Mapping the Knowledge Landscape of Atopic Dermatitis and Allergic Rhinitis Comorbidity: A Bibliometrics Study

Summary

Atopic dermatitis and allergic rhinitis are common comorbid conditions, yet research on their co-occurrence remains to be fully elucidated. This study aims to employ a bibliometric approach to map the knowledge structure, identify research hotspots, and trace the evolving trends in this field.

Abstract

Atopic dermatitis (AD) and allergic rhinitis (AR) are common allergic diseases that frequently co-occur, sharing common underlying pathophysiological mechanisms. This study retrieved comorbidity-related publications on AD and AR from the Web of Science (WOS) database over the past decade. Bibliometric analysis was performed using software tools including Microsoft Excel, VOSviewer, CiteSpace, and Scimago Graphica to examine countries, research institutions, journals, authors, and keywords. The results indicate that a total of 1571 relevant publications were published in the past 10 years, showing a fluctuating upward trend. The United States contributed the most publications (244 articles), cluster analysis identified 3 major research hubs, represented by the United States, China, and Germany. In terms of research institutions, China Medical University was the most productive (42 publications). Cluster analysis revealed 4 primary institutional research centers, represented by the University of Zurich, China Medical University, Seoul National University, and the University of Colorado. Among journals, Allergy published the most articles (56), while the Journal of Allergy and Clinical Immunology demonstrated the strongest academic influence, with an average of 78.77 citations per article. Regarding authors, Akdis, Cezmi A., had the highest output (15 publications), while Irvine, Alan D., exhibited the strongest impact, with a total citation count of 2711. Keyword analysis identified 11 clusters, which were primarily associated with immune regulation. This protocol provides researchers with an effective strategy to identify potential research gaps and guide future studies in these interrelated allergic diseases.

Introduction

Atopic Dermatitis (AD) and Allergic Rhinitis (AR) are two common chronic inflammatory allergic diseases, which together form the core components of the atopic march^1,2,3. Atopic Dermatitis is a chronic inflammatory skin disease associated with a genetic predisposition to allergy, characterized by impaired skin barrier function, immune dysregulation, and intense pruritus^4,5. Allergic rhinitis is an IgE-mediated chronic inflammation of the nasal mucosa in response to environmental allergens, typically presenting with nasal congestion, rhinorrhea, nasal itching, and sneezing⁶. In recent years, extensive epidemiological and clinical studies have confirmed a significant comorbidity between Atopic Dermatitis and Allergic rhinitis^7,8. Patients often develop these two conditions simultaneously or sequentially, which not only increases the disease burden but also leads to a significant decline in quality of life and higher healthcare costs. Concurrently, the association between the two diseases provides important clues for research into their pathogenesis. It is widely accepted in the academic community that these two diseases share complex genetic backgrounds, immunological mechanisms, and environmental factors⁹. However, although substantial progress has been made in basic and clinical research on either Atopic Dermatitis or Allergic Rhinitis, systematic and macro-level studies on their comorbidity remain relatively limited.

Bibliometrics, as a scientific discipline that employs mathematical and statistical methods to quantitatively analyze academic literature, demonstrates unique advantages¹⁰. It transcends the subjective limitations inherent in traditional reviews by analyzing data such as publication counts, authors, institutions, national/regional collaborations, keyword co-occurrence, and document co-citations¹¹. This allows for an objective and systematic revelation of the research output, knowledge structure, evolutionary dynamics, and future directions within a specific field¹². Applying bibliometrics to the research domain of Atopic Dermatitis (AD) and Allergic Rhinitis (AR) comorbidity can help researchers quickly grasp the overall landscape of this field. This study integrates bibliometric methods to systematically review the literature related to AD and AR over the past decade. This study contributes a reproducible bibliometric protocol that integrates multiple software tools to systematically process and analyze literature data. Through this data-driven approach, we comprehensively present the knowledge structure of the AD and AR comorbidity field, including research hotspots, collaboration networks, and thematic evolution over the past decade. This methodological framework moves beyond traditional narrative reviews by providing an objective and quantitative synthesis of the literature, offering researchers a valuable tool for understanding the current state and future directions of this field.

