Fecal microbiome analysis in patients with metabolic syndrome and type 2 diabetes
| UDC.coleccion | Investigación | |
| UDC.departamento | Ciencias da Computación e Tecnoloxías da Información | |
| UDC.endPage | 27 | |
| UDC.grupoInv | Laboratorio de Aprendizaxe Automático en Ciencias Vivas (MALL) | |
| UDC.institutoCentro | CITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicación | |
| UDC.issue | e19108 | |
| UDC.journalTitle | PeerJ | |
| UDC.startPage | 1 | |
| UDC.volume | 13 | |
| dc.contributor.author | Fernández-Edreira, Diego | |
| dc.contributor.author | Sinisterra Loaiza, Laura Isabel | |
| dc.contributor.author | Liñares Blanco, José | |
| dc.contributor.author | Cepeda, Alberto | |
| dc.contributor.author | Cardelle-Cobas, Alejandra | |
| dc.contributor.author | Fernández-Lozano, Carlos | |
| dc.date.accessioned | 2025-09-18T14:56:59Z | |
| dc.date.available | 2025-09-18T14:56:59Z | |
| dc.date.issued | 2025-06 | |
| dc.description | The following information was supplied regarding data availability: The source code to reproduce all the analysis, along with documentation, is available on GitHub and Zenodo: - https://github.com/MALL-Machine-Learning-in-Live-Sciences/IBEROBDIA. - Diego FE, & Carlos Fernandez-Lozano. (2024). MALL-Machine-Learning-in-Live-Sciences/IBEROBDIA: 0.0.1 (0.0.1). Zenodo. https://doi.org/10.5281/zenodo.14051704. The raw data in phyloseq format is available at Figshare: Fernandez Edreira, Diego; Fernandez-Lozano, Carlos; Liñares, Jose (2025). Data. figshare. Dataset. https://doi.org/10.6084/m9.figshare.26063020.v1. Supplemental Information Experimental data (ratios, p-values, adjusted p-values, etc). DOI: 10.7717/peerj.19108/supp-1 | |
| dc.description.abstract | [Abstract]: Background: Metabolic syndrome (MS) and type 2 diabetes (T2D) are metabolically related diseases with rising global prevalence and increasingly evident links to the intestinal microbiota. Research suggests that imbalances in microbiota composition may play a crucial role in their pathogenesis. Specific population cohorts, such as the one in Galicia, Spain, offer the opportunity to analyze microbiota patterns within a distinct geographical and genetic context. This study was performed to investigate the relationship between the intestinal microbiota and MS and T2D. Methods: A cohort of 79 volunteers was analyzed over a 2-year study period. Recruitment posed significant challenges because of strict inclusion criteria (918PTE0540; PCI2018-093284), which required participants to be free from chronic medications and have a moderate to high risk of developing T2D. Volunteers were classified based on their serum glucose levels, body mass index, and the presence or absence of MS. To analyze the microbiota composition, amplicon sequencing of 16S rRNA genes was performed on stool samples. Alpha diversity was assessed using the Chao and Shannon indices, while beta diversity was evaluated using permutational analysis of variance with Bray–Curtis and Chao distances. Differential abundance analysis was conducted using the LinDA method. Results: In patients with MS, we observed a higher Firmicutes/Bacteroidetes ratio and an increased prevalence of Blautia compared to healthy patients. than in healthy individuals. Other enriched taxa in patients with MS included Tyzerella, Streptococcus, and Ruminococcus callidus. In patients with T2D, we observed a higher Bacteroidetes/Firmicutes ratio and a decrease in the phylum Actinobacteria compared with healthy individuals. Taxa such as Dorea, Prevotella, Dialister invisus, Fusicatenibacter, and Coprococcus were associated with T2D, while beneficial taxa such as Eubacterium, Ligilactobacillus, and Acidaminococcus were more prevalent in healthy or prediabetic individuals. Conclusions: This study reveals notable differences in the intestinal