Dietary exposure biomarker-lead discovery based on Metabolomics analysis of urine samples

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Dietary exposure biomarker-lead discovery based on Metabolomics analysis of urine samples. / Beckmann, Manfred; Lloyd, Amanda; Halder, Sumanto; Fave, Gaelle; Seal, Chris; Brandt, Kirsten; Mathers, John C.; Draper, John.

In: Proceedings of the Nutrition Society, Vol. 72, No. 3, 08.2013, p. 352-361.

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Harvard

Beckmann, M, Lloyd, A, Halder, S, Fave, G, Seal, C, Brandt, K, Mathers, JC & Draper, J 2013, 'Dietary exposure biomarker-lead discovery based on Metabolomics analysis of urine samples', Proceedings of the Nutrition Society, vol. 72, no. 3, pp. 352-361. https://doi.org/10.1017/S0029665113001237

APA

Beckmann, M., Lloyd, A., Halder, S., Fave, G., Seal, C., Brandt, K., Mathers, J. C., & Draper, J. (2013). Dietary exposure biomarker-lead discovery based on Metabolomics analysis of urine samples. Proceedings of the Nutrition Society, 72(3), 352-361. https://doi.org/10.1017/S0029665113001237

Vancouver

Beckmann M, Lloyd A, Halder S, Fave G, Seal C, Brandt K et al. Dietary exposure biomarker-lead discovery based on Metabolomics analysis of urine samples. Proceedings of the Nutrition Society. 2013 Aug;72(3):352-361. https://doi.org/10.1017/S0029665113001237

Author

Beckmann, Manfred ; Lloyd, Amanda ; Halder, Sumanto ; Fave, Gaelle ; Seal, Chris ; Brandt, Kirsten ; Mathers, John C. ; Draper, John. / Dietary exposure biomarker-lead discovery based on Metabolomics analysis of urine samples. In: Proceedings of the Nutrition Society. 2013 ; Vol. 72, No. 3. pp. 352-361.

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@article{26296e424a0541108d25783a53719b54,
title = "Dietary exposure biomarker-lead discovery based on Metabolomics analysis of urine samples",
abstract = "Although robust associations between dietary intake and population health are evident from conventional observational epidemiology, the outcomes of large-scale intervention studies testing the causality of those links have often proved inconclusive or have failed to demonstrate causality. This apparent conflict may be due to the well-recognised difficulty in measuring habitual food intake which may lead to confounding in observational epidemiology. Urine biomarkers indicative of exposure to specific foods offer information supplementary to thereliance on dietary intake self-assessment tools, such as FFQ, which are subject to individual bias. Biomarker discovery strategies using non-targeted metabolomics have been used recently to analyse urine from either short-term food intervention studies or from cohort studies in which participants consumed a freely-chosen diet. In the latter, the analysis of diet diary or FFQ information allowed classification of individuals in terms of the frequency of consumptionof specific diet constituents. We review these approaches for biomarker discovery and illustrate both with particular reference to two studies carried out by the authors using approaches combining metabolite fingerprinting by MS with supervised multivariate data analysis. In both approaches, urine signals responsible for distinguishing between specific foods were identified and could be related to the chemical composition of the original foods. When using dietarydata, both food distinctiveness and consumption frequency influenced whether differential dietary exposure could be discriminated adequately. We conclude that metabolomics methods for fingerprinting or profiling of overnight void urine, in particular, provide a robust strategy for dietary exposure biomarker-lead discovery.",
keywords = "Biological Markers/*urine Cereals Food Habits/*physiology Humans Metabolomics/*methods Multivariate Analysis Questionnaires Research Design, Dietary exposure, Metabolite fingerprinting, FFQ, Multivariate data analysis, Urine biomarkers",
author = "Manfred Beckmann and Amanda Lloyd and Sumanto Halder and Gaelle Fave and Chris Seal and Kirsten Brandt and Mathers, {John C.} and John Draper",
note = "Beckmann, M., Lloyd, A., Halder, S., Fave, G., Seal, C., Brandt, K., Mathers, J. C. Draper, J. (2013). Dietary exposure biomarker-lead discovery based on Metabolomics analysis of urine samples. Proceedings of the Nutrition Society, 72 (3), 352-361.",
year = "2013",
month = aug,
doi = "10.1017/S0029665113001237",
language = "English",
volume = "72",
pages = "352--361",
journal = "Proceedings of the Nutrition Society",
issn = "0029-6651",
publisher = "Cambridge University Press",
number = "3",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Dietary exposure biomarker-lead discovery based on Metabolomics analysis of urine samples

AU - Beckmann, Manfred

AU - Lloyd, Amanda

AU - Halder, Sumanto

AU - Fave, Gaelle

AU - Seal, Chris

AU - Brandt, Kirsten

AU - Mathers, John C.

