The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study
Open accessAbstract
The link between the gut microbiota and type 2 diabetes (T2D) warrants further investigation because of known confounding effects from antidiabetic treatment. Here, we profiled the gut microbiota in a discovery (n = 1,011) and validation (n = 484) cohort comprising Swedish subjects naive for diabetes treatment and grouped by glycemic status. We observed that overall gut microbiota composition was altered in groups with impaired glucose tolerance, combined glucose intolerance and T2D, but not in those with impaired fasting glucose. In addition, the abundance of several butyrate producers and functional potential for butyrate production were decreased both in prediabetes and T2D groups. Multivariate analyses and machine learning microbiome models indicated that insulin resistance was strongly associated with microbial variations. Therefore, our study indicates that the gut microbiota represents an important modifiable factor to consider when developing precision medicine approaches for the prevention and/or delay of T2D.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280883 · Cited by 31,502
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
vegan: Community Ecology Package
Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O'Hara, R. B., Solymos, P., Stevens, M. H. H., Szoecs, E., et al. (2001) · CRAN: Contributed Packages · doi:10.32614/cran.package.vegan · Cited by 22,615
BLAST+: architecture and applications
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-421 · Cited by 22,074
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin
(2002) · New England Journal of Medicine · doi:10.1056/nejmoa012512 · Cited by 19,011
Tuomilehto, J., Lindström, J., Eriksson, J. G., Valle, T. T., Hämäläinen, H., Ilanne-Parikka, P., Keinänen-Kiukaanniemi, S., Laakso, M., Louheranta, A., Rastas, M., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200105033441801 · Cited by 10,873
Building Predictive Models in R Using the caret Package
Kuhn, M. (2008) · Journal of Statistical Software · doi:10.18637/jss.v028.i05 · Cited by 8,863
From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites
Koh, A., De Vadder, F., Kovatcheva-Datchary, P., & Bäckhed, F. (2016) · Cell · doi:10.1016/j.cell.2016.05.041 · Cited by 7,070
Self-citation
Cited by
Gut microbiome and health: mechanistic insights
de Vos, W. M., Tilg, H., Van Hul, M., & Cani, P. D. (2022) · Gut · doi:10.1136/gutjnl-2021-326789 · Cited by 2,388
Trends in insulin resistance: insights into mechanisms and therapeutic strategy
Li, M., Chi, X., Wang, Y., Setrerrahmane, S., Xie, W., & Xu, H. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01073-0 · Cited by 939
Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance
Suez, J., Cohen, Y., Valdés-Mas, R., Mor, U., Dori-Bachash, M., Federici, S., Zmora, N., Leshem, A., Heinemann, M., Linevsky, R., et al. (2022) · Cell · doi:10.1016/j.cell.2022.07.016 · Cited by 423
Role of the gut microbiota in anticancer therapy: from molecular mechanisms to clinical applications
Zhao, L.-Y., Mei, J.-X., Yu, G., Lei, L., Zhang, W.-H., Liu, K., Chen, X.-L., Kołat, D., Yang, K., & Hu, J.-K. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01406-7 · Cited by 359
Precision nutrition: A systematic literature review
Kirk, D., Catal, C., & Tekinerdogan, B. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104365 · Cited by 238
Metadata
- Authors
- Wu, H., Tremaroli, V., Schmidt, C., Lundqvist, A., Olsson, L. M., Krämer, M., Gummesson, A., Perkins, R., Bergström, G., & Bäckhed, F.
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 32
- Issue
- 3
- Pages
- 379-390.e3
- ISSN
- 1550-4131 (ISSN-L)
- OpenAlex
- W3041248404
- Cited by
- 528
- References
- 69
- FWCI
- 15.97
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study. https://app.cochat.ai/papers/10.1016/j.cmet.2020.06.011
- The paper
- Wu, H., Tremaroli, V., Schmidt, C., Lundqvist, A., Olsson, L. M., Krämer, M., Gummesson, A., Perkins, R., Bergström, G., & Bäckhed, F. (2020). The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study. Cell Metabolism. https://doi.org/10.1016/j.cmet.2020.06.011
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