Protocol

1. Literature search and acquisition

1. Access the Medical Subject Headings (MeSH) database (https://meshb.nlm.nih.gov/).
2. Enter the terms "Atopic Dermatitis" and "Allergic Rhinitis" separately to retrieve their respective controlled vocabulary terms and subheadings.
3. Based on the identified MeSH terms and subheadings, formulate a comprehensive search strategy.
4. Navigate to the Web of Science (WoS) database (https://www.webofscience.com/wos/). Select the "Advanced Search" option.
5. Input the following search strategy into the query box: TS=("Atopic Dermatitis" OR "Atopic Eczema" OR "Infantile Eczema" OR "Atopic Neurodermatitis" OR "Disseminated Neurodermatitis") AND TS=("Allergic Rhinitis"). Limit the publication date range from January 1, 2015, to December 31, 2024.
6. Exclude non-research document types such as meeting abstracts, conference papers, editorial materials, book chapters, and retracted publications; retain only English-language literature.
7. For publications with inconsistent author names or institutional affiliations, use the affiliation of the first author as the standard to ensure consistency in network analyses.
8. Export the obtained dataset in plain text format and store it on the computer for subsequent analysis.
9. Record the annual number of published articles and click on 'Citation Report' to export the citation counts.
10. Click "See all" in the "Countries/Regions" section and export the national publication output to Excel.
11. Under the "Publication Years" column, click "See all", then sequentially select "2015" and "Refine". Next, click "See all" in the "Countries/Regions" column.
12. Record the specific publication counts for the top 10 countries in the year 2015.
13. Following Step 10, record the annual publication counts for the top 10 countries from 2015 to 2024.

2. Trend analysis of annual publication output and citation frequency

1. Open Microsoft Excel and sequentially enter the year, the corresponding number of published articles, and the citation count.
2. Select the input data, then navigate to Insert > Chart > Combo Chart. Set the annual number of published articles as a Column Chart, and the citation count as a Line Chart.
3. Select the chart and label the X-axis title as "Year" and the Y-axis title as "Publications".
4. Select the data labels and set them to display the specific values.
5. Select the figure and save it in TIFF format.

3. National publication output and cross-border research networks

NOTE: VOSviewer was used to construct the national collaboration network due to its strength in bibliometric network visualization. Scimago Graphica was subsequently employed to generate world maps for clearer geographic presentation of the network data.

1. Launch the software, then navigate through and click: Create → Create a map based on bibliographic data → Read data from bibliographic database files.
2. Import the plain text dataset, then click 'Next' to read the data.
3. In the "Type of analysis" field, select "Co-authorship"; then choose "Countries" under "Unit of analysis" and click "Next". Set the value in "Number of organizations to be selected" to 22, proceed by clicking "Next", and initiate the data analysis.
4. Right-click on the data in the "Source" column and export metrics, including national publication output and citation counts for subsequent data analysis.
5. Click "Finish" to generate the national collaboration network, then select the "Items" tab in the left toolbar to view the number of clusters.
6. Navigate to "Analysis" in the left toolbar, select "Minimum cluster size", and adjust this parameter to maintain the number of clusters within an appropriate range.
7. Access the "Analysis" and then "Layout" sections within the left toolbar. Adjust the numerical values for the "Attraction" and "Repulsion" parameters to optimize the scale of the visualization.
8. Select "Visualization" on the right to modify the appearance of the visual map. Drag the "Scale" slider to adjust country/region labels to an appropriate size, use the "Labels" control to set node dimensions, manipulate the "Lines" parameter to achieve suitable connection line thickness, and click "Curved lines" to select straight or curved lines for the connections.
9. Navigate to File > Screenshot > Save to export the visualization; then select File > Save > GML to preserve the GML format file for subsequent use.
10. Launch the software and import the GML format file.
11. Set the "lable" column as "Country", change the data type of the "cluster" column to "String", and click the visualization icon in the upper-left corner.
12. Drag "weight<documents>" from the left panel to the "Size" box, move "cluster" to the "Color" box, and place "lable" into the "Label", "Tooltip", and "Unit" boxes respectively.
13. Select "Map" under the "Marks" section in the right toolbar to generate the world map; then use the "Edges" option to adjust connection line curvature and modify "Edge color" to configure node colors.
14. Select "Color" in the left toolbar to adjust cluster colors; then configure "Legend position" to modify the placement of the legend.
15. Click "Export" in the top toolbar to save the image in PNG format.

