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Abstract

Background

The relationship between gout pathogenesis and inflammatory cytokine alterations remains uncertain, with inconsistent findings across studies. This study evaluated the causal links between gout and 41 inflammatory cytokines using Mendelian randomization (MR).

Methods

Gout-related genetic variants were examined in two large public databases: the Finnish database with 8489 gout patients and 240,862 European ancestry controls and the Genome-Wide Association Studies (GWAS) catalog dataset with 375 gout cases and 455,973 European ancestry controls. Cytokine levels in 8293 healthy participants from the GWAS were analyzed. Moreover, univariate MR analysis, primarily the inverse variance weighting method, was applied to examine the causal association between these factors. The findings were further validated using four additional MR methods based on different modeling assumptions, and their robustness was examined through sensitivity analysis using the MR leave-one-out analysis, Cochran’s Q test, MR-Egger intercept test, and linkage disequilibrium score regression analysis.

Results

Following Bonferroni correction, this study unveiled a potential connection between macrophage inflammatory protein-1 beta (MIP 1B) and the risk of gout (OR: 1.08, 95%CI: 1.03-1.13, p = 9.61 × 10^-4). Additionally, growth-regulated oncogene alpha (GROA) and macrophage migration inhibitory factor (MIF) were implicated in downstream gout development (OR: 1.04, 95%CI: 1.01-1.08, p = 0.02; OR = 1.04, 95%CI: 1.01-1.07, p = 0.0079). Sensitivity analyses confirmed the findings’ reliability and consistency.

Conclusion

Our study suggests that MIP 1B may be a risk factor for gout, and inflammatory cytokines such as GROA and MIF are involved in the progression of gout. This supports a causal relationship between inflammatory cytokines and gout.

Keywords

inflammatory cytokines gout Mendelian randomization causal association

Introduction

Gout is a metabolic disease featured by augmented serum uric acid levels, activation of inflammatory mediators, and release of cytokines, resulting in acute gouty arthritis and monosodium urate (MSU) crystal accumulation in the joints.¹ Additionally, gout poses risks for kidney-related diseases, including uric acid nephropathy and chronic renal failure.² The global incidence of gout over recent years has increased due to changes in dietary habits and lifestyle.³ Moreover, the average prevalence of gout in men is 3 to 4 times that of women, reaching approximately 2.6%.⁴ Acute gout attacks are triggered by the interaction of MSU crystals with mononuclear phagocytes. Phagocyte engulfment of MSU crystals releases sodium ions in an acidic environment, causing changes in intracellular sodium and potassium ion concentrations and subsequent NOD-like receptor protein 3 inflammasome activation. This activation promotes the release of interleukin (IL)-1β, IL-6, IL-8, and C-X-C motif chemokine ligand (CXCL) and induces neutrophil infiltration.⁵ Inflammatory cytokines, generated by immune cells, orchestrate the inflammatory responses by attracting and mobilizing monocytes, macrophages, and T cells while stimulating inflammation-related gene expression and further cytokine production.^6,7

Previous studies have demonstrated the connection of IL-6, IL-1β, and tumor necrosis factor (TNF)-α to inflammatory activity in gout patients.⁸ Some studies have revealed a notable increase in serum IL-6 levels in child and adult gout patients, indicating that this increase is positively linked to white blood cell count and C-reactive protein levels.^9,10 Kim et al. found that stimulating synoviocytes with MSU crystals promoted IL-8 mRNA expression, highlighting the vital role of IL-8 in promoting neutrophil accumulation and mediating inflammation and its strong association with acute gout attacks.¹¹ Liu et al. have noted that MSU crystal stimulation of human peripheral blood mononuclear cells and neutrophils, a process that leads to IL-8 accumulation at inflammation sites, markedly elevated serum IL-8 levels, attracting more inflammatory cytokines and intensifying the inflammatory response.¹² Wu et al. have revealed that IL-10 reduces TNF-α and IL-1β transcription and stability, thus effectively regulating the inflammatory response, a finding crucial for comprehending the mechanisms of gout inflammation.¹³ Chen and colleagues have compared IL-10 expression in the synovial fluid of gout patients, osteoarthritis patients, and healthy controls and unveiled that IL-10 levels are significantly higher among gout patients, indicating that the body naturally increases IL-10 to counteract gout inflammation.¹⁴ While several population-based observational investigations have found correlations between multiple inflammatory cytokines and gout, the limitations inherent in these investigations, including the potential for reverse causality, small sample sizes, and confounding factors such as sex, underscore the need for comprehensive research to identify gout-specific biomarkers for early diagnosis and treatment in clinical settings.

Mendelian randomization (MR) is a genetic epidemiological procedure with single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to deduce exposure factors and outcomes based on Mendelian laws of inheritance. This approach allows for the examination of potential causal relationships between genes. By exploiting the random assignment of genes during conception, MR minimizes confounding, deviation, and reverse causation while maintaining reasonable temporal order^15,16 and could be used to infer causal relationships using genetic variation as a predictor of disease onset, independent of external environmental and social behaviors. This study initially extracted relevant genetic variants from genome-wide association study (GWAS) data on 41 inflammatory cytokines and explored the direction of causality by examining the connections between exposures and outcomes using dual-sample MR research methods, hoping to offer new research ideas and approaches for gout prevention and treatment.

