Global testing of shifts in metabolic phenotype

Parastoo Fazelzadeh, Huub C J Hoefsloot, Thomas Hankemeier, Jasper Most, Sander Kersten, Ellen E Blaak, Mark Boekschoten, John van Duynhoven, Parastoo Fazelzadeh, Huub C J Hoefsloot, Thomas Hankemeier, Jasper Most, Sander Kersten, Ellen E Blaak, Mark Boekschoten, John van Duynhoven

Abstract

Introduction: Current metabolomics approaches to unravel impact of diet- or lifestyle induced phenotype variation and shifts predominantly deploy univariate or multivariate approaches, with a posteriori interpretation at pathway level. This however often provides only a fragmented view on the involved metabolic pathways.

Objectives: To demonstrate the feasibility of using Goeman's global test (GGT) for assessment of variation and shifts in metabolic phenotype at the level of a priori defined pathways.

Methods: Two intervention studies with identified phenotype variations and shifts were examined. In a weight loss (WL) intervention study obese subjects received a mixed meal challenge before and after WL. In a polyphenol (PP) intervention study obese subjects received a high fat mixed meal challenge (61E% fat) before and after a PP intervention. Plasma samples were obtained at fasting and during the postprandial response. Besides WL- and PP-induced phenotype shifts, also correlation of plasma metabolome with phenotype descriptors was assessed at pathway level. The plasma metabolome covered organic acids, amino acids, biogenic amines, acylcarnitines and oxylipins.

Results: For the population of the WL study, GGT revealed that HOMA correlated with the fasting levels of the TCA cycle, BCAA catabolism, the lactate, arginine-proline and phenylalanine-tyrosine pathways. For the population of the PP study, HOMA correlated with fasting metabolite levels of TCA cycle, fatty acid oxidation and phenylalanine-tyrosine pathways. These correlations were more pronounced for metabolic pathways in the fasting state, than during the postprandial response. The effect of the WL and PP intervention on a priori defined metabolic pathways, and correlation of pathways with insulin sensitivity as described by HOMA was in line with previous studies.

Conclusion: GGT confirmed earlier biological findings in a hypothesis led approach. A main advantage of GGT is that it provides a direct view on involvement of a priori defined pathways in phenotype shifts.

Trial registration: ClinicalTrials.gov NCT01675401 NCT02381145.

Keywords: Goeman’s global test; Metabolic pathways; Phenotype shifts.

Conflict of interest statement

Conflict of interest

JvD is employed by a company that manufactures and markets food products. None of the materials or products used in this study originates from this company. The other authors declare they have no conflict of interest.

Ethical approval

The studies were approved by the Medical Ethics Committee of Maastricht University Medical Center and registered at clinicaltrials.gov as NCT01675401 and NCT02381145.

Informed consent

All participants gave written, informed consent before entering the study.

Figures

Fig. 1
Fig. 1
Schematic overview of Goeman’s global testing of differences in pathways between different phenotype groups (lean, obese) and due to WL and PP interventions. In the WL and PP intervention studies respectively also control (CTRL) and placebo groups were included. For all groups plasma was sampled at fasting state (T0) and during the postprandial response to a meal challenge. The postprandial response was captured in the incremental area under the curve (iAUC). The effect of the WL, CRTL, PP and placebo interventions was summarized in ΔT0 and ΔiAUC. The vertical arrows indicate application of Goeman’s test (indicated in red) to comparison of lean versus obese (based on T0 and iAUC) and on comparison of WL versus CRTL and PP versus placebo groups (based on ΔT0 and ΔiAUC)
Fig. 2
Fig. 2
P-value distribution of effect of WL on fasting state values in obese subjects (ΔWL T0 vs. Δ CTRL T0). a Effect of WL on challenge response values in obese subjects (iAUC WL vs. iAUC CTRL). b Effect of PP intervention on fasting state values in obese subjects (Δ PP T0 vs. Δ placebo T0). c Effect of PP intervention on challenge response values in obese subjects (iAUC PP vs. iAUC placebo). d Metabolites with P < 0.05 were coloured green and otherwise red