4. Analysis of institutional publication output and collaboration networks

NOTE: VOSviewer was chosen for institutional collaboration analysis because it effectively handles large-scale bibliometric data and provides a clear visualization of institutional clusters.

1. Launch the software, then navigate through and click: Create → Create a map based on bibliographic data → Read data from bibliographic database files.
2. Import the plain text dataset, then click 'Next' to read the data.
3. In the "Type of analysis" field, select "Co-authorship"; then choose "Organizations" under "Unit of analysis" and click "Next". Set the value in "Number of organizations to be selected" to 50, proceed by clicking "Next", and initiate the data analysis.
4. Right-click on the data in the "Source" column and export metrics, including institutional publication output and citation counts for subsequent data analysis.
5. Click "Finish" to generate the institutional collaboration network, then select the "Items" tab in the left toolbar to view the number of clusters.
6. Navigate to "Analysis" in the left toolbar, select "Minimum cluster size", and adjust this parameter to maintain the number of clusters within an appropriate range.
7. Access the "Analysis" and then "Layout" sections within the left toolbar. Adjust the numerical values for the "Attraction" and "Repulsion" parameters to optimize the scale of the visualization.
8. Select "Visualization" on the right to modify the appearance of the visual map. Drag the "Scale" slider to adjust institution names to an appropriate size, use the "Labels" control to set node dimensions, manipulate the "Lines" parameter to achieve suitable connection line thickness, and click "Curved lines" to select straight or curved lines for the connections.
9. Click "File", then "Screenshot", and finally "Save" to export the visualization.
10. Launch Microsoft Excel, sort the research institutions by publication output and citation counts, select the top 10 institutions, identify their respective countries, and prepare this dataset for visual presentation.
11. Select the three data columns containing the top 10 research institutions by publication output, their respective publication counts, and host countries. Then navigate through Insert > Charts > Column Chart to create a visualization of the top 10 research institutions ranked by publication volume.
12. Select the data labels to display the actual values, and export the chart in TIFF format.
13. Select the three data columns containing the top 10 research institutions by citation count, their corresponding citation numbers, and host countries. Then navigate through Insert > Charts > Column Chart to create a visualization of the top 10 research institutions ranked by citation count.

5. Journal publication output and citation analysis

NOTE: Perform analysis of journal publication output and citation using VOSviewer software.

