Bibliometric and Visual Analysis of the Immune System in Osteomyelitis (1990-2024)

Summary

Here, the authors present a protocol to perform a bibliometric and visual analysis of immune-related osteomyelitis research (1990–2024). This approach identifies global publication trends, collaborative networks, and thematic hotspots, providing a systematic overview of the transition toward immune-centric management in skeletal infections.

Abstract

Osteomyelitis (OM) is an inflammatory bone disorder in which dysregulation of the immune microenvironment significantly contributes to disease progression. This study conducts a bibliometric analysis to map the global landscape of immune-related OM research from 1990 to 2024. A total of 3,031 original articles and reviews were retrieved from the Web of Science Core Collection. Visualization software was used to evaluate publication trends, collaborative networks, and keyword evolution. Results indicate that annual publications increased steadily, with the United States and China emerging as primary research contributors. High-impact studies transitioned from investigating genetic-autoimmune links to exploring Staphylococcus aureus-host interactions and immune evasion. Disciplinary trends shifted from surgery toward immunology and computational biology. Keyword analysis revealed a shift from early infection-related terms to molecular immune mechanisms, including IL-1β and Th17 signaling. This analysis offers a comprehensive overview of the research transition from pathogen-focused approaches to immune-centric perspectives in the field of osteomyelitis.

Introduction

Osteomyelitis (OM) is an inflammatory bone disorder caused by microbial infection, pathologically characterized by progressive bone destruction and the involvement of adjacent soft tissues^1,2. The main treatment for OM typically involves a prolonged course of culture-guided antibiotics, often combined with surgical debridement to remove infected or dead bone tissue^3,4. Epidemiological studies identify Staphylococcus aureus hematogenous dissemination as the leading cause of OM⁵. With the growing prevalence of antibiotic resistance, the recurrence and disability rates associated with OM are increasing, particularly among elderly patients with comorbidities such as diabetes, vascular insufficiency, or neuropathy, posing a considerable burden on healthcare systems⁶.

Recent clinical observations have revealed overlaps between OM and autoinflammatory syndromes, suggesting that immune dysregulation may play a critical role in disease pathogenesis⁷. Dysregulation of the immune microenvironment is now recognized as a key pathological driver in chronic and refractory OM. In addition to immune microenvironment dysregulation, aberrant bone metabolism, such as impaired osteogenic differentiation, which may be linked to specific cellular death processes like ferroptosis, is increasingly recognized as a contributor to the refractory nature of OM⁸. Research has shown that pathogens can impair neutrophil chemotaxis via virulence factors and induce macrophage polarization. While acute exposure to pathogen-associated molecular patterns typically triggers M1 macrophage polarization to facilitate microbial clearance, persistent infections and biofilms often induce a transition toward an immunosuppressive M2-like phenotype, fostering an immunosuppressive environment^9,10. The immune response during bone infection is highly complex: the abnormal expansion of myeloid-derived suppressor cells inhibits T cell activation, resulting in T helper cell dysfunction, while increased B cell apoptosis undermines humoral immune surveillance¹¹. Moreover, recent genetic studies implicate various immune cell types, including memory B cells, in elevated OM risk¹². Given the substantial societal and clinical impact of OM, it is essential to elucidate the underlying mechanisms of immune interactions to facilitate the development of targeted therapeutic strategies.

Bibliometric analysis is a methodological approach used to assess the characteristics of scholarly output within a specific research domain¹³, aiming to identify key trends and thematic concentrations in the literature¹⁴. Unlike traditional research methods, bibliometric analysis employs visualization tools to explore academic publications, offering deeper insight and significant analytical advantages¹⁵. This approach enables the evaluation of academic influence, the identification of research hotspots and emerging themes, and the mapping of historical and current focal areas, thereby providing valuable guidance for future investigations. Although the immunological dimensions of OM have garnered increasing attention in recent years, previous bibliometric studies in the field of bone infection have focused primarily on general surgical techniques or antibiotic materials. A systematic analysis focusing specifically on the immune microenvironment and host-pathogen interactions is currently lacking. This study is timely and necessary to clarify how immunological research has evolved and to identify gaps for future host-directed therapies. To address this gap, the authors conducted a bibliometric analysis using the Web of Science Core Collection database covering the period from January 1, 1990, to December 31, 2024. The authors hypothesize that research focus has transitioned from phenotypic descriptions of bacterial infection toward a molecular understanding of immune microenvironment dysregulation, marking a shift toward precision management in OM.

Protocol

All bibliographical data used in this study were retrieved from publicly available databases (Web of Science Core Collection). This research did not involve human participants or animal subjects; therefore, ethical approval and informed consent were not required for this analysis.

