A Bibliometric Analysis of GWAS on Rheumatoid Arthritis from 2002 to 2024

Assessing causal relationship between non-alcoholic fatty liver disease and risk of atrial fibrillation

Objective: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent joint inflammation and immune dysregulation. Aberrant activation of immune cells plays a central role in RA development and progression. This study aimed to investigate the causal relationships between specific immune cell phenotypes and RA using a bidirectional Mendelian randomization (MR) approach. Methods: We conducted two-sample bidirectional MR analyses using genome-wide association study (GWAS) summary statistics. Genetic variants significantly associated with immune cell traits ([Formula: see text] < 1 × 10[Formula: see text]) were used as instruments for forward MR, while variants associated with RA were selected using a more stringent threshold ([Formula: see text] < 5 × 10[Formula: see text]) for reverse MR in order to enhance the specificity and reliability of causal inference, particularly given the complex genetic architecture of RA. Inverse variance weighted (IVW) analysis served as the primary method, supported by weighted median and MR-Egger regression. Heterogeneity and pleiotropy were assessed using Cochran’s [Formula: see text] test and the MR-Egger intercept. Results: Forward MR identified significant causal associations between RA risk and seven immune traits, including CD4[Formula: see text] T cell memory, CD8[Formula: see text] T cell subsets, dendritic cell (DC) subpopulations, and CD4n:%pre-Th17 cells. In the reverse MR analysis, RA was found to causally affect two immune phenotypes: CD4n:%pre-Th17 and CD123[Formula: see text] on CD11c[Formula: see text] DCs. Conclusions: This study provides genetic evidence supporting a causal interplay between specific immune cell populations and RA. In particular, Th17 precursors and CD123[Formula: see text] DCs emerged as key players in both RA onset and immune remodeling.

Epidemiological and other studies have shown that the occurrence and progression of rheumatoid arthritis (RA) are closely related to diet. To further explore the causal association between dietary habits and RA, we performed a bidirectional Mendelian randomization (MR) analysis. The dataset related to dietary habits is from genome-wide association studies, including 143 dietary habits. The dataset of RA is from the FinnGen database. Inverse variance weighted (IVW), MR-Egger, simple mode, weighted median, and weighted mode were used for the 2-sample, 2-way MR analysis. At the same time, a variety of pleiotropic and heterogeneity tests were used to ensure the accuracy of the results. IVW results show that among current drinkers (drinks usually with meals yes + it varies vs no) was positively correlated with RA (β, 0.563 [95% confidence interval [CI], 0.286-0.840]; P = 6.7 × 10-5). Spread type (low fat spread vs any other) was negatively correlated with RA (β, -2.536 [95% CI, -3.725 to -1.346]; P = 2.9 × 10-5). In addition, the reverse MR results showed that RA was positively correlated with milk type (skimmed vs any other; β, 0.006 [95% CI, 0.000-0.011]; P = 4.5 × 10-2). RA was positively correlated with spread type (tub margarine vs never; β, 0.016 [95% CI, 0.002-0.029]; P = 2.5 × 10-2). The results of pleiotropy and heterogeneity tests showed that there was no pleiotropy (P > .05) in the obtained results. The analysis results of MR-Egger, simple mode, weighted median, and weighted mode are consistent with our IVW results. This study reveals a potential association between specific dietary habits and RA. Among current drinkers (drinks usually with meals yes + it varies vs no) was positively correlated with RA. Spread type (low fat spread vs any other) was negatively correlated with RA. RA was positively correlated with milk type (skimmed vs any other) and spread type (tub margarine vs never).