1. Launch the software, then navigate through and click: Create → Create a map based on bibliographic data → Read data from bibliographic database files.
2. Import the plain text dataset, then click 'Next' to read the data.
3. In the "Type of analysis" section, select "Citation"; then choose "Sources" under "Unit of analysis" and click "Next". Set the value in "Minimum number of documents of a source" field to 5, set "Minimum number of citations of a source" to 0, click "Next", and proceed with data analysis.
4. Right-click on the data in the "Source" column and select the "Save As" option to export both the journal publication output and citation counts for subsequent data analysis.
5. Click "Finish" to generate the journal collaboration network map, then select the "Items" tab in the left toolbar to view the cluster count.
6. Click "Analysis" in the left toolbar and set the "Minimum cluster size" parameter to maintain the number of clusters within an appropriate range.
7. Navigate to "Analysis" and then "Layout" in the left toolbar. Adjust the values for the "Attraction" and "Repulsion" parameters to achieve an optimally scaled visualization.
8. Select "Visualization" on the right to modify the appearance of the visual map. Drag the "Scale" slider to adjust journal names to an appropriate size, use the "Labels" control to set node dimensions, manipulate the "Lines" parameter to achieve suitable connection line thickness, and click "Curved lines" to select straight or curved lines for the connections.
9. Click "File", then "Screenshot", and finally "Save" to export the visualization.
10. Launch Microsoft Excel and calculate the average citations per journal based on publication output and citation counts.
11. Select the three columns of data (journal titles, publication output, and average citations), then navigate to Insert > Charts > Column Chart to create a visualization comparing journal publication output and average citation rates.
12. Select the data labels to display the actual values, then export the chart in TIFF format.

6. Collaboration network analysis of high-productivity authors

NOTE: Perform collaboration network analysis of high-productivity and highly-cited authors using VOSviewer software.

1. Launch the software, then navigate through and click: Create → Create a map based on bibliographic data → Read data from bibliographic database files.
2. In the "Type of analysis" field, select "Co-authorship"; then choose "Authors" under "Unit of analysis" and click "Next". Set the value in "Number of organizations to be selected" to 95, proceed by clicking "Next", and initiate the data analysis.
3. Right-click the data in the "Source" column and export metrics, including publication counts and citation numbers of high-productivity authors, for subsequent data.
4. After clicking "Finish" to generate the institutional collaboration network, select the "Items" tab in the left toolbar to view the cluster count.
5. After clicking "Finish" to generate the author collaboration network, select the "Items" tab in the left toolbar to view the cluster count.
6. Navigate to "Analysis" and then "Layout" in the left toolbar. Adjust the values for the "Attraction" and "Repulsion" parameters to achieve an optimally scaled visualization.
7. Select "Visualization" on the right to modify the appearance of the visual map. Drag the "Scale" slider to adjust journal names to an appropriate size, use the "Labels" control to set node dimensions, manipulate the "Lines" parameter to achieve suitable connection line thickness, and click "Curved lines" to select straight or curved lines for the connections.
8. Navigate to File > Screenshot > Save to export the visualization.
9. Launch Microsoft Excel, sort the high-productivity authors by publication output, select the top 10 authors, and prepare this selection for visual presentation.
10. Select the top 10 high-productivity authors and their corresponding publication counts. Then navigate through Insert > Charts > Column Chart to create a visualization with author names on the horizontal axis and publication counts on the vertical axis. Enable the display of data labels. Finally, right-click the chart and save it in TIFF format.

7. Keyword co-occurrence and thematic evolution analysis

NOTE: CiteSpace was selected for keyword analysis due to its advanced capabilities in detecting research hotspots, cluster analysis, and temporal evolution patterns.

1. Launch the software, duplicate records were identified and removed using CiteSpace's deduplication function. Navigate to Data > Import/Export, set the Input Directory to the "input" folder, and configure the Output Directory to the "output" folder.
2. Navigate to Deduplication & Organization, select both "Article" and "Review" document types, and click "Start". Then import the plain text dataset and proceed by clicking "Start" again.
3. Click "Close" to exit the settings page and return to the main interface.
4. Click "New" on the main interface, set the Project Home to the "project" folder, configure the Data Directory to the "data" folder, select "WoS" for Data Source and "English" for Preferred Language, and finally save the settings.
5. In the timeslice panel on the right, set the time range from January 2015 to December 2024. Select "Keyword" as the node type. Choose the g-index (k=25) for network scaling. Enable the "Pathfinder", "Pruning sliced networks", and "Pruning the merged network" options in the pruning settings to simplify and clarify the network visualization.
6. Click "Start" to initiate data processing. After the operation is complete, select the "One-Click Clustering Label Optimization" function to generate the keyword clustering diagram.
7. Adjust the node labels and cluster labels in the right control panel to modify elements such as node size in the visualization.
8. Navigate to Toolbar > Label > Label Color > Article Labels to customize the label colors; then select the Node option in the toolbar to modify node attributes such as colors and shapes.
9. After completing the adjustments, navigate to File > Save As and export the image in PNG format.
10. Navigate to Layout > Timeline in the control panel to generate the keyword timeline view.
11. In the control panel, adjust the node labels and cluster labels to modify the node size and cluster label size.
12. Navigate to File and save the image in PNG format.
13. Navigate to Burstness in the control panel, sequentially click Refresh and View, enter "20" in the data field, confirm the selection, and generate a research hotspot analysis diagram of the top 20 keywords.
14. Capture and save the research hotspot analysis diagram of the top 20 keywords using a computer screenshot tool.

