Database selection

Scopus was selected as the primary data source for this study due to its comprehensive coverage of global scientific output. Widely recognized by the international research community, Scopus is considered a reliable, high-quality bibliometric database for conducting quantitative analyses in academic research13,14.

Search strategy

A systematic search was conducted in Scopus on June 17, 2025, using the query: TITLE ("rheumatoid arthritis") AND TITLE (gene OR genes OR genetic OR genetics OR genome OR polymorphism OR snp OR allele* OR haplotype* OR genotype OR mutation OR chromosome OR rna OR mrna OR dna). The TITLE field was utilized in the Scopus search to enhance specificity by retrieving only articles explicitly focused on the genetic aspects of rheumatoid arthritis. This minimized the inclusion of studies where genetic terms appear incidentally in abstracts or keywords, thereby reducing noise and sharpening the analytical focus. The search strategy was validated by reviewing the relevance of the retrieved articles. Only English-language original research articles focusing on the genetic aspects of rheumatoid arthritis were included, while reviews, conference papers, editorials, letters, and non-English publications were excluded. A total of 3,731 documents represent the eligible records retrieved directly from Scopus after filtering. Figure 1 depicts the PRISMA flow diagram of the study screening and selection process.

Data Analysis

Citation analysis was performed using Harzing’s Publish or Perish software (version 8)16 on data exported in RIS format, from which citation metrics were extracted. Country co-authorship and author keywords co-occurrence maps were generated using VOSviewer (version 1.6.20)17 based on data in CSV format. For all VOSviewer analyses, the default resolution parameter (resolution = 1.0) was applied. In the country co-authorship analysis, a minimum threshold of five documents per country was set, resulting in the inclusion of 62 countries in the network visualization. For the author keyword co-occurrence analysis18, a minimum occurrence threshold of 10 occurrences per keyword was applied. To improve analytical accuracy and reduce term fragmentation, a thesaurus file was employed to standardize synonymous and variant terms, including singular and plural forms as well as commonly used abbreviations. For example, “cytokine” was merged with “cytokines,” “major histocompatibility complex” with “MHC,” and “interleukin-1” with “IL-1”. This standardization enhanced the precision and interpretability of the VOSviewer analyses.

Annual Trends in Scientific Publications and Citations

The annual trends in scientific publications and citations from 1953 to 2024 demonstrate a clear evolution in research activity (Figure 2). Early output was negligible, with fewer than five papers per year until the 1980s. A gradual rise began in the late 1980s, marked by a spike in 1987, when 11 publications received 2,659 citations, reflecting the influence of seminal works. A sustained increase followed through the 1990s, highlighting growing interest in the field. The most productive epoch occurred between 2000 and 2015, with publication volume and citation counts peaking at 173 papers in 2016 and 6,146 citations in 2005. Although annual output remained high beyond 2016, citations have declined slightly, likely due to the citation lag for newer publications. Despite this, the continued high publication rate suggests enduring interest in this research area.

Country Contributions to Publications and International Collaboration Analysis

Table 1 presents the top 10 contributing countries in rheumatoid arthritis and genetics research based on bibliometric indicators. China leads in total publications (663), followed by the United States (608) and the United Kingdom (409). Despite producing fewer papers than China, the United States ranks highest in citation impact, with the most total citations (38,878), the highest average citations per publication (63.94), and the top h-index (95), reflecting both the volume and influence of its research output. European countries, such as Sweden, the Netherlands, and the United Kingdom, demonstrate strong citation performance relative to their output. Sweden, in particular, attained the highest average citations per publication (87.31) and cited publication (90.41), indicating high-impact research despite fewer publications. Meanwhile, East Asian countries like Japan, South Korea, and China contribute substantially to publication volume, though citation rates vary—Japan shows moderate impact (45.03), while China’s lower average (17.65) suggests a growing but still maturing influence.

