Leila M Shinn1, Yutong Li2, Aditya Mansharamani3, Loretta S Auvil4, Michael E Welge4,5, Colleen Bushell4,5, Naiman A Khan1,6, Craig S Charron7, Janet A Novotny7, David J Baer7, Ruoqing Zhu2,4, Hannah D Holscher1,4,6,8. 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.
Abstract
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/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. 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.
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/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. 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.