Results

Over the past decade, 1,571 articles on atopic dermatitis and allergic rhinitis have been published. The annual publication volume showed an overall upward trend, while citation frequency demonstrated steady growth (Figure 1). In terms of countries, the United States published the most articles (244). Further visualization of research clusters and collaborative networks among countries revealed three major national research clusters, represented by the United States, China, and Germany. The overall research network structure presented a multi-center model (Figure 2). Regarding institutions, China Medical University was the most prolific research institution (42 publications) (Figure 3). The top 10 institutions by publication volume are listed in Table 1. Further cluster analysis identified four main research clusters, represented by the University of Zurich, China Medical University, Seoul National University, and the University of Colorado. Concerning journals, the top 10 journals by publication volume are listed in Table 2. The Journal of Allergy and Clinical Immunology had the highest average citations per article (78.77). The journal collaboration network was visualized using VOSviewer. The most prolific author was Akdis, Cezmi A., with 15 published research papers, which were cited 2,246 times. Although Irvine, Alan D., published only 5 papers, they received the highest number of citations, reaching 2,711. Together with Akdis, Cezmi A., they formed the central hub of the author collaboration network and maintained close collaborative relationships with other authors. The relevant author collaboration network is shown in Figure 4. Co-occurrence and cluster analysis of keywords identified a total of 11 distinct keyword clusters (Figure 5A). These clusters primarily encompass themes related to immune regulation, genetics, epidemiology, environmental factors, and treatment strategies, with immune regulation emerging as the central focus. The keyword timeline graph is shown in Figure 5B, and the keyword research hotspot map is shown in Figure 5C.

Figure 1: Annual publications and citations (2015–2024). Bar chart showing the number of publications per year (blue bars) and line graph showing total citation counts per year (red line), indicating an overall upward trend in both metrics over the past decade. Please click here to view a larger version of this figure.

Figure 2: Collaboration network among countries/regions. Network visualization generated by VOSviewer showing international collaborative relationships. Nodes represent countries/regions, node size reflects publication output, and connecting lines indicate collaborative strength. Please click here to view a larger version of this figure.

Figure 3: Inter-institutional collaboration network. Network map displaying collaborative relationships among research institutions. Different colors represent different clusters, node size corresponds to publication volume, and line thickness indicates collaboration intensity. Please click here to view a larger version of this figure.

Figure 4: Collaboration network among authors. Author collaboration network visualization. Larger nodes represent authors with higher publication output, and connecting lines demonstrate co-authorship relationships. Please click here to view a larger version of this figure.

Figure 5: Keyword analysis. (A) Keyword clustering. (B) Keyword timeline. (C) Keyword burst detection. (A) Keyword clustering map showing 11 distinct research clusters identified through CiteSpace analysis. (B) Keyword timeline visualization displaying the temporal evolution of major research themes from 2015 to 2024. (C) Keyword burst detection map highlighting the top 20 keywords with the strongest citation bursts, indicating research hotspots over time. Please click here to view a larger version of this figure.