VOSviewer was utilized to visualize the international co-authorship network. As shown in Figure 3, the analysis encompassed 62 countries with at least five publications, stratified into seven clusters based on co-authorship frequency. These clusters underscore distinct regional and thematic research collaborations, with each cluster representing a group of countries more likely to publish together. The United States (turquoise cluster), along with the United Kingdom, Netherlands, and Sweden (red cluster), and Spain (turquoise cluster), stood out as key contributors, showing both high publication output and strong citation impact. Their high total link strength values suggest central roles in global research networks. In contrast, countries like Thailand and Lithuania show weaker international collaboration, highlighting room for growth. Emerging nations such as China, Iran, Pakistan, and several Middle Eastern countries (yellow nodes) are becoming more active, with increasing publication counts. Although many of their outputs are recent, resulting in lower average citations, their growing publication volume suggests strong potential for future impact.

Leading Journals

Table 2 highlights the top ten journals contributing to rheumatoid arthritis and genetics research, emphasizing both publication volume and citation impact. leads in output with 302 articles and over 25,000 citations, reflecting its historical significance, though it has since transitioned to . Its legacy is underscored by strong metrics, including a CiteScore of 21.4. In current influence, stands out with the highest CiteScore (33.2) and a robust citation profile, ranking second in volume but maintaining top-tier impact. Other key contributors include , , and , each showing strong bibliometric performance. , for example, garnered over 5,000 citations from 131 papers, supported by a CiteScore of 8.5. Generalist and interdisciplinary journals like and also feature prominently, reflecting the expanding interface between rheumatology, immunology, and broader biomedical research. , despite fewer papers, demonstrates high impact with a CiteScore of 10.8 and strong visibility across disciplines.

Most Prolific Authors

Table 3 highlights the top ten most prolific authors, showcasing their affiliations, countries, and bibliometric impact. Martín J (Spain) leads in total publications (99) and cited papers (97), reflecting both productivity and consistent scholarly influence. Barton A (UK), despite fewer publications (73), achieves the highest average citations per paper (110.90), emphasizing the impact of his contributions. Other highly influential figures include Gregersen PK (USA) with the most total citations (12,849), and Huizinga TWJ (Netherlands), with a remarkable average citations per paper of 132.62. Swedish researchers Klareskog L and Padyukov L stand out with high citation averages (155.00 and 163.22, respectively), reinforcing Sweden's research prominence. Spanish authors like Balsa A and González-Gay MA further underscore Spain’s active role in the field, while Bae SC and Lee YH represent South Korea’s growing influence through consistent output and strong citation metrics.

Most Influential Articles

Table 4 highlights the 10 most highly cited publications in rheumatoid arthritis and genetics, representing foundational work that has shaped current scientific understanding. Leading the list is Gregersen et al. (1987)19, and their shared epitope hypothesis remains a seminal contribution with 2,265 citations and a lasting impact on molecular rheumatoid arthritis research. Okada et al. (2014)20, published in , with 1,807 citations and the highest annual citation rate (164.27), marking a transformative step in linking GWAS findings to therapeutic development. Similarly, Begovich et al. (2004)21 identified a key single-nucleotide polymorphism (SNP) in protein tyrosine phosphatase non-receptor type 22 (PTPN22), earning 1,256 citations and influencing subsequent genetic studies on autoimmune susceptibility. Several other top-cited articles delve into GWAS and functional genomics, such as those by Stahl et al. (2010)22 and Suzuki et al. (2003)23, which expanded the map of rheumatoid arthritis-associated loci. Padyukov et al. (2004)27 made a pivotal contribution by linking smoking with Human Leukocyte Antigen (HLA)–DR alleles, offering early evidence of gene–environment interactions. Liu et al. (2013)25 brought epigenetics to the forefront with insights into DNA methylation in rheumatoid arthritis. Earlier landmark studies by MacGregor et al. (2000)24 on heritability and Firestein et al. (1990)26 on cytokine gene expression continue to be influential, reflecting their foundational role in both clinical and molecular aspects of rheumatoid arthritis research.

