Evidence map›Paper›PMID 40520642›Full record

ArticlePeerJ2025

Fecal microbiome analysis in patients with metabolic syndrome and type 2 diabetes.

Laura Isabel Sinisterra Loaiza, Diego Fernández-Edreira, Jose Liñares-Blanco, Alberto Cepeda, Alejandra Cardelle-Cobas, Carlos Fernandez-Lozano

Abstract read
In one paragraph

Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
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  5. Recent advances in the positive role ofFrontiers in immunology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Laura Isabel Sinisterra Loaiza *Department of Analytical Chemistry, Nutrition and Bromatology, Faculty of Veterinary Medicine, Universidade de Santiago de Compostela, Campus de Lugo, Lugo, Spain.
Diego Fernández-Edreira *Machine Learning in Life Sciences Laboratory, Department of Computer Science and Information Technologies, Universidade da Coruña (CITIC), A Coruña, Spain.ORCID 0000-0001-6906-3374
Jose Liñares-BlancoMachine Learning in Life Sciences Laboratory, Department of Computer Science and Information Technologies, Universidade da Coruña (CITIC), A Coruña, Spain.
Alberto CepedaDepartment of Analytical Chemistry, Nutrition and Bromatology, Faculty of Veterinary Medicine, Universidade de Santiago de Compostela, Campus de Lugo, Lugo, Spain.
Alejandra Cardelle-CobasDepartment of Analytical Chemistry, Nutrition and Bromatology, Faculty of Veterinary Medicine, Universidade de Santiago de Compostela, Campus de Lugo, Lugo, Spain.
Carlos Fernandez-LozanoMachine Learning in Life Sciences Laboratory, Department of Computer Science and Information Technologies, Universidade da Coruña (CITIC), A Coruña, Spain.ORCID 0000-0003-0413-5677

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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

Indexed as

Diabetes Mellitus, Type 2FecesGastrointestinal MicrobiomeMetabolic SyndromeAdultAgedCohort StudiesFemaleHumansMaleMiddle AgedRNA, Ribosomal, 16SSpainRNA, Ribosomal, 16SBioinformaticsBiomarkersMetabolic syndromeMicrobiomeType 2 diabetes

Identifiers

PMID40520642
PMCPMC12166849

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.