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Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern

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Harvard

Andersen, M-BS, Rinnan, Å, Manach, C, Poulsen, SK, Pujos-Guillot, E, Larsen, TM, Astrup, A & Dragsted, LO 2014, 'Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern' Journal of Proteome Research, vol. 13, no. 3, pp. 1405-18. https://doi.org/10.1021/pr400964s

APA

Andersen, M-B. S., Rinnan, Å., Manach, C., Poulsen, S. K., Pujos-Guillot, E., Larsen, T. M., ... Dragsted, L. O. (2014). Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern. Journal of Proteome Research, 13(3), 1405-18. https://doi.org/10.1021/pr400964s

CBE

MLA

Vancouver

Author

Andersen, Maj-Britt S ; Rinnan, Åsmund ; Manach, Claudine ; Poulsen, Sanne K ; Pujos-Guillot, Estelle ; Larsen, Thomas Meinert ; Astrup, Arne ; Dragsted, Lars Ove. / Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern. In: Journal of Proteome Research. 2014 ; Vol. 13, No. 3. pp. 1405-18.

Bibtex

@article{bc454c9943394f3b89cd3460a609df73,
title = "Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern",
abstract = "There is a growing interest in studying the nutritional effects of complex diets. For such studies, measurement of dietary compliance is a challenge because the currently available compliance markers cover only limited aspects of a diet. In the present study, an untargeted metabolomics approach was used to develop a compliance measure in urine to distinguish between two dietary patterns. A parallel intervention study was carried out in which 181 participants were randomized to follow either a New Nordic Diet (NND) or an Average Danish Diet (ADD) for 6 months. Dietary intakes were closely monitored over the whole study period, and 24 h urine samples as well as weighed dietary records were collected several times during the study. The urine samples were analyzed by UPLC-qTOF-MS, and a partial least-squares discriminant analysis with feature selection was applied to develop a compliance model based on data from 214 urine samples. The optimized model included 52 metabolites and had a misclassification rate of 19{\%} in a validation set containing 139 samples. The metabolites identified in the model were markers of individual foods such as citrus, cocoa-containing products, and fish as well as more general dietary traits such as high fruit and vegetable intake or high intake of heat-treated foods. It was easier to classify the ADD diet than the NND diet probably due to seasonal variation in the food composition of NND and indications of lower compliance among the NND subjects. In conclusion, untargeted metabolomics is a promising approach to develop compliance measures that cover the most important discriminant metabolites of complex diets.",
keywords = "Adolescent, Adult, Aged, Citrus, Cooperative Behavior, Diet, Feeding Behavior, Female, Fish Products, Fruit, Humans, Male, Metabolomics, Middle Aged, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, Urinalysis, Vegetables, Journal Article, Research Support, Non-U.S. Gov't",
author = "Andersen, {Maj-Britt S} and {\AA}smund Rinnan and Claudine Manach and Poulsen, {Sanne K} and Estelle Pujos-Guillot and Larsen, {Thomas Meinert} and Arne Astrup and Dragsted, {Lars Ove}",
year = "2014",
month = "3",
day = "7",
doi = "10.1021/pr400964s",
language = "English",
volume = "13",
pages = "1405--18",
journal = "Journal of Proteome Research",
issn = "1535-3893",
publisher = "American Chemical Society",
number = "3",

}

RIS

TY - JOUR

T1 - Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern

AU - Andersen, Maj-Britt S

AU - Rinnan, Åsmund

AU - Manach, Claudine

AU - Poulsen, Sanne K

AU - Pujos-Guillot, Estelle

AU - Larsen, Thomas Meinert

AU - Astrup, Arne

AU - Dragsted, Lars Ove

PY - 2014/3/7

Y1 - 2014/3/7

N2 - There is a growing interest in studying the nutritional effects of complex diets. For such studies, measurement of dietary compliance is a challenge because the currently available compliance markers cover only limited aspects of a diet. In the present study, an untargeted metabolomics approach was used to develop a compliance measure in urine to distinguish between two dietary patterns. A parallel intervention study was carried out in which 181 participants were randomized to follow either a New Nordic Diet (NND) or an Average Danish Diet (ADD) for 6 months. Dietary intakes were closely monitored over the whole study period, and 24 h urine samples as well as weighed dietary records were collected several times during the study. The urine samples were analyzed by UPLC-qTOF-MS, and a partial least-squares discriminant analysis with feature selection was applied to develop a compliance model based on data from 214 urine samples. The optimized model included 52 metabolites and had a misclassification rate of 19% in a validation set containing 139 samples. The metabolites identified in the model were markers of individual foods such as citrus, cocoa-containing products, and fish as well as more general dietary traits such as high fruit and vegetable intake or high intake of heat-treated foods. It was easier to classify the ADD diet than the NND diet probably due to seasonal variation in the food composition of NND and indications of lower compliance among the NND subjects. In conclusion, untargeted metabolomics is a promising approach to develop compliance measures that cover the most important discriminant metabolites of complex diets.

AB - There is a growing interest in studying the nutritional effects of complex diets. For such studies, measurement of dietary compliance is a challenge because the currently available compliance markers cover only limited aspects of a diet. In the present study, an untargeted metabolomics approach was used to develop a compliance measure in urine to distinguish between two dietary patterns. A parallel intervention study was carried out in which 181 participants were randomized to follow either a New Nordic Diet (NND) or an Average Danish Diet (ADD) for 6 months. Dietary intakes were closely monitored over the whole study period, and 24 h urine samples as well as weighed dietary records were collected several times during the study. The urine samples were analyzed by UPLC-qTOF-MS, and a partial least-squares discriminant analysis with feature selection was applied to develop a compliance model based on data from 214 urine samples. The optimized model included 52 metabolites and had a misclassification rate of 19% in a validation set containing 139 samples. The metabolites identified in the model were markers of individual foods such as citrus, cocoa-containing products, and fish as well as more general dietary traits such as high fruit and vegetable intake or high intake of heat-treated foods. It was easier to classify the ADD diet than the NND diet probably due to seasonal variation in the food composition of NND and indications of lower compliance among the NND subjects. In conclusion, untargeted metabolomics is a promising approach to develop compliance measures that cover the most important discriminant metabolites of complex diets.

KW - Adolescent

KW - Adult

KW - Aged

KW - Citrus

KW - Cooperative Behavior

KW - Diet

KW - Feeding Behavior

KW - Female

KW - Fish Products

KW - Fruit

KW - Humans

KW - Male

KW - Metabolomics

KW - Middle Aged

KW - Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization

KW - Urinalysis

KW - Vegetables

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

U2 - 10.1021/pr400964s

DO - 10.1021/pr400964s

M3 - Journal article

VL - 13

SP - 1405

EP - 1418

JO - Journal of Proteome Research

JF - Journal of Proteome Research

SN - 1535-3893

IS - 3

ER -

ID: 50655676