Visualization Analysis of Dynamic Changes in Co-Occurrence of the Author’s Keywords

VOSviewer was used to generate a keyword co-occurrence map based on 102 author keywords grouped into four thematic clusters (Figure 4). Cluster 1 (red) is the largest and most recent cluster, focusing on the molecular underpinnings of rheumatoid arthritis and the development of personalized treatment strategies. Prominent keywords include rheumatoid arthritis, methotrexate, inflammation, autoimmune diseases, gene expression, biomarkers, and cytokines. Emerging keywords like bioinformatics (2021.8), machine learning (2023.1), and RNA-seq (2020.1) reflect a data-driven shift in rheumatoid arthritis research. Recent interests include Mendelian randomization (2023.3), COVID-19 (2022.7), and interstitial lung disease (2022.9). The green cluster (Cluster 2) captures classical genetic epidemiology and comparative autoimmune research. Recurring keywords are polymorphisms, SNPs, meta-analysis, systemic lupus erythematosus (SLE), and autoantibodies. Although many terms are older (e.g., juvenile rheumatoid arthritis, 2002), their continued relevance reflects enduring efforts in gene discovery. The blue cluster (Cluster 3) represents well-established immunogenetic domains with clinical relevance. Main keywords include HLA, HLA-DRB1, rheumatoid factors, anti-CCP, PTPN22, and shared epitope. Keywords such as cardiovascular disease, atherosclerosis, and disease activity reflect links between genetics and clinical outcomes. Many keywords are either older (e.g., HLA, 2004.2) or more recent, such as anti-CCP (2016.0) and ACPA (2016.2). The yellow cluster (Cluster 4) explores functional polymorphisms and genotype-phenotype relationships. The keywords include gene polymorphisms, haplotypes, genotypes, tumor necrosis factor (TNF), vitamin D receptor (VDR), etanercept, and TNF-α. Some keywords, such as vitamin D (2019.4) and TNF-α (2018.5), reflect recent interest in functional gene variants. This cluster also contains older keywords related to foundational methods like restriction fragment length polymorphism (RFLP, 2005.5) and immunogenetics (2002.3).

Research Gaps and Future Agenda

Although data-driven and integrative genomic approaches are increasingly shaping rheumatoid arthritis research, several knowledge gaps remain that limit the translation of genetic discoveries into clinical benefit. Current GWAS are still dominated by European ancestry cohorts, resulting in limited transferability of risk loci and polygenic risk scores to underrepresented populations5,8,20. Furthermore, functional follow-up studies integrating multi-omics layers such as epigenomic QTLs, single-cell transcriptomics, and proteomics remain sparse25,31,33. These gaps hinder the identification of causal variants and the mapping of gene regulatory networks underlying extra-articular manifestations such as interstitial lung disease or cardiovascular comorbidities3,39. Moreover, while polygenic risk scores and Mendelian randomization offer tools for prediction and causal inference, their clinical validation and incorporation into personalized treatment strategies remain nascent34,36,51.

Future research should prioritize the following questions to accelerate scientific and translational progress:

1. How can multi-ancestry GWAS improve fine-mapping accuracy and reveal population-specific risk variants in rheumatoid arthritis?

2. What role can multi-omics Mendelian randomization play in clarifying causal pathways linking genetic variants to extra-articular manifestations (e.g., lung fibrosis, atherosclerosis)?

3. How can integrative analyses of epigenomic QTLs and single-cell data refine our understanding of disease-relevant immune cell subtypes?

4. What strategies can enhance the clinical utility, equity, and calibration of polygenic risk scores across diverse ancestries?

5. How can predictive machine learning models be combined with genomic data to guide therapeutic response and drug repurposing?

Addressing these questions will be pivotal for transforming genetic discoveries into precision healthcare tools for rheumatoid arthritis.