Institution	Documents	Country
China Medical University	42	China
Seoul National University	38	South Korea
Chang Gung University	36	Taiwan,China
Yonsei University	35	South Korea
Hallym University	33	South Korea
KyungHee University	29	South Korea
The Catholic University of Korea	29	South Korea
Colorado State University	25	USA
University of Zurich	24	Switzerland
National Yang Ming University	24	Taiwan,China

Table 1: Top 10 institutions by publication volume. Ranking of the most productive research institutions in the field of AD and AR comorbidity, including their publication counts and respective countries.

Source	Documents	Citations	Average Citations	IF(2024)	JCR Partition (2024)
Allergy	56	3856	68.86	12	Q1
Journal of Allergy and Clinical Immunology	35	2757	78.77	11.2	Q1
Journal of Allergy and Clinical Immunology-In practice	33	1225	37.12	6.6	Q1
Pediatric Allergy and Immunology	46	968	21.04	4.5	Q1
Allergology International	18	870	48.33	6.7	Q1
Clinical and Experimental Allergy	21	824	39.24	5.2	Q1
Frontiers in Immunology	28	806	28.79	5.9	Q1
Annals of Allergy Asthma & Immunology	22	799	36.32	4.7	Q1
International Journal of Molecular Sciences	16	608	38	4.9	Q1
Current Allergy and Asthma Reports	15	553	36.87	4.6	Q2

Table 2: Top 10 journals by publication volume. List of journals with the highest number of publications on AD and AR comorbidity, including their publication counts and average citations per article.

Discussion

The bibliometric analysis conducted in this study provides a comprehensive mapping of the research landscape on the comorbidity between AD and AR. This approach not only highlights global research trends but also identifies key contributors, emerging themes, and potential future directions for research in this important field.

Over the past decade, publications on the comorbidity of AD and AR have shown a fluctuating upward trend, reflecting growing global interest in this field. However, after peaking in 2021 (182 publications), it declined before returning to its peak in 2024. Reviewing other bibliometric studies reveals that the decline in related research observed in 2021 was a common phenomenon^13,14,15. This decline may be associated with the COVID-19 pandemic. At the National Level, the United States has been the leading contributor, publishing the most articles, followed by China and Germany. These trends demonstrate the active role of international collaborations, especially between research hubs in North America, Europe, and Asia. Institutions such as China Medical University and the University of Zurich emerged as key research centers, fostering substantial academic exchange and driving forward the knowledge base in this domain. The cluster analysis of countries, institutions, and authors revealed distinct research hubs, further underscoring the field's collaborative nature. For instance, the collaborative efforts between countries such as the United States, China, and Germany suggest a strong, interconnected research network. In the future, cooperation between countries and institutions should be further strengthened, integrating resources and professional knowledge from different regions to jointly address key unresolved issues in AD and AR comorbidity research.

An interrogation of keyword and co-occurrence patterns indicates a dynamic trajectory in the field's thematic emphasis. Research into AD and AR comorbidity was initially dominated by epidemiological studies and clinical observations, whereas recent investigations have increasingly prioritized molecular mechanisms, immune regulation, and therapeutic strategies. This evolution parallels a wider reorientation in allergic disease research, wherein the deciphering of immunological and genetic determinants has emerged as a central domain of inquiry^16,17,18. Emerging evidence also highlights the role of environmental factors in allergic diseases. For instance, a recent study showed that pet ownership increases exhaled nitric oxide and asthma severity in children with atopic asthma, suggesting that environmental exposures can modulate disease expression¹⁹. Another population-based cohort study demonstrated that the atmospheric environment influences the persistence of pediatric asthma, underscoring the importance of environmental factors in the course of allergic diseases²⁰. These findings align with the keyword clusters related to environmental triggers identified in our analysis, supporting the need for integrated research on gene-environment interactions in AD and AR comorbidity. The identification of 11 distinct keyword clusters underscores the diversity of research, encompassing immune modulation, genetics, and environmental triggers. Furthermore, the recurring centrality of "immune regulation" across these cluster points to the pivotal role of immune pathways in the AD-AR interplay. Given the shared immune dysregulation in both diseases, this immunological focus represents a promising avenue for developing integrated treatment strategies^21,22.

