Abstract
Due to the high impact of diet exposure on health, it is crucial the generation of robust data of regular dietary intake, hence improving the accuracy of dietary assessment. The metabolites derived from individual food or group of food have great potential to become biomarkers of food intake (BFIs) and provide more objective food consumption measurements.
Herein, it is presented an untargeted metabolomic workflow for the discovery BFIs in blood and urine samples, from the study design to the biomarker identification. Samples are analyzed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). A wide variety of compounds are covered by separate analyses of medium to nonpolar molecules and polar metabolites based on two LC separations as well as both positive and negative electrospray ionization. The main steps of data treatment of the comprehensive data sets and statistical analysis are described, as well as the principal considerations for the BFI identification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Weckwerth W (2003) Metabolomics in sytems biology. Annu Rev Plant Biol 54:669–689. https://doi.org/10.1146/annurev.arplant.54.031902.135014
Maruvada P, Lampe JW, Wishart DS, Barupal D, Chester DN, Dodd D, Djoumbou-Feunang Y, Dorrestein PC, Dragsted LO, Draper J, Duffy LC, Dwyer JT, Emenaker NJ, Fiehn O, Gerszten RE, Hu FB, Karp RW, Klurfeld DM, Laughlin MR, Little AR, Lynch CJ, Moore SC, Nicastro HL, O’Brien DM, Ordovás JM, Osganian SK, Playdon M, Prentice R, Raftery D, Reisdorph N, Roche HM, Ross SA, Sang S, Scalbert A, Srinivas PR, Zeisel SH (2019) Perspective: dietary biomarkers of intake and exposure—exploration with omics approaches. Adv Nutr 11:200–215. https://doi.org/10.1093/advances/nmz075
O’Gorman A, Brennan L (2017) The role of metabolomics in determination of new dietary biomarkers. Proc Nutr Soc 76:295–302. https://doi.org/10.1017/S0029665116002974
Hedrick VE, Dietrich AM, Estabrooks PA, Savla J, Serrano E, Davy BM (2012) Dietary biomarkers: advances, limitations and future directions. Nutr J 11:1. https://doi.org/10.1186/1475-2891-11-109
Dunn WB, Wilson ID, Nicholls AW, Broadhurst D (2012) The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis 4:2249–2264. https://doi.org/10.4155/bio.12.204
Lacalle-Bergeron L, Izquierdo-Sandoval D, Sancho JV, López FJ, Hernández F, Portolés T (2021) Chromatography hyphenated to high resolution mass spectrometry in untargeted metabolomics for investigation of food (bio)markers. TrAC Trends Anal Chem 135:116161. https://doi.org/10.1016/j.trac.2020.116161
Castro-Puyana M, Pérez-Míguez R, Montero L, Herrero M (2017) Application of mass spectrometry-based metabolomics approaches for food safety, quality and traceability. TrAC Trends Anal Chem 93:102–118. https://doi.org/10.1016/j.trac.2017.05.004
Segers K, Declerck S, Mangelings D, Vander HY, Van EA (2019) Analytical techniques for metabolomic studies: a review. Bioanalysis 11:2297–2318. https://doi.org/10.4155/bio-2019-0014
Mairinger T, Causon TJ, Hann S (2018) The potential of ion mobility–mass spectrometry for non-targeted metabolomics. Curr Opin Chem Biol 42:9–15
Paglia G, Smith AJ, Astarita G (2021) Ion mobility mass spectrometry in the omics era: challenges and opportunities for metabolomics and lipidomics. Mass Spectrom Rev:mas.21686. https://doi.org/10.1002/mas.21686
Worley B, Powers R (2012) Multivariate analysis in metabolomics. Curr Metabolomics 1:92–107. https://doi.org/10.2174/2213235X130108
Bijlsma L, Bade R, Celma A, Mullin L, Cleland G, Stead S, Hernandez F, Sancho JV (2017) Prediction of collision cross-section values for small molecules: application to pesticide residue analysis. Anal Chem 89:6583–6589. https://doi.org/10.1021/acs.analchem.7b00741
Zhou Z, Tu J, Xiong X, Shen X, Zhu Z-J (2017) LipidCCS: prediction of collision cross-section values for lipids with high precision to support ion mobility–mass spectrometry-based lipidomics. Anal Chem 89:9559–9566. https://doi.org/10.1021/acs.analchem.7b02625
Plante P-L, Francovic-Fontaine É, May JC, McLean JA, Baker ES, Laviolette F, Marchand M, Corbeil J (2019) Predicting ion mobility collision cross-sections using a deep neural network: DeepCCS. Anal Chem 91:5191–5199. https://doi.org/10.1021/acs.analchem.8b05821
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Lacalle-Bergeron, L., Izquierdo-Sandoval, D., Sancho, J.V., Portolés, T. (2023). Discovery of Food Intake Biomarkers Using Metabolomics. In: González-Domínguez, R. (eds) Mass Spectrometry for Metabolomics. Methods in Molecular Biology, vol 2571. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2699-3_4
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2699-3_4
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2698-6
Online ISBN: 978-1-0716-2699-3
eBook Packages: Springer Protocols