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ARS Home » Northeast Area » Beltsville, Maryland (BHNRC) » Beltsville Human Nutrition Research Center » Food Components and Health Laboratory » Research » Publications at this Location » Publication #390945

Research Project: Strategies to Alter Dietary Food Components and Their Effects on Food Choice and Health-Related Outcomes

Location: Food Components and Health Laboratory

Title: Fecal metabolites as biomarkers for predicting food intake in healthy adults

item SHINN, LEILA - University Of Illinois
item MANSHARAMANI, ADITYA - University Of Illinois
item Baer, David
item Novotny, Janet
item CHARRON, CRAIG - Retired ARS Employee
item KHAN, NAIMAN - University Of Illinois
item ZHU, RUOQING - University Of Illinois
item HOLSCHER, HANNAH - University Of Illinois

Submitted to: Journal of Nutrition
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/25/2022
Publication Date: 8/30/2022
Citation: Shinn, L.A., Mansharamani, A., Baer, D.J., Novotny, J., Charron, C.S., Khan, N.A., Zhu, R., Holscher, H.D. 2022. Fecal metabolites as biomarkers for predicting food intake in healthy adults. Journal of Nutrition. 152:2956-2965.

Interpretive Summary: Researchers from multiple government agencies and public and private organizations have acknowledged that a need exists to promote the discovery, development, and use of biomarkers to collect better information on diets and consumed foods. Self-reported information on consumed foods is frequently used in studies but their reliability and validity have been scrutinized due to errors, including misreporting. The majority of identified biomarkers are from blood or urine samples, leaving a gap of knowledge on the usefulness of fecal samples, a non-invasive biological sample, to generate biomarkers of food intake. The purpose of the present investigation was to utilize a computationally intensive, multivariate, machine learning approach to identify metabolite biomarkers with high predictive accuracy of food intake. We examined fecal metabolites associated with individual food intake (i.e., almond, avocado, broccoli, whole-grain oat, whole-grain barley, and walnut). This effort revealed high predictive accuracy of almond and walnut intake, both individually (compared to respective controls) and in a mixed-food model (almond versus walnut). Most fecal metabolites identified as important features in differentiating each individual food were related to fat or amino acid metabolism. These findings begin to establish the potential role of fecal metabolites to objectively complement self-reported food measures and study compliance.

Technical Abstract: The fecal metabolome is affected by diet and includes metabolites generated by human and microbial metabolism. Advances in -omics technologies and analytic approaches have allowed researchers to identify metabolites and better utilize large data sets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake. We aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake. Data were aggregated from 5 controlled feeding studies in adults that tested the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. Fecal samples underwent GC-MS metabolomic analysis; 344 metabolites were detected in preintervention samples, whereas 307 metabolites were detected postintervention. After removing metabolites that were only detected in either pre- or postintervention and those undetectable in =80% of samples in all study groups, changes in 96 metabolites relative concentrations (treatment postintervention minus preintervention) were utilized in random forest models to 1) examine the relation between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score). Using the change in relative concentration of 96 fecal metabolites, 6 single-food random forest models for almond, avocado, broccoli, walnuts, whole-grain barley, and whole-grain oats revealed prediction accuracies between 47% and 89%. When comparing foods with one another, almond intake was differentiated from walnut intake with 91% classification accuracy. Our findings reveal promise in utilizing fecal metabolites as objective complements to certain selfreported food intake estimates. Future research on other foods at different doses and dietary patterns is needed to identify biomarkers that can be applied in feeding study compliance and clinical settings.