Rheumatoid arthritis (RA) is a complex disease with several risk factors. The effects of blood metabolites on RA remains elusive. We conducted a genetic correlation scan to explore the relevance of blood metabolism with RA. The genome-wide association study (GWAS) dataset of RA(2014) was obtained from a large scale meta-analysis, including 29,880 RA cases and 73,758 controls. The GWAS datasets of 529 blood metabolites were derived from a recently published study. Linkage disequilibrium score regression (LDSC) analysis was performed to evaluate the genetic correlation between each of the blood metabolite and RA(2014). Then we used another GWAS data of RA(2021) and blood metabolites for LDSC analysis to verify whether the same blood metabolites were genetically correlated with RA. Mendelian randomization (MR) analysis was then applied to assess the causal relationship between the significant blood metabolites identified by LDSC and RA(2014). Six suggestive blood metabolites were identified for RA(2014), including 10-Undecenoate (correlation coefficient=-0.1686, p value=0.0394), isovalerylcarnitine (correlation coefficient=0.1660 p value=0.0273), proline (correlation coefficient=0.1647, p value=0.0145), pantothenate (correlation coefficient=-0.3311, p value=0.0078), tyrosine (correlation coefficient=0.1735, p value=0.0010), X-14057 (correlation coefficient=0.2695, p value=0.0373). We identified four blood metabolites may have genetic correlations with RA(2021), including oleoylcarnitine (correlation coefficient=0.1927, p value=0.0432), levulinate (correlation coefficient=0.1008, p value=0.0413), pantothenate (correlation coefficient=-0.2311, p value=0.0180), tyrosine (correlation coefficient=0.1301, p value=0.0078). There are two identical blood metabolites were found to be related with RA: pantothenate and tyrosine. It was found that there was a significant positive causal relationship between RA (exposure) and 10-Undecenoate (outcome) (β=0.0077, SE=0.0033, p=0.0192) by MR analysis. We investigated the genetic correlation and causal relationship between RA and blood metabolites by LDSC and MR analysis. These results may provide novel insights into the genetic mechanism of RA.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and joint destruction. Although the roles of inflammatory cytokines and metabolites in RA pathogenesis have caught a lot of attention, there is a lack of systematic studies, and their causal relationships remain unclear. We conducted a two-step mendelian randomization analysis utilizing genetic data from genome-wide association studies (GWAS) of inflammatory cytokines, metabolites, and RA. The first step assessed the causal effect of 91 inflammatory cytokines and 1400 metabolites on RA risk using inverse variance weighted method, complemented by MR-Egger, weighted median, simple mode and MR-PRESSO to ensure robustness and assess pleiotropy. The second step evaluated the mediation effects of selected metabolites on the relationship between cytokines and RA. The analysis identified 9 inflammatory cytokines, including IL-1α and IL-10, which significantly increase RA risk, while TNF-β exhibited a protective effect. Additionally, 6 metabolites were associated with increased RA risk, including 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPE and arachidonate (20:4n6). Conversely, 5 metabolites, such as docosatrienoate (22:3n3) and Cholesterol, were found to reduce RA risk. The mediation analysis revealed that TNF-β may exerts its protective effect through its influence on specific metabolites, and X-24949, which accounted for a -2.58% mediated effect in the TNF-β-RA causal pathway. This study explores the complex interplay between inflammatory cytokines, metabolites, and RA. The findings suggest potential biomarkers for early diagnosis and novel therapeutic targets, particularly those related to lipid metabolites and specific cytokines like TNF-β. Key message What is already known on this topic Inflammatory factors and metabolites are considered to be related to the onset and progression of RA. What this study adds We conducted a MR analysis to identify all inflammatory factors and metabolites associated with RA and calculated the mediation effect of inflammatory cytokines on RA through metabolites. This study contributes to a comprehensive understanding of the pathophysiological processes of RA. How this study might affect research, practice or policy This has laid the groundwork for developing early diagnosis methods and future treatments.

