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Anal Chim Acta. 2016 Feb 18;908:121-31. doi: 10.1016/j.aca.2015.12.009. Epub 2015 Dec 17.

Postprandial metabolomics: A pilot mass spectrometry and NMR study of the human plasma metabolome in response to a challenge meal.

Author information

1
Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 90187 Umeå, Sweden.
2
Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 90187 Umeå, Sweden; Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå, Sweden.
3
Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 90187 Umeå, Sweden; Bioinformatics Infrastructure for Life Sciences, Sweden.
4
Department of Nutrition, University of California, Davis, One Shields Ave, CA 95616, USA.
5
Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 90187 Umeå, Sweden. Electronic address: malin.nording@umu.se.

Abstract

The study of postprandial metabolism is relevant for understanding metabolic diseases and characterizing personal responses to diet. We combined three analytical platforms - gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) - to validate a multi-platform approach for characterizing individual variation in the postprandial state. We analyzed the postprandial plasma metabolome by introducing, at three occasions, meal challenges on a usual diet, and 1.5 years later, on a modified background diet. The postprandial response was stable over time and largely independent of the background diet as revealed by all three analytical platforms. Coverage of the metabolome between NMR and GC-MS included more polar metabolites detectable only by NMR and more hydrophobic compounds detected by GC-MS. The variability across three separate testing occasions among the identified metabolites was in the range of 1.1-86% for GC-MS and 0.9-42% for NMR in the fasting state at baseline. For the LC-MS analysis, the coefficients of variation of the detected compounds in the fasting state at baseline were in the range of 2-97% for the positive and 4-69% for the negative mode. Multivariate analysis (MVA) of metabolites detected with GC-MS revealed that for both background diets, levels of postprandial amino acids and sugars increased whereas those of fatty acids decreased at 0.5 h after the meal was consumed, reflecting the expected response to the challenge meal. MVA of NMR data revealed increasing postprandial levels of amino acids and other organic acids together with decreasing levels of acetoacetate and 3-hydroxybutanoic acid, also independent of the background diet. Together these data show that the postprandial response to the same challenge meal was stable even though it was tested 1.5 years apart, and that it was largely independent of background diet. This work demonstrates the efficacy of a multi-platform metabolomics approach followed by multivariate and univariate data analysis for a broad-scale screen of the individual metabolome, particularly for studies using repeated measures to determine dietary response phenotype.

KEYWORDS:

Data analysis; Mass spectrometry; Metabolomics; Multi-platform analysis; Nucleic magnetic resonance; Postprandial

PMID:
26826694
DOI:
10.1016/j.aca.2015.12.009
[Indexed for MEDLINE]

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