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Lookup NU author(s): Dr Bano Louca
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2025.The interplay between diet and gut microbiome composition is complex. Faecal metabolites, the end products of human and microbial metabolism, provide insights into these interactions. Here, we integrate faecal metabolomics, metagenomics, and habitual dietary data from 1810 individuals from the TwinsUK and 837 from the ZOE PREDICT1 cohorts. Using machine learning models, we find that faecal metabolites accurately predict reported intakes of 20 food groups (area under the curve (AUC) > 0.80 for meat, nuts and seeds, wholegrains, tea and coffee, and alcohol) and adherence to seven dietary patterns (AUC from 0.71 for the Plant-based Diet Index to 0.83 for the Dietary Approaches to Stop Hypertension score). Notably, the faecal metabolome is a stronger predictor of atherosclerotic cardiovascular disease risk (AUC = 0.86) than the Dietary Approaches to Stop Hypertension score (AUC = 0.66). We identify 414 associations between 19 food groups and 211 metabolites, that significantly correlate with microbial α-diversity and 217 species. Our findings reveal that faecal metabolites capture mediations between diet and the gut microbiome, advancing our understanding of diet-related disease risk and informing metabolite-based interventions.
Author(s): Pope R, Visconti A, Zhang X, Louca P, Baleanu A-F, Lin Y, Asnicar F, Bermingham K, Wong KE, Michelotti GA, Wolf J, Segata N, Berry SE, Spector TD, Leeming ER, Gibson R, Menni C, Falchi M
Publication type: Article
Publication status: Published
Journal: Nature Communications
Year: 2025
Volume: 16
Issue: 1
Online publication date: 04/12/2025
Acceptance date: 27/10/2025
Date deposited: 16/12/2025
ISSN (electronic): 2041-1723
Publisher: Nature Research
URL: https://doi.org/10.1038/s41467-025-66046-7
DOI: 10.1038/s41467-025-66046-7
Data Access Statement: The raw metagenomic sequence data used in this study have been deposited in the European Bioinformatics Institute European Nucleo tide Archive database (TwinsUK accession code: PRJEB98467; ZOE PREDICT1 accession code: PRJEB39223). All data relating to TwinsUK samples have been deposited to the TwinsUK BioResource data man agement team. These data and non-metagenomic data for ZOE PRE DICT1 are available by application to the Twin Research Executive Access committee (TREC) at King’s College London. The TwinsUK BioResource is managed by TREC, which provides governance of access to TwinsUK data and samples. TwinsUK data users are bound by data sharing agreement set out in the data access application form, which includes responsibilities with respect to third party data sharing and maintaining participant privacy. Further responsibilities include a responsibility to acknowledge data sharing. All results from the associations studies between food and beverage groups
PubMed id: 41345102
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