BackgroundThe pathogenesis of rheumatoid arthritis (RA) is an immune imbalance, in which various inflammatory immune cells and pro-inflammatory factors are involved. Interleukin-17 (IL-17), a potent pro-inflammatory cytokine, has been found to have increased expression in the joints of patients with RA compared to healthy individuals. However, the causal relationship between the expression level of IL-17 or IL-17 receptor (IL-17R) and RA remained unknown. In this study, two-sample Mendelian randomization (MR) was used to investigate the causal relationship between IL-17 and RA.MethodsSummary statistics for RA (14,361 RA cases and 43,923 healthy controls) and IL-17 (3,301 samples) were obtained from an available meta-analysis of published genome-wide association studies (GWAS). Relevant single nucleotide polymorphisms (SNPs) were selected by executing quality control steps from the GWAS summary results. Then we used bi-directional two-sample Mendelian randomization (MR) and multi-variable MR (MVMR) analysis to examine evidence of causality. MR and MVMR analyses progressed mainly using inverse variance weighted (IVW), weighted median (WM), and MR-Egger regression methods, which were applied to the genetic instrumental variables (IVs) of IL-17A/IL-17 RA, IL-17C/IL-17 RC, and IL-17D/IL-17RD and RA. For assessing the robustness of the results, we also carried out a sensitivity analysis to assess heterogeneity and pleiotropy, such as MR-Egger, leave-one-out, and MR pleiotropy residual sum and outlier (MR-PRESSO).ResultsTwo-sample MR Analysis showed the causal relationship between IL-17A/IL-17RA and RA. The presence of genetically high IL-17A/IL-17RA may increase the risk of RA (IL-17A(OR = 1.095; 95% C.I., 0.990-1.210, p.adj = 0.013), IL-17RA(OR = 1.113, 95%CI = 1.006-1.231, p.adj = 0.006)). However, the results indicated that IL-17C/IL-17RC, and IL-17D/IL-17RD demonstrated no causal impact on RA (IL-17C(OR = 1.007, 95%CI = 0.890-1.139, p.adj = 0.152), IL-17RC(OR = 1.006, 95%CI = 0.904-1.119, p.adj = 0.152), IL-17D(OR = 0.979, 95%CI = 0.843-1.137, p.adj = 0.130), IL-17RD(OR = 0.983, 95%CI = 0.876-1.104, p.adj = 0.129)). Furthermore, MVMR analysis shown that IL-17RA(OR = 1.049, 95% CI: 0.997-1.102, p.adj = 0.014) was associated with increased risk of RA. Sensitivity analysis showed no heterogeneity and pleiotropy, suggesting that the above results were robust and reliable.ConclusionThe MR analysis provides evidence that IL-17A/IL-17RA are risk factors for RA. This emphasizes the importance of intervention on IL-17A/IL-17RA in patients with RA. Developing drugs that limit IL-17A may reduce the risk of RA.

Objective This study aimed to examine whether rheumatoid arthritis (RA) is causally associated with type 2 diabetes (T2D). Methods We performed a two-sample Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW), weighted median, and MR-Egger regression methods. We used the publicly available summary statistics datasets from a genomewide association studies (GWAS) meta-analysis of 5,539 autoantibody-positive individuals with RA and 20,169 controls of European descent, and a GWAS dataset of 10,247 individuals with T2D and 53,924 controls, overwhelmingly of European descent as outcomes. Results We selected 10 single-nucleotide polymorphisms from GWAS data on RA as instrumental variables to improve the inference. The IVW method supported a causal association between RA and T2D (β=0.044, standard error [SE]=0.022, p=0.047). The MR-Egger analysis showed a causal association between RA and T2D (β=0.093, SE=0.033, p=0.023). In addition, the weighted median approach supported a causal association between RA and T2D (β=0.056, SE=0.025, p=0.028). The association between RA and T2D was consistently observed using IVW, MR Egger, and weighted median methods. Cochran's Q test indicated no evidence of heterogeneity between instrumental variable estimates based on individual variants and MR-Egger regression revealed that directional pleiotropy was unlikely to have biased the results (in-tercept=−0.030; p=0.101). Conclusion MR analysis supports that RA may be causally associated with an increased risk of T2D.

BackgroundOsteoarthritis (OA) and rheumatoid arthritis (RA) are often difficult to distinguish in the early stage of the disease. The purpose of this study was to explore the similarities and differences between the two diseases through Mendelian randomization (MR) and transcriptome analysis.MethodsWe first performed a correlation analysis of phenotypic data from genome-wide association studies (GWAS) of OA and RA. Then, we performed functional and pathway enrichment of differentially expressed genes in OA, RA, and normal patients. The infiltration of immune cells in arthritis was analyzed according to gene expression. Finally, MR analysis was performed with inflammatory cytokines and immune cells as exposures and arthritis as the outcome. The same and different key cytokines and immune cells were obtained by the two analysis methods.ResultsGWAS indicated that there was a genetic correlation between OA and RA. The common function of OA and RA is enriched in their response to cytokines, while the difference is enriched in lymphocyte activation. T cells are the main immune cells that differentiate between OA and RA. MR analysis further revealed that OA is associated with more protective cytokines, and most of the cytokines in RA are pathogenic. In addition, CCR7 on naive CD4 + T cell was positively correlated with OA. SSC-A on CD4 + T cell was negatively correlated with RA, while HLA DR on CD33- HLA DR + was positively correlated with RA.ConclusionOur study demonstrated the similarities and differences of immune inflammation between OA and RA, allowing us to better understand these two diseases.

