Guo Keyu, Li Jiaqi, Li Xia, Huang Juan, Zhou Zhiguang
National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China.
Section of Endocrinology, Department of Internal Medicine, School of Medicine, Yale University, New Haven, CT, United States.
Front Microbiol. 2023 Mar 9;14:1137595. doi: 10.3389/fmicb.2023.1137595. eCollection 2023.
To conduct the first thorough bibliometric analysis to evaluate and quantify global research regarding to the gut microbiota and type 1 diabetes (T1D).
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.
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."
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.
进行首次全面的文献计量分析,以评估和量化全球关于肠道微生物群与1型糖尿病(T1D)的研究。
于2022年9月24日使用科学网核心合集(WoSCC)数据库对肠道微生物群和T1D的研究进行文献检索。应用VOSviewer软件以及RStudio中使用的Bibliometrix R和ggplot程序包进行文献计量和可视化分析。
使用“肠道微生物群”和“1型糖尿病”(及其医学主题词表中的同义词)检索词共提取出639篇出版物。最终,324篇文章纳入文献计量分析。美国和欧洲国家是该领域的主要贡献者,排名前十的最具影响力机构均位于美国、芬兰和丹麦。该领域最具影响力的三位研究人员是李文、约尔马·伊洛宁和米凯尔·克尼普。历史直接引文分析显示了T1D和肠道微生物群领域被引用最多的论文的演变。聚类分析确定了七个聚类,涵盖了T1D和肠道微生物群基础及临床研究的当前主要主题。2018年至2021年期间最常见的高频关键词是“宏基因组学”、“中性粒细胞”和“机器学习”。
多组学和机器学习方法的应用将是未来更好理解T1D中肠道微生物群的必要步骤。最后,针对重塑T1D患者肠道微生物群的定制疗法的未来前景依然广阔。