References

    1. Carstensen M, Thomsen C, Hermansen K. Incremental area under response curve more accurately describes the triglyceride response to an oral fat load in both healthy and type 2 diabetic subjects. Metabolism. 2003;52:1034–1037. doi: 10.1016/S0026-0495(03)00155-0.
    1. Corpeleijn E, Saris WH, Blaak EE. Metabolic flexibility in the development of insulin resistance and type 2 diabetes: Effects of lifestyle. Obesity Reviews. 2009;10:178–193. doi: 10.1111/j.1467-789X.2008.00544.x.
    1. Elliott R, Pico C, Dommels Y, Wybranska I, Hesketh J, Keijer J. Nutrigenomic approaches for benefit-risk analysis of foods and food components: Defining markers of health. British Journal of Nutrition. 2007;98:1095–1100. doi: 10.1017/S0007114507803400.
    1. Fazelzadeh P, Hangelbroek RWJ, Joris PJ, Schalkwijk CG, Esser D, Afman L, et al. Weight loss moderately affects the mixed meal challenge response of the plasma metabolome and transcriptome of peripheral blood mononuclear cells in abdominally obese subjects. Metabolomics. 2018;14:46. doi: 10.1007/s11306-018-1328-x.
    1. Fiamoncini J, Rundle M, Gibbons H, Thomas EL, Geillinger-Kastle K, Bunzel D, et al. Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements. The FASEB Journal. 2018 doi: 10.1096/fj.201800330R.
    1. Goeman JJ, Van De Geer SA, De Kort F, Van Houwelingen HC. A global test for groups of genes: Testing association with a clinical outcome. Bioinformatics. 2004;20:93–99. doi: 10.1093/bioinformatics/btg382.
    1. Hendrickx DM, Hoefsloot HC, Hendriks MM, Canelas AB, Smilde AK. Global test for metabolic pathway differences between conditions. Analytica Chimica Acta. 2012;719:8–15. doi: 10.1016/j.aca.2011.12.051.
    1. Huber M, Knottnerus JA, Green L, Horst HVD, Jadad AR, Kromhout D, et al. How should we define health? BMJ. 2011;343:d4163. doi: 10.1136/bmj.d4163.
    1. Joris PJ, Plat J, Kusters YH, Houben AJ, Stehouwer CD, Schalkwijk CG, et al. Diet-induced weight loss improves not only cardiometabolic risk markers but also markers of vascular function: A randomized controlled trial in abdominally obese men. The American Journal of Clinical Nutrition. 2016;105:23–31. doi: 10.3945/ajcn.116.143552.
    1. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research. 2000;28:27–30. doi: 10.1093/nar/28.1.27.
    1. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Research. 2010;38:D355–D360. doi: 10.1093/nar/gkp896.
    1. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, et al. From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Research. 2006;34:D354–D357. doi: 10.1093/nar/gkj102.
    1. Krug S, Kastenmuller G, Stuckler F, Rist MJ, Skurk T, Sailer M, et al. The dynamic range of the human metabolome revealed by challenges. The FASEB Journal. 2012;26:2607–2619. doi: 10.1096/fj.11-198093.
    1. Mihaleva VV, Korhonen SP, Van Duynhoven J, Niemitz M, Vervoort J, Jacobs DM. Automated quantum mechanical total line shape fitting model for quantitative NMR-based profiling of human serum metabolites. Analytical and Bioanalytical Chemistry. 2014;406:3091–3102. doi: 10.1007/s00216-014-7752-5.
    1. Most J, Timmers S, Warnke I, Jocken JW, Van Boekschoten M, De Groot P, et al. Combined epigallocatechin-3-gallate and resveratrol supplementation for 12 wk increases mitochondrial capacity and fat oxidation, but not insulin sensitivity, in obese humans: A randomized controlled trial. The American Journal of Clinical Nutrition. 2016;104:215–227. doi: 10.3945/ajcn.115.122937.
    1. Newgard CB. Interplay between lipids and branched-chain amino acids in development of insulin resistance. Cell Metabolism. 2012;15:606–614. doi: 10.1016/j.cmet.2012.01.024.
    1. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metabolism. 2009;9:311–326. doi: 10.1016/j.cmet.2009.02.002.
    1. Noga MJ, Dane A, Shi S, Attali A, Van Aken H, Suidgeest E, et al. Metabolomics of cerebrospinal fluid reveals changes in the central nervous system metabolism in a rat model of multiple sclerosis. Metabolomics. 2012;8:253–263. doi: 10.1007/s11306-011-0306-3.
    1. Pellis L, Van Erk MJ, Van Ommen B, Bakker GC, Hendriks HF, Cnubben NH, et al. Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status. Metabolomics. 2012;8:347–359. doi: 10.1007/s11306-011-0320-5.
    1. Ramos-Roman MA, Sweetman L, Valdez MJ, Parks EJ. Postprandial changes in plasma acylcarnitine concentrations as markers of fatty acid flux in overweight and obesity. Metabolism. 2012;61:202–212. doi: 10.1016/j.metabol.2011.06.008.
    1. Senn S. Analysis of serial measurements in medical research. BMJ: British Medical Journal. 1990;300:680. doi: 10.1136/bmj.300.6725.680.
    1. Strimmer K. fdrtool: A versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics. 2008;24:1461–1462. doi: 10.1093/bioinformatics/btn209.
    1. Strimmer K. A unified approach to false discovery rate estimation. BMC Bioinformatics. 2008;9:303. doi: 10.1186/1471-2105-9-303.
    1. Van Der Kloet FM, Bobeldijk I, Verheij ER, Jellema RH. Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. Journal of Proteome Research. 2009;8:5132–5141. doi: 10.1021/pr900499r.
    1. Van Ommen B, Van Der Greef J, Ordovas JM, Daniel H. Phenotypic flexibility as key factor in the human nutrition and health relationship. Genes & Nutrition. 2014;9:423. doi: 10.1007/s12263-014-0423-5.
    1. Vis DJ, Westerhuis JA, Jacobs DM, Van Duynhoven JPM, Wopereis S, Van Ommen B, et al. Analyzing metabolomics-based challenge tests. Metabolomics. 2015;11:50–63. doi: 10.1007/s11306-014-0673-7.

Source: PubMed

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