STUDY QUESTIONIs there an increased risk of immunological diseases among endometriosis patients, and does a shared genetic basis contribute to this risk?SUMMARY ANSWEREndometriosis patients show a significantly increased risk of autoimmune, autoinflammatory, and mixed-pattern diseases, including rheumatoid arthritis, multiple sclerosis, coeliac disease, osteoarthritis, and psoriasis, with genetic correlations between endometriosis and osteoarthritis, rheumatoid arthritis, and multiple sclerosis, and a potential causal link to rheumatoid arthritis.WHAT IS KNOWN ALREADYThe epidemiological evidence for an increased risk of immunological diseases among women with endometriosis is limited in scope and has varied in robustness due to the opportunity for biases. The presence of a biological basis for increased comorbidity across immunological conditions has not been investigated. Here we investigate the phenotypic and genetic association between endometriosis and 31 immune conditions in the UK Biobank.STUDY DESIGN, SIZE, DURATIONPhenotypic analyses between endometriosis and immune conditions (17 classical autoimmune, 10 autoinflammatory, and 4 mixed-pattern diseases) were conducted using two approaches (8223 endometriosis, 64 620 immunological disease cases): (i) retrospective cohort study design to incorporate temporality between diagnoses and (ii) cross-sectional analysis for simple association. Genome-wide association studies (GWAS) and meta-analyses for those immune conditions that showed phenotypic association with endometriosis (1493–77 052 cases) were conducted.PARTICIPANTS/MATERIALS, SETTING, METHODSComprehensive phenotypic association analyses were conducted in females in the UK Biobank. GWAS for immunological conditions were conducted in females-only and sex-combined study populations in UK Biobank and meta-analysed with existing largest available GWAS results. Genetic correlation and Mendelian randomization (MR) analyses were conducted to investigate potential causal relationships. Those immune conditions with significant genetic correlation with endometriosis were included in multi-trait analysis of GWAS to boost discovery of novel and shared genetic variants. These shared variants were functionally annotated to identify affected genes utilizing expression quantitative trait loci (eQTL) data from GTEx and eQTLGen databases. Biological pathway enrichment analysis was conducted to identify shared underlying biological pathways.MAIN RESULTS AND THE ROLE OF CHANCEIn both retrospective cohort and cross-sectional analyses, endometriosis patients were at significantly increased (30–80%) risk of classical autoimmune (rheumatoid arthritis, multiple sclerosis, coeliac disease), autoinflammatory (osteoarthritis), and mixed-pattern (psoriasis) diseases. Osteoarthritis (genetic correlation (rg) = 0.28, P = 3.25 × 10−15), rheumatoid arthritis (rg = 0.27, P = 1.5 × 10−5) and multiple sclerosis (rg = 0.09, P = 4.00 × 10−3) were significantly genetically correlated with endometriosis. MR analysis suggested a causal association between endometriosis and rheumatoid arthritis (OR = 1.16, 95% CI = 1.02–1.33). eQTL analyses highlighted genes affected by shared risk variants, enriched for seven pathways across all four conditions, with three genetic loci shared between endometriosis and osteoarthritis (BMPR2/2q33.1, BSN/3p21.31, MLLT10/10p12.31) and one with rheumatoid arthritis (XKR6/8p23.1).LIMITATIONS, REASONS FOR CAUTIONWe conducted the first female-specific GWAS analyses for immune conditions. Given the novelty of these analyses, the sample sizes from which results were derived were limited compared to sex-combined GWAS meta-analyses, which limited the power to use female-specific summary statistics to uncover the shared genetic basis with endometriosis in follow-up analyses. Secondly, the 39 genome-wide significant endometriosis-associated variants used as instrumental variables in the MR analysis explained approximately 5% of disease variation, which may account for the nominal or non-significant MR results.WIDER IMPLICATIONS OF THE FINDINGSEndometriosis patients have a moderately increased risk for osteoarthritis, rheumatoid arthritis, and to a lesser extent, multiple sclerosis, due to underlying shared biological mechanisms. Clinical implications primarily involve the need for increased awareness and vigilance. The shared genetic basis opens up opportunities for developing new treatments or repurposing therapies across these conditions.STUDY FUNDING/COMPETING INTEREST(S)We thank all the UK Biobank and 23andMe participants. Part of this research was conducted using the UK Biobank Resource under Application Number 9637. N.R. was supported by a grant from the Wellbeing of Women UK (RG2031) and the EU Horizon 2020 funded project FEMaLe (101017562). A.P.M. was supported in part by Versus Arthritis (grant 21754). H.F. was supported by the National Natural Science Foundation of China (grant 32170663). N.R., S.A.M., and K.T.Z. were supported in part by a grant from CDMRP DoD PRMRP (W81XWH-20-PRMRP-IIRA). K.T.Z. and C.M.B. reported grants in 3 years prior, outside the submitted work, from Bayer AG, AbbVie Inc., Volition Rx, MDNA Life Sciences, PrecisionLife Ltd., and Roche Diagnostics Inc. S.A.M. reports grants in the 3 years prior, outside this submitted work, from AbbVie Inc. N.R. is a consultant for Endogene.bio, outside this submitted work. The other authors have no conflicts of interest to declare.TRIAL REGISTRATION NUMBERN/A