Methods

Study design

The study explored the causal association of inflammatory cytokines with gout by employing the open GWAS catalog and FinnGen data sets. A two-way MR analysis was performed with 41 inflammatory cytokines as exposure or outcome and gout as the counterpart. To obtain reliable inference results, we strictly adhered to the following three fundamental assumptions^17,18: (1) correlation assumption, where IVs are strongly connected with exposure factors; (2) independence assumption, where IVs are unrelated to confounding factors; and (3) exclusion hypothesis, where IVs have no direct relationship with outcome factors. To address the reproducibility issue in the MR-dependent GWAS method,¹⁹ we conducted repeated MR analyses using two sets of gout GWAS data and combined the results to enhance the confidence of the MR estimate. Figure 1 shows the study’s flow chart. Additionally, this article is in compliance with the STROBE-MR guidelines (https://www.strobe-mr.org/), ensuring that it meets the necessary standards for methodological reporting (As shown in Supplemental file 1).

Figure 1.

The study’s flow chart.

Data sources and participants

This two-sample MR used only publicly available, de-identified GWAS summary statistics; no new participant recruitment occurred. Data extraction, harmonization, and statistical analyses were performed from October 2023 to July 2024. Summary statistics for the outcome GWAS (FinnGen R9) and the independent replication GWAS (GWAS Catalog accession GCST90043662) were accessed in June 2024; the cytokine GWAS summary data were accessed in May 2024.

Cytokine GWAS data sources: circulating levels of 41 inflammatory cytokines were obtained from a Finnish meta-GWAS of 8293 participants²⁰ (Young Finns Study, FINRISK1997, FINRISK2002) using an additive genetic model across 10.7 million SNPs; models were adjusted for age, sex, BMI, and the top 10 genetic principal components.

Gout GWAS data sources: we used two European-ancestry datasets. The primary analysis drew on FinnGen R9²¹(N = 249,351; 8489 cases, 240,862 controls). The replication analysis used GWAS Catalog accession GCST90043662²² (N = 456,348; 375 cases, 455,973 controls). All gout phenotypes were adjusted based on 10 principal components, such as age and genotyping batches.

Since all data were from previously published articles and open-source databases, the need for the Ethics Committee’s authorization from each institution and written informed consent from individual participants were unnecessary. The diagnostic criteria for gout used in both preliminary and replication analyses were based on the International Classification of Diseases, 10th edition (ICD-10) (https://icd.who.int/browse10/2016/en#).

Screening of IVs

To augment the validity and reliability of MR results, we carefully screened IVs based on the following procedures. Firstly, SNPs with strong associations with gout and inflammatory cytokines were selected using a significance threshold of p < 5 × 10^-8. For cytokines with fewer identified SNPs, a cutoff of p < 1 × 10^-5 was used to include SNPs with significant connections with the exposure factors. Secondly, SNPs with linkage disequilibrium were eliminated using the clump_data function with parameters (r² = 0.01, kb = 500) to ensure independence between SNP sites. Thirdly, F statistics were employed to inspect the robustness of correlation of individual IVs with exposure factors and the effect value of SNPs. It is generally accepted that F >10 indicates a non-weak IV.^23,24 The F value was computed using the following equation:

F = \frac{R^{2} \times (N ‐ 1 ‐ K)}{(1 ‐ R^{2}) \times K}

where N is the number of samples in the exposure GWAS study, k represents the number of IVs, and R² is the regression equation’s coefficient, which quantifies the extent to which the IV elucidates the exposure, and was computed as

R^{2} = \frac{2 \times (1 ‐ MAF) \times MAF \times β}{S D}

where minor allele frequency (MAF) is a measure of the frequency at which fewer common alleles occur in a population, β is the effect value of the allele, and SD is the standard deviation of β.

MR analysis,meta-analysis and sensitivity analysis

We assessed the causal association between 41 inflammatory cytokines and gout in a two-sample design using FinnGen (primary outcome GWAS) and an independent GWAS Catalog dataset (replication). The causal relationship between cytokines and gout was estimated using the IVW method, renowned for its robust performance and statistical power. IVW was used as the primary analytical method because it provides the most precise estimate of the causal effect under the assumption that all genetic variants are valid instrumental variables (i.e., no pleiotropy). It combines the ratio estimates of each SNP by weighting them by the inverse of their variance, making it highly efficient when the instrumental variables meet the MR assumptions.²⁵ Then, the weighted median method was applied.²⁶ It provides consistent causal estimates even when up to 50% of the genetic instruments are invalid due to pleiotropy. This method strengthens our confidence in the results if its estimates align with those from IVW. Additionally, The simple mode-based method²⁷ was applied to assess the causal effect under the assumption that the largest number of similar genetic variants, irrespective of their precision, represents the valid causal estimate. This approach is robust when the valid instruments form a plurality (the largest cluster) among heterogeneous ratio estimates, even if they constitute less than 50% of the total instruments. Furthermore, The weighted mode²⁷ estimator was employed as a more efficient and precise extension of the simple mode method. It assigns greater weight to genetic variants with more precise causal estimates (typically those with lower standard errors) when identifying the modal causal effect. This approach increases the stability and reliability of the estimate while maintaining robustness to invalid instruments, provided the modal category of variants is valid.

