The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Multi-Omic Analysis of the Microbiome and Metabolome in Healthy Subjects Reveals Microbiome-Dependent Relationships Between Diet and Metabolites
Open accessAbstract
The human microbiome has been associated with health status, and risk of disease development. While the etiology of microbiome-mediated disease remains to be fully elucidated, one mechanism may be through microbial metabolism. Metabolites produced by commensal organisms, including in response to host diet, may affect host metabolic processes, with potentially protective or pathogenic consequences. We conducted multi-omic phenotyping of healthy subjects (N=136), in order to investigate the interaction between diet, the microbiome, and the metabolome in a cross-sectional sample. We analyzed the nutrient composition of self-reported diet (3-day food records and food frequency questionnaires). We profiled the gut and oral microbiome (16S rRNA) from stool and saliva, and applied metabolomic profiling to plasma and stool samples in a subset of individuals (N=75). We analyzed these multi-omic data to investigate the relationship between diet, the microbiome, and the gut and circulating metabolome. On a global level, we observed significant relationships, particularly between long-term diet, the gut microbiome and the metabolome. Intake of plant-derived nutrients as well as consumption of artificial sweeteners were associated with significant differences in circulating metabolites, particularly bile acids, which were dependent on gut enterotype, indicating that microbiome composition mediates the effect of diet on host physiology. Our analysis identifies dietary compounds and phytochemicals that may modulate bacterial abundance within the gut and interact with microbiome composition to alter host metabolism.
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Cites
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Diet rapidly and reproducibly alters the human gut microbiome
David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., Ling, A. V., Devlin, A. S., Varma, Y., Fischbach, M. A., et al. (2014) · Nature · doi:10.1038/nature12820 · Cited by 10,100
Human gut microbiome viewed across age and geography
Yatsunenko, T., Rey, F. E., Manary, M. J., Trehan, I., Dominguez-Bello, M. G., Contreras, M., Magris, M., Hidalgo, G., Baldassano, R. N., Anokhin, A. P., et al. (2012) · Nature · doi:10.1038/nature11053 · Cited by 7,815
Heart disease and stroke statistics-2018 update: a report from the american heart association
Benjamin, E. J., Virani, S. S., Callaway, C. W., Chamberlain, A. M., Chang, A. R., Cheng, S., Chiuve, S. E., Cushman, M., Delling, F. N., Deo, R., et al. (2018) · Circulation · doi:10.1161/cir.0000000000000558 · Cited by 7,146
Self-citationGut flora metabolism of phosphatidylcholine promotes cardiovascular disease
Wang, Z., Klipfell, E., Bennett, B. J., Koeth, R., Levison, B. S., DuGar, B., Feldstein, A. E., Britt, E. B., Fu, X., Chung, Y.-M., et al. (2011) · Nature · doi:10.1038/nature09922 · Cited by 5,597
De Filippo, C., Cavalieri, D., Di Paola, M., Ramazzotti, M., Poullet, J. B., Massart, S., Collini, S., Pieraccini, G., & Lionetti, P. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1005963107 · Cited by 5,579
Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells
Furusawa, Y., Obata, Y., Fukuda, S., Endo, T. A., Nakato, G., Takahashi, D., Nakanishi, Y., Uetake, C., Kato, K., Kato, T., et al. (2013) · Nature · doi:10.1038/nature12721 · Cited by 5,549
A general approach to causal mediation analysis.
Imai, K., Keele, L., & Tingley, D. (2010) · Psychological Methods · doi:10.1037/a0020761 · Cited by 4,143
Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk
Tang, W. H. W., Wang, Z., Levison, B. S., Koeth, R. A., Britt, E. B., Fu, X., Wu, Y., & Hazen, S. L. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1109400 · Cited by 3,400
Mechanisms linking obesity with cardiovascular disease
Van Gaal, L. F., Mertens, I. L., & De Block, C. E. (2006) · Nature · doi:10.1038/nature05487 · Cited by 2,814 · cited 1×
“We measured plasma CRP, as a clinically-relevant marker of inflammation, which predicts future disease risk (Ridker, 2003), and used BMI as a proxy for obesity and future metabolic risk (Van Gaal et al., 2006).”
Method
Cited by
Health and disease markers correlate with gut microbiome composition across thousands of people
Manor, O., Dai, C. L., Kornilov, S. A., Smith, B., Price, N. D., Lovejoy, J. C., Gibbons, S. M., & Magis, A. T. (2020) · Nature Communications · doi:10.1038/s41467-020-18871-1 · Cited by 816
Metadata
- Authors
- Tang, Z.-Z., Chen, G., Hong, Q., Huang, S., Smith, H. M., Shah, R. D., Scholz, M., & Ferguson, J. F.
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2019-05-17
- Volume
- 10
- Pages
- 454-454
- ISSN
- 1664-8021 (ISSN-L)
- OpenAlex
- W2945425561
- Cited by
- 169
- References
- 28
- FWCI
- 7.16
- Type
- journal article
- Language
- en
- Open access
- gold
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- CoChat. (2026). Citation check: Multi-Omic Analysis of the Microbiome and Metabolome in Healthy Subjects Reveals Microbiome-Dependent Relationships Between Diet and Metabolites. https://app.cochat.ai/papers/10.3389/fgene.2019.00454
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- Tang, Z.-Z., Chen, G., Hong, Q., Huang, S., Smith, H. M., Shah, R. D., Scholz, M., & Ferguson, J. F. (2019). Multi-Omic Analysis of the Microbiome and Metabolome in Healthy Subjects Reveals Microbiome-Dependent Relationships Between Diet and Metabolites. Frontiers in Genetics. https://doi.org/10.3389/fgene.2019.00454
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