As previous studies have demonstrated an association between immune inflammation and rheumatoid arthritis (RA), our study aimed to lend novel insight by exploring the potential causal association between RA and different immunophenotypes. Data were obtained from the genome-wide association study (GWAS) from Finn Gen. The dataset of GWAS contains a cohort of 6236 RA cases and 147,221 controls in European population. Data on immune cell traits are publicly available from the GWAS catalog. A total of 731 immunophenotypes were included in this study including absolute cell counts (ACs), median fluorescence intensity, morphological parameters, and relative cell counts. Mendelian randomization analysis was performed by several methods, and sensitivity analysis and visualization of the results were also carried out. After being adjusted by false discovery rate (FDR), 6 immune phenotypes were significantly and causally associated with the development of RA: CD16 on CD14+ CD16+ monocytes (adjusted odds ratio [OR]: 0.950, 95% confidence interval [CI]: 0.924-0.977, P = 4.04 × 10-4), CD62L-CD86+ myeloid DC %DC (adjusted OR: 1.048, 95% CI: 1.021-1.076, P = 4.29 × 10-4), CD62L-CD86+ myeloid DC AC (adjusted OR: 1.050, 95% CI: 1.024-1.076, P = 1.11 × 10-4), CD62L- myeloid DC AC (adjusted OR: 1.067, 95% CI: 1.033-1.101, P = 8.35 × 10-5), DC AC (adjusted OR: 1.105, 95% CI: 1.062-1.149, P = 7.73 × 10-7), myeloid DC AC (adjusted OR: 1.060, 95% CI: 1.029-1.091, P = 9.96 × 10-5). In addition, we found that CD62L- Dendritic cell % increases with the onset of RA (OR: 1.136, 95% CI: 1.064-1.213, P = 1.36 × 10-4, PFDR = 0.099). This study explored the association between different immunophenotypes and RA, which may lend some novel insights into RA pathogenesis and facilitate the development of new treatments.

Several observational studies suggested an association between rheumatoid arthritis (RA) and bronchiectasis. Nevertheless, the presence of a causal relationship between these conditions is yet to be determined. This study aimed to investigate whether genetically predicted RA is associated with the risk of bronchiectasis and vice versa. We obtained RA genome-wide association study (GWAS) data from FinnGen consortium, and bronchiectasis GWAS data from IEU Open GWAS project. Univariate Mendelian randomization (MR) analysis was performed using inverse variance weighted (IVW) estimation as the main method. Furthermore, bidirectional and replication MR analysis, multivariate MR (MVMR), Mediation analysis, and sensitivity analyses were conducted to validate the findings. In the UVMR analysis, the IVW results revealed that RA had an increased risk of bronchiectasis (OR = 1.18, 95% CI = 1.10-1.27; p = 2.34 × 10-6). In the reverse MR analysis, no evidence of a causal effect of bronchiectasis on the risk of RA was detected. Conversely, in the replication MR analysis, RA remained associated with an increased risk of bronchiectasis. Estimates remained consistent in MVMR analyses after adjusting for the prescription of non-steroidal anti-inflammatory drugs (NSAIDs) and glucocorticoids. Immunosuppressants were found to mediate 58% of the effect of the RA on bronchiectasis. Sensitivity analyses confirmed the stability of these associations. This study demonstrated a positive causal relationship between RA and an increased risk of bronchiectasis, offering insights for the early prevention of bronchiectasis in RA patients and shedding new light on the potential role of immunosuppressants as mediators in promoting the effects of RA on bronchiectasis.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease of unknown etiology. Recent studies have indicated a potential relationship between the oral microbiome and the onset and progression of RA. However, research trends in this area have not been comprehensively examined. The aim of this study was to conduct a bibliometric analysis of the relationship between RA and the oral microbiome from January 1, 1995, to January 10, 2024, to elucidate the research landscape, including hot topics and emerging trends. We extracted literature related to RA and the oral microbiome from the Web of Science database. Utilizing CiteSpace software, we analyzed publications, countries, institutions, authors, and keywords through a visual knowledge graph to assess research hotspots and trends. In total, 833 articles were identified, revealing a consistent increase in the number of annual publications in this field over the study period. The United States has emerged as the leading country in terms of publication volume, with Harvard University being the most prolific institution. Among the authors, Jan Potempa has the highest number of publications. Keyword analysis indicated that current research hotspots concerning the relationship between RA and the oral microbiome primarily focus on Porphyromonas gingivalis, periodontitis, inflammation, expression, and peptidylarginine deiminase. Investigating the mechanisms by which oral and intestinal microorganisms influence RA, as well as developing intervention strategies targeting these microbiotas, is anticipated to be a significant future research direction. This study characterized the trends in the literature regarding the relationship between RA and the oral microbiome, providing valuable insights for scholars pursuing further research.

