metabolite classes (amino acids, hexose, acylcarnitines, phosphatidylcholines, lysophosphatidylcholines and sphingomyelins, with corrected p-values ranging from 0.05 to 6.7E−37), which clustered in five distinct metabolic response profiles. Our data contribute to the understanding of plasma metabolomics response to diverse metabolic challenges, including previously unreported metabolite changes in response to intravenous challenges. The FTO genotype had only minor effects on the metabolite fluxes after standardized metabolic challenges.

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Acknowledgments

This work was funded by the Else Kroener-Fresenius Foundation, Bad Homburg v. d. H, Germany, the grant Virtual Institute ‘Molecular basis of glucose regulation and type 2 diabetes’ received from the Helmholtz Zentrum München, Neuherberg, Germany, the grant Clinical Cooperation Group ‘Nutrigenomics and type 2 diabetes’ received from the Helmholtz Zentrum München, Neuherberg, Germany, the Technische Universität München, Freising-Weihenstephan, and by funding from the German Federal Ministry for Education and Research (BMBF) to the German Center for Diabetes Research (DZD e. V.). WRM is funded by BMBF Grant no. 03IS2061B (project Gani_Med). KS is supported by ‘Biomedical Research Program’ funds at Weill Cornell Medical College in Qatar, a program funded by the Qatar Foundation. The KORA study group consists of A. Peters (speaker), R. Leidl, R. Holle, J. Heinrich, C. Meisinger, C. Strauch, and their coworkers, who are responsible for the design and conduct of the KORA studies. We gratefully acknowledge the contribution of all members of field staffs conducting the KORA study and thank all study probands participating in the study. The KORA research platform studies were initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Science, Research and Technology and by the State of Bavaria. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Julia Scarpa, Katharina Sckell and Arsin Sabunchi for metabolomics measurement performed at the Helmholtz Zentrum München, Genome Analysis Center, Metabolomics Core Facility, Neuherberg, Germany, and Jan Krumsiek for advice concerning weighted enrichment analysis.

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All authors declare that there is no conflict of interest.

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Authors and Affiliations

  1. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany

    Simone Wahl, Thomas Illig & Harald Grallert

  2. German Center for Diabetes Research (DZD), Neuherberg, Germany

    Simone Wahl, Susanne Krug, Cornelia Huth, Christa Meisinger, Annette Peters, Barbara Thorand, Jerzy Adamski, Harald Grallert, Helmut Laumen & Hans Hauner

  3. Else Kröner-Fresenius-Center for Nutritional Medicine, Chair of Nutritional Medicine, Technische Universität München, Gregor-Mendel-Str. 2, 85350, Freising-Weihenstephan, Germany

    Susanne Krug, Thomas Skurk, Melina Claussnitzer, Helmut Laumen & Hans Hauner

  4. ZIEL, Research Center for Nutrition and Food Sciences, Technische Universität München, Freising-Weihenstephan, Germany

    Susanne Krug, Tina Brand, Thomas Skurk, Helmut Laumen & Hans Hauner

  5. Clinical Cooperation Group Nutrigenomics and Type 2, Technische Universität München and Helmholtz Zentrum München, Munich, Germany

    Susanne Krug, Tina Brand, Harald Grallert, Helmut Laumen & Hans Hauner

  6. Medizinische Klinik and Poliklinik IV, Diabetes Zentrum – Campus Innenstadt, Klinikum der Universität München, Munich, Germany

    Cornelia Then, Anna Kirchhofer & Jochen Seissler

  7. Clinical Cooperation Group Diabetes, Ludwig-Maximilians-Universität München and Helmholtz Zentrum München, Munich, Germany

    Cornelia Then, Anna Kirchhofer & Jochen Seissler

  8. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany

    Gabi Kastenmüller, Werner Römisch-Margl & Karsten Suhre

  9. Else Kröner-Fresenius-Center for Nutritional Medicine, Klinikum rechts der Isar, Technische Universität München, Freising-Weihenstephan, Germany

    Tina Brand, Thomas Skurk & Hans Hauner

  10. Institute of Epidemiology II, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany

    Cornelia Huth, Margit Heier, Christa Meisinger, Annette Peters & Barbara Thorand

  11. Institute of Genetic Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany

    Christian Gieger

  12. Institute of Experimental Genetics, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany

    Cornelia Prehn, Jerzy Adamski & Helmut Laumen

  13. Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany

    Jerzy Adamski

  14. Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar (WCMC-Q), Doha, Qatar

    Karsten Suhre

  15. Medical School Hannover, Hannover Unified Biobank, Hanover, Germany

    Thomas Illig

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  1. Simone Wahl
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  2. Susanne Krug
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  3. Cornelia Then
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  14. Christian Gieger
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  19. Thomas Illig
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  20. Harald Grallert
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  21. Helmut Laumen
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  22. Jochen Seissler
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Corresponding author

Correspondence to Helmut Laumen.

