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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
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.
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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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