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Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis

OBJECTIVE: To conduct the first thorough bibliometric analysis to evaluate and quantify global research regarding to the gut microbiota and type 1 diabetes (T1D). METHODS: A literature search for research studies on gut microbiota and T1D was conducted using the Web of Science Core Collection (WoSCC...

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Detalles Bibliográficos
Autores principales: Guo, Keyu, Li, Jiaqi, Li, Xia, Huang, Juan, Zhou, Zhiguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10033956/
https://www.ncbi.nlm.nih.gov/pubmed/36970681
http://dx.doi.org/10.3389/fmicb.2023.1137595
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author Guo, Keyu
Li, Jiaqi
Li, Xia
Huang, Juan
Zhou, Zhiguang
author_facet Guo, Keyu
Li, Jiaqi
Li, Xia
Huang, Juan
Zhou, Zhiguang
author_sort Guo, Keyu
collection PubMed
description OBJECTIVE: To conduct the first thorough bibliometric analysis to evaluate and quantify global research regarding to the gut microbiota and type 1 diabetes (T1D). METHODS: A literature search for research studies on gut microbiota and T1D was conducted using the Web of Science Core Collection (WoSCC) database on 24 September 2022. VOSviewer software and the packages Bibliometrix R and ggplot used in RStudio were applied to perform the bibliometric and visualization analysis. RESULTS: A total of 639 publications was extracted using the terms “gut microbiota” and “type 1 diabetes” (and their synonyms in MeSH). Ultimately, 324 articles were included in the bibliometric analysis. The United States and European countries are the main contributors to this field, and the top 10 most influential institutions are all based in the United States, Finland and Denmark. The three most influential researchers in this field are Li Wen, Jorma Ilonen and Mikael Knip. Historical direct citation analysis showed the evolution of the most cited papers in the field of T1D and gut microbiota. Clustering analysis defined seven clusters, covering the current main topics in both basic and clinical research on T1D and gut microbiota. The most commonly found high-frequency keywords in the period from 2018 to 2021 were “metagenomics,” “neutrophils” and “machine learning.” CONCLUSION: The application of multi-omics and machine learning approaches will be a necessary future step for better understanding gut microbiota in T1D. Finally, the future outlook for customized therapy toward reshaping gut microbiota of T1D patients remains promising.
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spelling pubmed-100339562023-03-24 Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis Guo, Keyu Li, Jiaqi Li, Xia Huang, Juan Zhou, Zhiguang Front Microbiol Microbiology OBJECTIVE: To conduct the first thorough bibliometric analysis to evaluate and quantify global research regarding to the gut microbiota and type 1 diabetes (T1D). METHODS: A literature search for research studies on gut microbiota and T1D was conducted using the Web of Science Core Collection (WoSCC) database on 24 September 2022. VOSviewer software and the packages Bibliometrix R and ggplot used in RStudio were applied to perform the bibliometric and visualization analysis. RESULTS: A total of 639 publications was extracted using the terms “gut microbiota” and “type 1 diabetes” (and their synonyms in MeSH). Ultimately, 324 articles were included in the bibliometric analysis. The United States and European countries are the main contributors to this field, and the top 10 most influential institutions are all based in the United States, Finland and Denmark. The three most influential researchers in this field are Li Wen, Jorma Ilonen and Mikael Knip. Historical direct citation analysis showed the evolution of the most cited papers in the field of T1D and gut microbiota. Clustering analysis defined seven clusters, covering the current main topics in both basic and clinical research on T1D and gut microbiota. The most commonly found high-frequency keywords in the period from 2018 to 2021 were “metagenomics,” “neutrophils” and “machine learning.” CONCLUSION: The application of multi-omics and machine learning approaches will be a necessary future step for better understanding gut microbiota in T1D. Finally, the future outlook for customized therapy toward reshaping gut microbiota of T1D patients remains promising. Frontiers Media S.A. 2023-03-09 /pmc/articles/PMC10033956/ /pubmed/36970681 http://dx.doi.org/10.3389/fmicb.2023.1137595 Text en Copyright © 2023 Guo, Li, Li, Huang and Zhou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Guo, Keyu
Li, Jiaqi
Li, Xia
Huang, Juan
Zhou, Zhiguang
Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis
title Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis
title_full Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis
title_fullStr Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis
title_full_unstemmed Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis
title_short Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis
title_sort emerging trends and focus on the link between gut microbiota and type 1 diabetes: a bibliometric and visualization analysis
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10033956/
https://www.ncbi.nlm.nih.gov/pubmed/36970681
http://dx.doi.org/10.3389/fmicb.2023.1137595
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