Additional information

Simone Wahl, Susanne Krug, and Cornelia Then have contributed equally to this work.

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Fig. S1 Time course of challenge responses of clinical traits. (a) and (b) Response to intravenous challenges, (c) to (g) response to oral challenges. Mean and standard deviation of plasma concentrations at the different time points are shown, connected through lines. Solid red lines, significant concentration changes as identified in LMEs, after correction for multiple testing. Dotted black lines, not significant. EH clamp euglycemic hyperinsulinemic clamp, HFHC meal high-fat high-carbohydrate meal, IVGTT intravenous glucose tolerance test, OGTT oral glucose tolerance test, OLTT oral lipid tolerance test

Fig. S2 K-means clustering of challenge response profiles, including only subjects having taken part in all challenge tests (n = 17 FTO risk allele carriers, n = 12 non-carriers). LMEs and clustering were performed as described for the full data set (Sect. 2), resulting in similar effects and a similar clustering solution. Mean scaled concentrations of each metabolite at the different time points are shown, connected through lines. Solid red lines, significant concentration changes as identified in LMEs, after correction for multiple testing. Dotted black lines, not significant. Metabolites belonging to the respective cluster are specified on the right-hand side of the graphs, the number of metabolites in each cluster is included in the graph titles. c45, 45 min after clamp steady state; EH clamp euglycemic hyperinsulinemic clamp, HFHC meal high-fat high-carbohydrate meal, IVGTT intravenous glucose tolerance test, OGTT oral glucose tolerance test, OLTT oral lipid tolerance test

Fig. S3. Time courses of challenge response for different biological metabolite groups within cluster 1, after including only subjects having taken part in all challenge tests (n = 17 FTO risk allele carriers, n = 12 non-carriers). Mean scaled concentrations of each metabolite at the different time points are shown, connected through lines. Solid red lines, significant concentration changes as identified in LMEs, after correction for multiple testing. Dotted black lines, not significant. Metabolites belonging to the respective biological group are specified on the right-hand side of the graphs, the number of metabolites in each group is included in the graph titles. c45, 45 min after clamp steady state; EH clamp euglycemic hyperinsulinemic clamp, HFHC meal high-fat high-carbohydrate meal, IVGTT intravenous glucose tolerance test, OGTT oral glucose tolerance test, OLTT oral lipid tolerance test

Table S1 Composition of the oral challenges according to manufacturers’ information. HFHC meal high-fat high-carbohydrate meal, OGTT oral glucose tolerance test, OLTT oral lipid tolerance testTable S2 Mean (sd) metabolite concentrations at different time points during the challenges and coefficients (95 % confidence intervals) and p-values for metabolite changes between time points as derived from LMEs. p-Values from all tests were subjected to correction for multiple testing, stars (*) indicate significant results after correction. EH clamp euglycemic hyperinsulinemic clamp, HFHC meal high-fat high-carbohydrate meal, IVGTT intravenous glucose tolerance test, OGTT oral glucose tolerance test, OLTT oral lipid tolerance test, ss steady state

Table S3 Mean (sd) concentrations of biochemical parameters at different time points during the challenges and coefficients (95 % confidence intervals) and p-values for concentration changes between time points as derived from LMEs. p-Values from all tests were subjected to correction for multiple testing, stars (*) indicate significant results after correction. EH clamp euglycemic hyperinsulinemic clamp, HFHC meal high-fat high-carbohydrate meal, IVGTT intravenous glucose tolerance test, OGTT oral glucose tolerance test, OLTT oral lipid tolerance test, ss steady state

Table S4 Metabolites with nominal FTO effect on 1-h OGTT response, as determined by LMEs. Mean metabolite concentrations are shown for FTO risk allele carriers and non-carriers at baseline and 1 h after the OGTTBelow is the link to the electronic supplementary material.

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Wahl, S., Krug, S., Then, C. et al. Comparative analysis of plasma metabolomics response to metabolic challenge tests in healthy subjects and influence of the FTO obesity risk allele. Metabolomics 10, 386–401 (2014). https://doi.org/10.1007/s11306-013-0586-x

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