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Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults
BACKGROUND: Diet affects the human gastrointestinal microbiota. Blood and urine samples have been used to determine nutritional biomarkers. However, there is a dearth of knowledge on the utility of fecal biomarkers, including microbes, as biomarkers of food intake. OBJECTIVES: This study aimed to id...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7849973/ https://www.ncbi.nlm.nih.gov/pubmed/33021315 http://dx.doi.org/10.1093/jn/nxaa285 |
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author | Shinn, Leila M Li, Yutong Mansharamani, Aditya Auvil, Loretta S Welge, Michael E Bushell, Colleen Khan, Naiman A Charron, Craig S Novotny, Janet A Baer, David J Zhu, Ruoqing Holscher, Hannah D |
author_facet | Shinn, Leila M Li, Yutong Mansharamani, Aditya Auvil, Loretta S Welge, Michael E Bushell, Colleen Khan, Naiman A Charron, Craig S Novotny, Janet A Baer, David J Zhu, Ruoqing Holscher, Hannah D |
author_sort | Shinn, Leila M |
collection | PubMed |
description | BACKGROUND: Diet affects the human gastrointestinal microbiota. Blood and urine samples have been used to determine nutritional biomarkers. However, there is a dearth of knowledge on the utility of fecal biomarkers, including microbes, as biomarkers of food intake. OBJECTIVES: This study aimed to identify a compact set of fecal microbial biomarkers of food intake with high predictive accuracy. METHODS: Data were aggregated from 5 controlled feeding studies in metabolically healthy adults (n = 285; 21–75 y; BMI 19–59 kg/m(2); 340 data observations) that studied the impact of specific foods (almonds, avocados, broccoli, walnuts, and whole-grain barley and whole-grain oats) on the human gastrointestinal microbiota. Fecal DNA was sequenced using 16S ribosomal RNA gene sequencing. Marginal screening was performed on all species-level taxa to examine the differences between the 6 foods and their respective controls. The top 20 species were selected and pooled together to predict study food consumption using a random forest model and out-of-bag estimation. The number of taxa was further decreased based on variable importance scores to determine the most compact, yet accurate feature set. RESULTS: Using the change in relative abundance of the 22 taxa remaining after feature selection, the overall model classification accuracy of all 6 foods was 70%. Collapsing barley and oats into 1 grains category increased the model accuracy to 77% with 23 unique taxa. Overall model accuracy was 85% using 15 unique taxa when classifying almonds (76% accurate), avocados (88% accurate), walnuts (72% accurate), and whole grains (96% accurate). Additional statistical validation was conducted to confirm that the model was predictive of specific food intake and not the studies themselves. CONCLUSIONS: Food consumption by healthy adults can be predicted using fecal bacteria as biomarkers. The fecal microbiota may provide useful fidelity measures to ascertain nutrition study compliance. |
format | Online Article Text |
id | pubmed-7849973 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-78499732021-02-04 Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults Shinn, Leila M Li, Yutong Mansharamani, Aditya Auvil, Loretta S Welge, Michael E Bushell, Colleen Khan, Naiman A Charron, Craig S Novotny, Janet A Baer, David J Zhu, Ruoqing Holscher, Hannah D J Nutr Methodology and Mathematical Modeling BACKGROUND: Diet affects the human gastrointestinal microbiota. Blood and urine samples have been used to determine nutritional biomarkers. However, there is a dearth of knowledge on the utility of fecal biomarkers, including microbes, as biomarkers of food intake. OBJECTIVES: This study aimed to identify a compact set of fecal microbial biomarkers of food intake with high predictive accuracy. METHODS: Data were aggregated from 5 controlled feeding studies in metabolically healthy adults (n = 285; 21–75 y; BMI 19–59 kg/m(2); 340 data observations) that studied the impact of specific foods (almonds, avocados, broccoli, walnuts, and whole-grain barley and whole-grain oats) on the human gastrointestinal microbiota. Fecal DNA was sequenced using 16S ribosomal RNA gene sequencing. Marginal screening was performed on all species-level taxa to examine the differences between the 6 foods and their respective controls. The top 20 species were selected and pooled together to predict study food consumption using a random forest model and out-of-bag estimation. The number of taxa was further decreased based on variable importance scores to determine the most compact, yet accurate feature set. RESULTS: Using the change in relative abundance of the 22 taxa remaining after feature selection, the overall model classification accuracy of all 6 foods was 70%. Collapsing barley and oats into 1 grains category increased the model accuracy to 77% with 23 unique taxa. Overall model accuracy was 85% using 15 unique taxa when classifying almonds (76% accurate), avocados (88% accurate), walnuts (72% accurate), and whole grains (96% accurate). Additional statistical validation was conducted to confirm that the model was predictive of specific food intake and not the studies themselves. CONCLUSIONS: Food consumption by healthy adults can be predicted using fecal bacteria as biomarkers. The fecal microbiota may provide useful fidelity measures to ascertain nutrition study compliance. Oxford University Press 2020-10-06 /pmc/articles/PMC7849973/ /pubmed/33021315 http://dx.doi.org/10.1093/jn/nxaa285 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of American Society for Nutrition. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Methodology and Mathematical Modeling Shinn, Leila M Li, Yutong Mansharamani, Aditya Auvil, Loretta S Welge, Michael E Bushell, Colleen Khan, Naiman A Charron, Craig S Novotny, Janet A Baer, David J Zhu, Ruoqing Holscher, Hannah D Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults |
title | Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults |
title_full | Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults |
title_fullStr | Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults |
title_full_unstemmed | Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults |
title_short | Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults |
title_sort | fecal bacteria as biomarkers for predicting food intake in healthy adults |
topic | Methodology and Mathematical Modeling |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7849973/ https://www.ncbi.nlm.nih.gov/pubmed/33021315 http://dx.doi.org/10.1093/jn/nxaa285 |
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