Fecal metagenomics to identify biomarkers of food intake in healthy adults: Findings from randomized, controlled, nutrition trials
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.10.536271v1?rss=1

Authors: Shinn, L. M., Mansharamani, A., Baer, D. J., Novotny, J. A., Charron, C. S., Khan, N. A., Zhu, R., Holscher, H. D.

Abstract: Background: Dietary intake provides nutrients for humans and their gastrointestinal microorganisms, as some dietary constituents bypass human digestion. These undigested components affect the composition and function of the microorganisms present. Metagenomic analyses allow researchers to study functional capacity. As dietary components affect the composition and function of the gastrointestinal microbiome, there is potential for developing objective biomarkers of food intake using metagenomic data. Objective: We aimed to utilize a computationally intensive, multivariate, machine learning approach to identify fecal Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) categories as biomarkers that accurately predict food intake. Design: Data were aggregated from five controlled feeding studies in adults that studied the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. DNA from pre- and post-intervention fecal samples underwent shotgun genomic sequencing. After pre-processing, sequences were aligned (DIAMONDv2.0.11.149) and functionally annotated (MEGANv6.12.2). After count normalization, the log of the fold change ratio for resulting features between pre- and post-intervention of the treatment group against its corresponding control was utilized to conduct differential KO abundance analysis. Differentially abundant KOs were used to train machine learning models examining potential biomarkers in both single-food and multi-food models. Results: We identified differentially abundant KOs for almond (n = 54), broccoli (n = 2,474), and walnut (n = 732) (q less than 0.20). Using the differentially abundant KOs, prediction accuracies were 80%, 87%, and 86% prediction accuracies for the almond, broccoli, and walnut groups, respectively using a random forest model to classify food intake. The mixed-food random forest achieved 81% prediction accuracy. Conclusions: Our findings reveal promise in utilizing fecal metagenomics to objectively complement self-reported measures of food intake. Future research on various foods and dietary patterns will expand these exploratory analyses for eventual use in feeding study compliance and clinical settings.

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