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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8468122 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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