microbiota composition among patients with MS and T2D. Changes in microbial composition, particularly the Firmicutes/Bacteroidetes ratio, may serve as indicators of underlying pathology. At more specific taxonomic levels, several enriched taxa were identified in patients with MS, including Blautia, Tyzzerella, Dorea, Streptococcus, and Ruminococcus callidus. Additionally, species such as Dorea longicatena and Dialister invisus were enriched in prediabetic and diabetic patients, whereas beneficial genera (Eubacterium, Acidaminococcus, Bifidobacterium, and Ligilactobacillus) were more prevalent in healthy and prediabetic individuals than in those with T2D. | |
| dc.description.sponsorship | This work was supported by the CyTED, Spain and National Organism for Science and Technology, founding the IBEROBDIA project (P918PTE0409). This work was financially supported by the Spanish Ministry of Economy and Competitiveness through the State Program of I+D+I Oriented to the Challenges of Society 2017–2020 (International Joint Programming 2018), projects (PCI2018-093245, and PCI2018-093284). CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Ministry of Culture, Education, Vocational Training, and Universities and the Galician universities for the strengthening of research centers in the University System of Galicia (CIGUS). This work was supported by the CESGA (Centro de Supercomputación de Galicia), providing computing resources and related technical support that contributed to the research results reported in this article. Jose Liñares-Blanco’s work was financed by the Spanish Ministry of Universities by means of the Margarita Salas (RSUC.UDC.MS06) linked to the European Union through the NextGenerationEU program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | |
| dc.description.uri | https://doi.org/10.5281/zenodo.14051704 | |
| dc.description.uri | https://doi.org/10.6084/m9.figshare.26063020.v1 | |
| dc.description.uri | https://github.com/MALL-Machine-Learning-in-Live-Sciences/IBEROBDIA | |
| dc.description.uri | https://doi.org/10.7717/peerj.19108/supp-1 | |
| dc.identifier.citation | Sinisterra Loaiza LI, Fernández-Edreira D, Liñares-Blanco J, Cepeda A, Cardelle-Cobas A, Fernandez-Lozano C. 2025. Fecal microbiome analysis in patients with metabolic syndrome and type 2 diabetes. PeerJ 13:e19108 https://doi.org/10.7717/peerj.19108 | |
| dc.identifier.doi | 10.7717/peerj.19108 | |
| dc.identifier.issn | 2167-8359 | |
| dc.identifier.uri | https://hdl.handle.net/2183/45791 | |
| dc.language.iso | eng | |
| dc.publisher | PeerJ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PCI2018-093284/ES/OBESIDAD Y DIABETES EN IBEROAMERICA: FACTORES DE RIESGO Y NUEVOS BIOMARCADORES PATOGENICOS Y PREDICTIVOS | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PCI2018-093284/ES/OBESIDAD Y DIABETES EN IBEROAMERICA: FACTORES DE RIESGO Y NUEVOS BIOMARCADORES PATOGENICOS Y PREDICTIVOS | |
| dc.relation.uri | https://doi.org/10.7717/peerj.19108 | |
| dc.rights | Attribution 4.0 International | en |
| dc.rights.accessRights | open access | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Bioinformatics | |
| dc.subject | Microbiome | |
| dc.subject | Type 2 diabetes | |
| dc.subject | Metabolic syndrome | |
| dc.subject | Biomarkers | |
| dc.title | Fecal microbiome analysis in patients with metabolic syndrome and type 2 diabetes | |
| dc.type | journal article | |
| dc.type.hasVersion | VoR | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 2e10acd1-d5b3-4a87-9b9f-df8654c8a246 | |
| relation.isAuthorOfPublication | cf4ecc37-12be-45fc-add3-01c6a7f02630 | |
| relation.isAuthorOfPublication | e5ddd06a-3e7f-4bf4-9f37-5f1cf3d3430a | |
| relation.isAuthorOfPublication.latestForDiscovery | cf4ecc37-12be-45fc-add3-01c6a7f02630 |
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