AU - Draper, John

N1 - Beckmann, M., Lloyd, A., Halder, S., Fave, G., Seal, C., Brandt, K., Mathers, J. C. Draper, J. (2013). Dietary exposure biomarker-lead discovery based on Metabolomics analysis of urine samples. Proceedings of the Nutrition Society, 72 (3), 352-361.

PY - 2013/8

Y1 - 2013/8

N2 - Although robust associations between dietary intake and population health are evident from conventional observational epidemiology, the outcomes of large-scale intervention studies testing the causality of those links have often proved inconclusive or have failed to demonstrate causality. This apparent conflict may be due to the well-recognised difficulty in measuring habitual food intake which may lead to confounding in observational epidemiology. Urine biomarkers indicative of exposure to specific foods offer information supplementary to thereliance on dietary intake self-assessment tools, such as FFQ, which are subject to individual bias. Biomarker discovery strategies using non-targeted metabolomics have been used recently to analyse urine from either short-term food intervention studies or from cohort studies in which participants consumed a freely-chosen diet. In the latter, the analysis of diet diary or FFQ information allowed classification of individuals in terms of the frequency of consumptionof specific diet constituents. We review these approaches for biomarker discovery and illustrate both with particular reference to two studies carried out by the authors using approaches combining metabolite fingerprinting by MS with supervised multivariate data analysis. In both approaches, urine signals responsible for distinguishing between specific foods were identified and could be related to the chemical composition of the original foods. When using dietarydata, both food distinctiveness and consumption frequency influenced whether differential dietary exposure could be discriminated adequately. We conclude that metabolomics methods for fingerprinting or profiling of overnight void urine, in particular, provide a robust strategy for dietary exposure biomarker-lead discovery.

AB - Although robust associations between dietary intake and population health are evident from conventional observational epidemiology, the outcomes of large-scale intervention studies testing the causality of those links have often proved inconclusive or have failed to demonstrate causality. This apparent conflict may be due to the well-recognised difficulty in measuring habitual food intake which may lead to confounding in observational epidemiology. Urine biomarkers indicative of exposure to specific foods offer information supplementary to thereliance on dietary intake self-assessment tools, such as FFQ, which are subject to individual bias. Biomarker discovery strategies using non-targeted metabolomics have been used recently to analyse urine from either short-term food intervention studies or from cohort studies in which participants consumed a freely-chosen diet. In the latter, the analysis of diet diary or FFQ information allowed classification of individuals in terms of the frequency of consumptionof specific diet constituents. We review these approaches for biomarker discovery and illustrate both with particular reference to two studies carried out by the authors using approaches combining metabolite fingerprinting by MS with supervised multivariate data analysis. In both approaches, urine signals responsible for distinguishing between specific foods were identified and could be related to the chemical composition of the original foods. When using dietarydata, both food distinctiveness and consumption frequency influenced whether differential dietary exposure could be discriminated adequately. We conclude that metabolomics methods for fingerprinting or profiling of overnight void urine, in particular, provide a robust strategy for dietary exposure biomarker-lead discovery.

KW - Biological Markers/urine Cereals Food Habits/physiology Humans Metabolomics/methods Multivariate Analysis Questionnaires Research Design

KW - Dietary exposure

KW - Metabolite fingerprinting

KW - FFQ

KW - Multivariate data analysis

KW - Urine biomarkers

UR - http://hdl.handle.net/2160/26583

U2 - 10.1017/S0029665113001237

DO - 10.1017/S0029665113001237

M3 - Article

C2 - 23632011

VL - 72

SP - 352

EP - 361

JO - Proceedings of the Nutrition Society

JF - Proceedings of the Nutrition Society

SN - 0029-6651

IS - 3

ER -

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