The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Microbiome and metabolome features of the cardiometabolic disease spectrum
Open accessAbstract
Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages—acute coronary syndrome, chronic IHD and IHD with heart failure—and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.
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
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Levey, A. S., Bosch, J. P., Lewis, J. B., Greene, T., Rogers, N., Roth, D., & for the Modification of Diet in Renal Disease Study Group* (1999) · Annals of Internal Medicine · doi:10.7326/0003-4819-130-6-199903160-00002 · Cited by 15,079
Heart Disease and Stroke Statistics—2015 Update: A Report From the American Heart Association
Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., de Ferranti, S., Després, J.-P., Fullerton, H. J., Howard, V. J., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000152 · Cited by 5,797
Richness of human gut microbiome correlates with metabolic markers
Le Chatelier, E., Nielsen, T., Qin, J., Prifti, E., Hildebrand, F., Falony, G., Almeida, M., Arumugam, M., Batto, J.-M., Kennedy, S., et al. (2013) · Nature · doi:10.1038/nature12506 · Cited by 4,662
Self-citationGut metagenome in European women with normal, impaired and diabetic glucose control
Karlsson, F. H., Tremaroli, V., Nookaew, I., Bergström, G., Behre, C. J., Fagerberg, B., Nielsen, J., & Bäckhed, F. (2013) · Nature · doi:10.1038/nature12198 · Cited by 3,115
Self-citationEnvironment dominates over host genetics in shaping human gut microbiota
Rothschild, D., Weissbrod, O., Barkan, E., Kurilshikov, A., Korem, T., Zeevi, D., Costea, P. I., Godneva, A., Kalka, I. N., Bar, N., et al. (2018) · Nature · doi:10.1038/nature25973 · Cited by 2,859
Self-citation2. Classification and Diagnosis of Diabetes:Standards of Medical Care in Diabetes—2019
American Diabetes Association (2019) · Diabetes Care · doi:10.2337/dc19-s002 · Cited by 2,697
Einarson, T. R., Acs, A., Ludwig, C., & Panton, U. H. (2018) · Cardiovascular Diabetology · doi:10.1186/s12933-018-0728-6 · Cited by 2,312
Myocardial Fatty Acid Metabolism in Health and Disease
Lopaschuk, G. D., Ussher, J. R., Folmes, C. D. L., Jaswal, J. S., & Stanley, W. C. (2010) · Physiological Reviews · doi:10.1152/physrev.00015.2009 · Cited by 2,179
An integrated catalog of reference genes in the human gut microbiome
MetaHIT Consortium, Li, J., Jia, H., Cai, X., Zhong, H., Feng, Q., Sunagawa, S., Arumugam, M., Kultima, J. R., Prifti, E., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2942 · Cited by 2,164
Self-citation
Cited by
Risk assessment with gut microbiome and metabolite markers in NAFLD development
Leung, H., Long, X., Ni, Y., Qian, L., Nychas, E., Siliceo, S. L., Pohl, D., Hanhineva, K., Liu, Y., Xu, A., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abk0855 · Cited by 152
Self-citationCardiometabolic health, diet and the gut microbiome: a meta-omics perspective
Valles-Colomer, M., Menni, C., Berry, S. E., Valdes, A. M., Spector, T. D., & Segata, N. (2023) · Nature Medicine · doi:10.1038/s41591-023-02260-4 · Cited by 114
Lee, J., Lee, J., Kim, K., Lee, J., Jung, Y., Hyeon, J. S., Seo, A., Jin, W., Weon, B., Shin, N., et al. (2024) · Kidney International · doi:10.1016/j.kint.2024.01.040 · Cited by 76
Saeedi Saravi, S. S., Pugin, B., Constancias, F., Shabanian, K., Spalinger, M., Thomas, A., Le Gludic, S., Shabanian, T., Karsai, G., Colucci, M., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00864-8 · Cited by 68
The Microbiome, Metabolism, and Networks in Precision Nutrition
Mensah, G. E., Maseng, M. G., Allard, S., & Gilbert, J. A. (2024) · Precision Nutrition · doi:10.1016/b978-0-443-15315-0.00011-0 · Cited by 2
Metadata
- Authors
- Fromentin, S., Forslund, S. K., Chechi, K., Aron-Wisnewsky, J., Chakaroun, R., Nielsen, T., Tremaroli, V., Ji, B., Prifti, E., Myridakis, A., Chilloux, J., Andrikopoulos, P., Fan, Y., Olanipekun, M. T., Alves, R., Adiouch, S., Bar, N., Talmor-Barkan, Y., Belda, E., Caesar, R., Coelho, L. P., Falony, G., Fellahi, S., Galan, P., Galleron, N., Helft, G., Hoyles, L., Isnard, R., Le Chatelier, E., Julienne, H., Olsson, L., Pedersen, H. K., Pons, N., Quinquis, B., Rouault, C., Roume, H., Salem, J.-E., Schmidt, T. S. B., Vieira-Silva, S., Li, P., Zimmermann-Kogadeeva, M., Lewinter, C., Søndertoft, N. B., Hansen, T. H., Gauguier, D., Gøtze, J. P., Køber, L., Kornowski, R., Vestergaard, H., Hansen, T., Zucker, J.-D., Hercberg, S., Letunic, I., Bäckhed, F., Oppert, J.-M., Nielsen, J., Raes, J., Bork, P., Stumvoll, M., Segal, E., Clément, K., Dumas, M.-E., Ehrlich, S. D., & Pedersen, O.
- Venue
- Nature Medicine
- Published
- 2022-02
- Volume
- 28
- Issue
- 2
- Pages
- 303-314
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4213163295
- Cited by
- 281
- References
- 71
- FWCI
- 15.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Microbiome and metabolome features of the cardiometabolic disease spectrum. https://app.cochat.ai/papers/10.1038/s41591-022-01688-4
- The paper
- Fromentin, S., Forslund, S. K., Chechi, K., Aron-Wisnewsky, J., Chakaroun, R., Nielsen, T., Tremaroli, V., Ji, B., Prifti, E., Myridakis, A., Chilloux, J., Andrikopoulos, P., Fan, Y., Olanipekun, M. T., Alves, R., Adiouch, S., Bar, N., Talmor-Barkan, Y., Belda, E., . . . Pedersen, O. (2022). Microbiome and metabolome features of the cardiometabolic disease spectrum. Nature Medicine. https://doi.org/10.1038/s41591-022-01688-4
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.
