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. 2024 May 28:15:1319654.
doi: 10.3389/fmicb.2024.1319654. eCollection 2024.

Microbial dysbiosis in systemic lupus erythematosus: a scientometric study

Affiliations

Microbial dysbiosis in systemic lupus erythematosus: a scientometric study

Miaomiao Zhao et al. Front Microbiol. .

Abstract

Introduction: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease. Mounting evidence suggests microbiota dysbiosis augment autoimmune response. This study aims to provide a systematic overview of this research field in SLE through a bibliometric analysis.

Methods: We conducted a comprehensive search and retrieval of literature related to microbial researches in SLE from the Web of Science Core Collection (WOSCC) database. The retrieved articles were subjected to bibliometric analysis using VOSviewer and Bibliometricx to explore annual publication output, collaborative patterns, research hotspots, current research status, and emerging trends.

Results: In this study, we conducted a comprehensive analysis of 218 research articles and 118 review articles. The quantity of publications rises annually, notably surging in 2015 and 2018. The United States and China emerged as the leading contributors in microbial research of SLE. Mashhad University of Medical Sciences had the highest publication outputs among the institutions. Frontiers in Immunology published the most papers. Luo XM and Margolles A were the most prolific and highly cited contributors among individual authors. Microbial research in SLE primarily focused on changes in microbial composition, particularly gut microbiota, as well as the mechanisms and practical applications in SLE. Recent trends emphasize "metabolites," "metabolomics," "fatty acids," "T cells," "lactobacillus," and "dietary supplementation," indicating a growing emphasis on microbial metabolism and interventions in SLE.

Conclusion: This study provides a thorough analysis of the research landscape concerning microbiota in SLE. The microbial research in SLE mainly focused on three aspects: microbial dysbiosis, mechanism studies and translational studies (microbiota-based therapeutics). It identifies current research trends and focal points, offering valuable guidance for scholars in the field.

Keywords: VOSviewer; autoimmune disease; bibliometric analysis; microbiota; systemic lupus erythematosus.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The study PRISMA flow diagram.
Figure 2
Figure 2
Quantities of publications output of the microbial research in SLE from 1991 to 2022.
Figure 3
Figure 3
Analysis of publications on microbial research in SLE by the countries/regions. (A) Visualization of papers according to the country of the corresponding authors. MCP, Multiple Country Publications; SCP, Single Country Publications. (B) Visualization of papers according to the cooperation networks of the corresponding authors.
Figure 4
Figure 4
Analysis of publications on microbial research in SLE by the affiliations. (A) Visualization of papers according to the affiliations. (B) Visualization of papers according to the cooperation networks of the affiliations.
Figure 5
Figure 5
Analysis of publications on microbial research in SLE by the authors.
Figure 6
Figure 6
Cluster visualization of the co-occurrence network according to the authors’ keywords. The minimum number of occurrences of the author’s keywords was set to 5. Finally, 101 keywords were included in the co-occurrence network. These keywords were divided into seven different clusters.
Figure 7
Figure 7
Analysis of hotspot and topic migration on microbial research in SLE. (A) The density visualization of keywords co-occurrence network (the figure represented the numbers of keyword occurrences). (B) The timestamp visualization of keywords co-occurrence network. The co-occurrence network was based on time characteristics, and each node was color-coded with a different color based on the average time multiple of the keywords. (C) The historical migration of research hotspots.

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