The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Metabolic Footprint of Diabetes: A Multiplatform Metabolomics Study in an Epidemiological Setting
Open accessAbstract
BACKGROUND: Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study. METHODOLOGY/PRINCIPAL FINDINGS: 40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid). CONCLUSIONS/SIGNIFICANCE: Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.
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
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Human metabolic phenotype diversity and its association with diet and blood pressure
Holmes, E., Loo, R. L., Stamler, J., Bictash, M., Yap, I. K. S., Chan, Q., Ebbels, T., De Iorio, M., Brown, I. J., Veselkov, K. A., et al. (2008) · Nature · doi:10.1038/nature06882 · Cited by 1,054
The role of the gut microbiota in energy metabolism and metabolic disease
Cani, P., & Delzenne, N. (2009) · Current Pharmaceutical Design · doi:10.2174/138161209788168164 · Cited by 974
Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum
Gieger, C., Geistlinger, L., Altmaier, E., Hrabé de Angelis, M., Kronenberg, F., Meitinger, T., Mewes, H.-W., Wichmann, H.-E., Weinberger, K. M., Adamski, J., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000282 · Cited by 737
Self-citationAnalysis of the Adult Human Plasma Metabolome
Lawton, K. A., Berger, A., Mitchell, M., Milgram, K. E., Evans, A. M., Guo, L., Hanson, R. W., Kalhan, S. C., Ryals, J. A., & Milburn, M. V. (2008) · Pharmacogenomics · doi:10.2217/14622416.9.4.383 · Cited by 490
Self-citation
Cited by
Large-scale association analyses identify host factors influencing human gut microbiome composition
Kurilshikov, A., Medina-Gomez, C., Bacigalupe, R., Radjabzadeh, D., Wang, J., Demirkan, A., Le Roy, C. I., Raygoza Garay, J. A., Finnicum, C. T., Liu, X., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00763-1 · Cited by 1,786
Branched-chain amino acids in metabolic signalling and insulin resistance
Lynch, C. J., & Adams, S. H. (2014) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2014.171 · Cited by 1,461
Human metabolic individuality in biomedical and pharmaceutical research
CARDIoGRAM, Suhre, K., Shin, S.-Y., Petersen, A.-K., Mohney, R. P., Meredith, D., Wägele, B., Altmaier, E., Deloukas, P., Erdmann, J., et al. (2011) · Nature · doi:10.1038/nature10354 · Cited by 1,017
Self-citationFloegel, A., Stefan, N., Yu, Z., Mühlenbruch, K., Drogan, D., Joost, H.-G., Fritsche, A., Häring, H.-U., Hrabě de Angelis, M., Peters, A., et al. (2013) · Diabetes · doi:10.2337/db12-0495 · Cited by 988
Self-citationUnderstanding the role of gut microbiome–host metabolic signal disruption in health and disease
Holmes, E., Li, J. V., Athanasiou, T., Ashrafian, H., & Nicholson, J. K. (2011) · Trends in Microbiology · doi:10.1016/j.tim.2011.05.006 · Cited by 541
Metadata
- Authors
- Suhre, K., Meisinger, C., Döring, A., Altmaier, E., Belcredi, P., Gieger, C., Chang, D., Milburn, M. V., Gall, W. E., Weinberger, K. M., Mewes, H.-W., Hrabé de Angelis, M., Wichmann, H.-E., Kronenberg, F., Adamski, J., & Illig, T.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-11-11
- Volume
- 5
- Issue
- 11
- Pages
- e13953
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W1973529232
- Cited by
- 556
- References
- 5
- FWCI
- 15.48
- Type
- journal article
- Language
- en
- Open access
- gold
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: Metabolic Footprint of Diabetes: A Multiplatform Metabolomics Study in an Epidemiological Setting. https://app.cochat.ai/papers/10.1371/journal.pone.0013953
- The paper
- Suhre, K., Meisinger, C., Döring, A., Altmaier, E., Belcredi, P., Gieger, C., Chang, D., Milburn, M. V., Gall, W. E., Weinberger, K. M., Mewes, H.-W., Hrabé de Angelis, M., Wichmann, H.-E., Kronenberg, F., Adamski, J., & Illig, T. (2010). Metabolic Footprint of Diabetes: A Multiplatform Metabolomics Study in an Epidemiological Setting. PLoS ONE. https://doi.org/10.1371/journal.pone.0013953
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.