Data acquisition and search strategy
Relevant studies on the genetic and immunological mechanisms of osteomyelitis were retrieved from online bibliographic databases (see Table of Materials), specifically the Science Citation Index Expanded and the Social Sciences Citation Index, which are widely used in bibliometric research. The database was selected due to its comprehensive coverage of peer-reviewed literature and high accuracy in journal classification^16,17. The search strategy employed the Topic (TS) field, which encompasses the Title, Abstract, Author Keywords, and Keywords Plus. The keywords used were as follows: TS = (Osteomyelitis OR Osteomyelitides) and TS = (immune* OR "T cell" OR "B CELL" OR "NK CELL" OR neutrophil OR macrophage OR "Treg Cell"). All English-language original articles and reviews indexed from January 1, 1990, to December 31, 2024, were included. While the search period covered research through the end of 2024, the final data export was performed on April 15, 2025, to ensure that all late-indexed publications from the 2024 period were captured. No automated screening tools were used. Bibliometric data were exported in "plain text" format, including full records and cited references. Two researchers independently conducted the search, and any discrepancies were resolved through discussion. All data utilized in this study were obtained from publicly available databases; therefore, ethical approval was not required for this analysis.

Data cataloging and standardization
Spreadsheet software (refer to Table of Materials) was used to catalog essential information, including titles, affiliations, publication sources (journals or books), authors, references, subject categories, and keywords. During processing, data cleaning was performed by standardizing keyword synonyms (e.g., merging "S. aureus" and "Staphylococcus aureus") and consolidating institutional names. This standardization ensures that the analytical software correctly identifies collaborative links and thematic clusters without redundancy.

Software-based bibliometric analysis
Bibliometric analysis was performed using a combination of specialized visualization and statistical tools. Bibliometric visualization VOSviewer (version 1.6.20) (refer to Table of Materials) was used to generate collaborative networks among countries and institutions. For these visualizations, a minimum threshold (e.g., 5 or 10 publications per node) was set to maintain clarity in the graphical representation. Bibliometric visualization CiteSpace (version 6.2.R2) (refer to Table of Materials) was utilized for reference co-citation and keyword burst analysis. The time slicing was set to 1 year per slice for the 35-year study period. The selection criteria utilized the g-index with a scale factor of k = 25. To identify research hotspots, the citation-burst sensitivity (gamma) was set to 0.7, and the minimum burst duration was set to 2 years. Statistical software (refer to Table of Materials) was used to evaluate publication trends and disciplinary distributions, while bibliometric analysis software (refer to Table of Materials) was used to evaluate citation frequencies. A knowledge synthesis approach was applied to integrate and summarize the key thematic terms identified¹⁸. All statistical procedures were independently performed by two researchers, with disagreements resolved through discussion to ensure accurate replication of the findings.

Results

General features of publications
A total of 3,658 publications were initially retrieved. Following the independent screening and removal of ineligible records, 3,031 articles were included in the bibliometric analysis, comprising 2,480 original research articles and 551 reviews. A detailed breakdown of the 627 excluded records identified 212 meeting abstracts, 154 editorial materials, 118 letters, 85 book chapters, 42 early access items, and 16 non-English publications. These publications were author...

Discussion

This bibliometric study provides a systematic evaluation of the global research landscape concerning the immune system in osteomyelitis, identifying a significant transition from pathogen-focused investigations toward a molecular understanding of the immune microenvironment. While previous bibliometric analyses in the field of bone infection focused on general surgical techniques or antibiotic materials, our findings emphasize a profound restructuring of disciplinary boundaries, in which immunology and computational biol...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
VOSviewer (version 1.6.20)	Leiden University's Centre for Science and Technology Studies (CWTS)	NA	Used alongside other tools to conduct data analysis and generate visual and graphical representations (e.g., figures mapping the top subject categories and keywords with the strongest citation bursts).
CiteSpace (version 6.2.R2 Basic)	Dr. Chaomei Chen.	NA	Used alongside other tools to conduct data analysis and generate visual and graphical representations (e.g., collaboration networks, citation networks).
R (version 4.3.1)	The R Project for Statistical Computing (R Foundation)	NA	Used alongside other tools to conduct data analysis and generate visual and graphical representations.
Hiscite Pro (version 2.0)	Institute for Scientific Information	NA	Used alongside other tools to conduct data analysis and generate visual and graphical representations.
Microsoft Excel	Microsoft Corporation	NA	Used to catalog information such as titles, affiliations, publication sources, authors, references, subject categories, and keywords.