ArticleThe Journal of nutrition2023
Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults.
Article in The Journal of nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- Leakage-Aware Machine Learning and Deep Learning Benchmarking of Food Antioxidant Capacity Prediction on the Antioxidant Food Table.Antioxidants (Basel, Switzerland) · 2026Article
- Targeted stool metabolomics suggests exploratory catecholamine- and tryptophan-linked metabolic features in autism spectrum disorder.Frontiers in neuroscience · 2026Article
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- Faecal metabolites as a readout of habitual diet capture dietary interactions with the gut microbiome.Nature communications · 2025Article
- State of omics-based microbial diagnostics of CRC.Gut microbes · 2025Review
- Fecal Microbiota and Metabolites Predict Metabolic Health Features across Various Dietary Patterns in Adults.The Journal of nutrition · 2025Article
- Predicting metabolite response to dietary intervention using deep learning.Nature communications · 2025Article
- Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments.Nature communications · 2024Article
- Predicting metabolite response to dietary intervention using deep learning.bioRxiv : the preprint server for biology · 2024Article
- Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments.bioRxiv : the preprint server for biology · 2024Article
- Identification of biological signatures of cruciferous vegetable consumption utilizing machine learning-based global untargeted stable isotope traced metabolomics.Frontiers in nutrition · 2024Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectivesWe aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake.
methodsData 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).
resultsUsing 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.
conclusionsOur findings reveal promise in utilizing fecal metabolites as objective complements to certain self-reported 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.
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