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Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics

Diet is recognised as the main driver of changes in gut microbiota. However, linking habitual dietary intake to microbiome composition and activity remains a challenge, leaving most microbiome studies with little or no dietary information. To fill this knowledge gap, we conducted two consecutive stu...

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Autores principales: Yáñez, Francisca, Soler, Zaida, Oliero, Manon, Xie, Zixuan, Oyarzun, Iñigo, Serrano-Gómez, Gerard, Manichanh, Chaysavanh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468122/
https://www.ncbi.nlm.nih.gov/pubmed/34578856
http://dx.doi.org/10.3390/nu13092978
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author Yáñez, Francisca
Soler, Zaida
Oliero, Manon
Xie, Zixuan
Oyarzun, Iñigo
Serrano-Gómez, Gerard
Manichanh, Chaysavanh
author_facet Yáñez, Francisca
Soler, Zaida
Oliero, Manon
Xie, Zixuan
Oyarzun, Iñigo
Serrano-Gómez, Gerard
Manichanh, Chaysavanh
author_sort Yáñez, Francisca
collection PubMed
description Diet is recognised as the main driver of changes in gut microbiota. However, linking habitual dietary intake to microbiome composition and activity remains a challenge, leaving most microbiome studies with little or no dietary information. To fill this knowledge gap, we conducted two consecutive studies (n = 84: a first pilot study (n = 40) to build a web-based, semi-quantitative simplified FFQ (sFFQ) based on three 24-h dietary recalls (24HRs); a second study (n = 44) served to validate the newly developed sFFQ using three 24HRs as reference method and to relate gut microbiome profiling (16S rRNA gene) with the extracted dietary and lifestyle data. Relative validation analysis provided acceptable classification and agreement for 13 out of 24 (54%) food groups and 20 out of 29 nutrients (69%) based on intraclass correlation coefficient, cross-classification, Spearman’s correlation, Wilcoxon test, and Bland–Altman. Microbiome analysis showed that higher diversity was positively associated with age, vaginal birth, and intake of fruit. In contrast, microbial diversity was negatively associated with BMI, processed meats, ready-to-eat meals, sodium, and saturated fat. Our analysis also revealed a correlation between food groups or nutrients and microbial composition. Overall, we provide the first dietary assessment tool to be validated and correlated with microbiome data for population studies.
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spelling pubmed-84681222021-09-27 Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics Yáñez, Francisca Soler, Zaida Oliero, Manon Xie, Zixuan Oyarzun, Iñigo Serrano-Gómez, Gerard Manichanh, Chaysavanh Nutrients Article Diet is recognised as the main driver of changes in gut microbiota. However, linking habitual dietary intake to microbiome composition and activity remains a challenge, leaving most microbiome studies with little or no dietary information. To fill this knowledge gap, we conducted two consecutive studies (n = 84: a first pilot study (n = 40) to build a web-based, semi-quantitative simplified FFQ (sFFQ) based on three 24-h dietary recalls (24HRs); a second study (n = 44) served to validate the newly developed sFFQ using three 24HRs as reference method and to relate gut microbiome profiling (16S rRNA gene) with the extracted dietary and lifestyle data. Relative validation analysis provided acceptable classification and agreement for 13 out of 24 (54%) food groups and 20 out of 29 nutrients (69%) based on intraclass correlation coefficient, cross-classification, Spearman’s correlation, Wilcoxon test, and Bland–Altman. Microbiome analysis showed that higher diversity was positively associated with age, vaginal birth, and intake of fruit. In contrast, microbial diversity was negatively associated with BMI, processed meats, ready-to-eat meals, sodium, and saturated fat. Our analysis also revealed a correlation between food groups or nutrients and microbial composition. Overall, we provide the first dietary assessment tool to be validated and correlated with microbiome data for population studies. MDPI 2021-08-27 /pmc/articles/PMC8468122/ /pubmed/34578856 http://dx.doi.org/10.3390/nu13092978 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yáñez, Francisca
Soler, Zaida
Oliero, Manon
Xie, Zixuan
Oyarzun, Iñigo
Serrano-Gómez, Gerard
Manichanh, Chaysavanh
Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics
title Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics
title_full Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics
title_fullStr Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics
title_full_unstemmed Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics
title_short Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics
title_sort integrating dietary data into microbiome studies: a step forward for nutri-metaomics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468122/
https://www.ncbi.nlm.nih.gov/pubmed/34578856
http://dx.doi.org/10.3390/nu13092978
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