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Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults.

Authors
  • Shinn, Leila M1
  • Li, Yutong2
  • Mansharamani, Aditya3
  • Auvil, Loretta S4
  • Welge, Michael E4, 5
  • Bushell, Colleen4, 5
  • Khan, Naiman A1, 6
  • Charron, Craig S7
  • Novotny, Janet A7
  • Baer, David J7
  • Zhu, Ruoqing2, 4
  • Holscher, Hannah D1, 4, 6, 8
  • 1 Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • 2 Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • 3 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • 4 National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • 5 Mayo-Illinois Alliance for Technology-Based Healthcare, Urbana, IL, USA.
  • 6 Department of Kinesiology & Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • 7 Beltsville Human Nutrition Research Center, USDA Agricultural Research Service, Beltsville, MD, USA.
  • 8 Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Type
Published Article
Journal
Journal of Nutrition
Publisher
Oxford University Press
Publication Date
Feb 01, 2021
Volume
151
Issue
2
Pages
423–433
Identifiers
DOI: 10.1093/jn/nxaa285
PMID: 33021315
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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. This study aimed to identify a compact set of fecal microbial biomarkers of food intake with high predictive accuracy. Data were aggregated from 5 controlled feeding studies in metabolically healthy adults (n = 285; 21-75 y; BMI 19-59 kg/m2; 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. 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. 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. © The Author(s) 2020. Published by Oxford University Press on behalf of American Society for Nutrition.

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