Relevant findings from previous studies have established common molecular and immune mechanisms underlying both AD and AR, findings further supported by the present analysis. These conditions converge on a pathophysiological model of immune dysregulation, particularly involving T-helper cells, and compromise the epithelial barrier in the skin and nose, consistent with previous research findings^23,24,25. Consequently, investigating these shared pathways is vital for identifying common biomarkers and therapeutic targets. The c findings highlight the central role of immune regulation, with cytokines and chemokines being critically implicated in both diseases. The co-occurrence of keywords related to immunity, inflammation, and genetics further underscores their complex etiology, in which genetic susceptibility, environmental exposures, and immune abnormalities intersect. Evidence suggests that the genetic and immunological commonalities between AD and AR allow them to mutually influence their clinical expression, potentially leading to more severe outcomes when they co-occur^26,27,28. Nevertheless, substantial gaps persist in clinical practice despite significant progress in understanding the shared mechanisms of AD and AR. A major shortcoming is the absence of standardized protocols for diagnosing and treating patients with this comorbidity. Given the considerable overlap in their immunopathology, it is imperative to develop integrated strategies that concurrently manage both conditions, moving beyond a siloed approach. Future research must therefore focus on refining diagnostic criteria, enhancing therapeutic efficacy, and optimizing management for these co-occurring allergic diseases.

Several limitations should be considered when interpreting the findings of this bibliometric analysis. First, the dominance of publications from the United States, China, and Germany may introduce geographic bias, as research output does not necessarily correlate with disease burden or population diversity. Findings derived primarily from these populations may not be fully generalizable to other regions with different genetic backgrounds, environmental exposures, and healthcare infrastructures. Second, the restriction to English-language publications may exclude relevant studies published in other languages, potentially omitting valuable insights from non-English-speaking regions. Third, the use of Web of Science as the sole data source may underrepresent research published in regional journals not indexed in this database. These geographic and linguistic biases should be considered when extrapolating the conclusions to global populations. Furthermore, it should be noted that bibliometric indicators, including publication counts and citation frequencies, reflect scholarly activity and visibility rather than directly measuring research quality or clinical relevance. Similarly, keyword clusters represent statistical co-occurrence patterns that require expert interpretation and validation through a comprehensive literature review. Therefore, while this study provides a valuable data-driven overview of the field, its findings should be interpreted alongside qualitative assessments and clinical expertise.

Conclusion
This systematic review and bibliometric analysis clarify the intellectual structure and emerging research trends in AD and AR comorbidity. It highlights immune regulation and genetic factors as key themes, thereby guiding future investigations into the common mechanisms underlying both diseases. As research progresses, multidisciplinary integration and global cooperation will be critical to advancing knowledge and enhancing patient care. Moving forward, studies should prioritize bridging gaps in clinical practice, especially through the development of unified diagnostic and therapeutic approaches for individuals with coexisting AD and AR.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CiteSpace	Chaomei Chen, Drexel University	CiteSpace 6.2.R4 (64-bit) beta Basic	Used for keyword co-occurrence analysis, cluster analysis, timeline visualization, and burst detection to identify research hotspots and thematic evolution.
Scimago Graphica	Spanish National Research Council (CSIC)	Scimago Graphica Setup 1.0.49	Used to visualize national collaboration networks and generate world maps based on VOSviewer output data.
VOSviewer	Centre for Science and Technology Studies, Leiden University, The Netherlands	VOSviewer version 1.6.19	Used to construct and visualize collaboration networks for countries, institutions, and authors, as well as journal citation analysis.