Rheumatoid arthritis (RA) is a prevalent autoimmune disease, and there is growing evidence suggesting a potential correlation between dietary factors and the pathogenesis of this condition. In order to investigate the causal relationship between diet and RA, we conducted a two-sample Mendelian randomisation (MR) analysis to examine the causal associations between twenty-two dietary factors and RA. Summary data from genome-wide association studies (GWAS) of RA were obtained from large GWAS meta-analyses. GWAS summary data for twenty-two dietary factors were obtained from UK Biobank. Random-effects inverse variance weighted was used as the primary method for assessing causality, and analyses of heterogeneity and horizontal pleiotropy were performed to ensure the accuracy of the results. Research indicates a negative genetic causal relationship between cereal intake (OR = 0·64, 95 % CI: 0·41, 0·99, P = 0·048) and oily fish intake (OR = 0·70, 95 % CI: 0·52, 0·95, P = 0·020) with the risk of RA. Other dietary factors were not causally related to RA. Sensitivity analysis shows that our results are reliable. This study provides genetic evidence suggesting that cereal intake and oily fish intake are protective factors for RA, indicating that RA patients and individuals at high risk should make appropriate dietary adjustments.

Genetic link between rheumatoid arthritis and autoimmune liver diseases: A two-sample Mendelian randomization study

The extra-articular lesions of rheumatoid arthritis (RA) are reported to involve multiple organs and systems throughout the body, including the heart, kidneys, liver, and lungs. This study assessed the potential causal relationship between RA and the risk of chronic kidney diseases (CKDs) using the Mendelian randomization (MR) analysis. Independent genetic instruments related to RA and CKD or CKD subtypes at the genome-wide significant level were chosen from the publicly shared summary-level data of genome-wide association studies (GWAS). Then, we obtained some single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs), which are associated with RA in individuals of European origin, and had genome-wide statistical significance (p5 × 10-8). The inverse-variance weighted (IVW) method was the main analysis method in MR analysis. The other methods, such as weighted median, MR-Egger, simple mode, and weighted mode were used as supplementary sensitivity analyses. Furthermore, the levels of pleiotropy and heterogeneity were assessed using Cochran's Q test and leave-one-out analysis. Furthermore, the relevant datasets were obtained from the Open GWAS database. Using the IVW method, the main method in MR analysis, the results showed that genetically determined RA was associated with higher risks of CKD [odds ratio (OR): 1.22, 95% confidence interval (CI) 1.13-1.31; p < 0.001], glomerulonephritis (OR: 1.23, 95% CI 1.15-1.31; p < 0.000), amyloidosis (OR = 1.43, 95% CI 1.10-1.88, p < 0.001), and renal failure (OR = 1.18, 95% CI 1.00-1.38, p < 0.001). Then, using multiple MR methods, it was confirmed that the associations persisted in sensitivity analyses, and no pleiotropy was detected. The findings revealed a causal relationship between RA and CKD, including glomerulonephritis, amyloidosis, and renal failure. Therefore, RA patients should pay more attention to monitoring their kidney function, thus providing the opportunity for earlier intervention and lower the risk of progression to CKDs.