Bonferroni correction²⁸ is a conservative statistical approach designed to mitigate the problem of increased false positives (Type I errors) arising from multiple hypothesis testing. The method adjusts the significance level (alpha, α) by dividing it by the number of comparisons performed. For example, when conducting 20 independent tests with an initial α of 0.05, the corrected significance threshold becomes 0.0025 (i.e., 0.05/20). Under this adjustment, a result is considered statistically significant only if its p-value falls below this more stringent cutoff. To address multiple comparisons across cytokines, we applied a Bonferroni correction to 41 primary tests, resulting in a per-test significance threshold of 0.00122 (0.05/41).

To examine the reliability of causal estimation of our MR analysis, we performed sensitivity analysis using multiple approaches. First, we incorporated more IVs. However, introducing more IVs with horizontal pleiotropy and heterogeneity can be potentially biased. Second, Additionally, MR-Egger was used to test for and adjust for directional pleiotropy. It allows pleiotropic effects to be non-zero on average, providing a corrected estimate even when all instruments are invalid, albeit at the cost of reduced statistical power. The intercept from MR-Egger was used to assess potential pleiotropic bias.²⁸ Third, we applied Cochran’s Q test to evaluate the heterogeneity between each SNP estimate and the I² statistic to measure the heterogeneity, with p > 0.05 indicating minimal intergenic heterogeneity.²⁵ The I² was computed as

I^{2} = \frac{Q ‐ df}{Q} \times 100 %

where Q is the statistic, and df is the dispersion. I² = 0, 25%, 50%, and 75% suggest no, low, medium, and high heterogeneity, respectively. We prespecified that fixed-effect IVW would be used when Cochran’s Q indicated no evidence of heterogeneity (p ≥ 0.10) and I² < 25%; otherwise, multiplicative random-effects IVW was applied. A leave-one-out sensitivity analysis²⁹ was conducted to assess whether the overall causal estimate was unduly influenced by any individual genetic variant. This method iteratively removes each SNP and recalculates the pooled effect estimate, thereby evaluating the stability and robustness of the association.

Linkage disequilibrium (LD) score regression (LDSC) analysis

LDSC is a technique testing genetic correlations between two complex traits, helping avoid inflated statistics and polygenicity, prevent potential confounding by co-inheritance, and establish a causal link between an outcome and an exposure.³⁰ In LDSC, the LD score, a statistic that measures the linkage disequilibrium relation of a SNP with its neighboring SNPs, is used to determine the SNP’s association with complex traits. By calculating the LD score of each SNP and evaluating its association with complex traits, researchers can obtain reliable results and ensure the accuracy and validity of MR studies by minimizing the potential confounding effects of co-inheritance.

Statistical methods

All statistical analyses were performed in R (version 4.2.3). MR analyses were implemented primarily using the MR package (version 0.5.0) and the TwoSample MR package (version 0.5.6). Meta-analyses were carried out using the metafor package (version 2.4-0), using REML random effects under heterogeneity and fixed effects otherwise. Briefly, SNP-level MR used TwoSampleMR (multiplicative random-effects IVW when Q-test p < 0.10 or I² ≥ 25%).

Results

To ensure sufficient SNPs for subsequent MR analysis, we selected p < 1 × 10^-5 for screening SNPs associated with each circulating inflammatory factor and gout-related functional outcomes. This screening revealed 472 SNPs with significant inflammatory factor associations. These SNPs were subsequently utilized as IVs for gout. The F statistics for all SNPs of the 41 inflammatory cytokines ranged from 20.90 to 782.45, suggesting no or weak IV bias (Supplemental Table S1).

Influence of 41 inflammatory cytokines on gout

Figure 2 (forest plot) and Supplemental Table S2 show the outcomes of the preliminary analyses of 41 inflammatory cytokines. IVW analysis revealed that individuals with genetically determined elevated MIP-1B levels had an 8% augmented risk of gout compared to the controls (OR: 1.08, 95% CI: 1.03-1.14, p = 0.015). This finding was consistent with the weighted median estimate (OR: 1.06, 95% confidence interval: 1.01-1.11, p = 0.021). MR-Egger, simple model, and weighted model analyses also exhibited similar directions of the β values, as shown in Figure 3 (scatter plot). Additionally, the MR-Egger intercept suggested no horizontal pleiotropy (p = 0.744) and leave-one-out method did not exhibit heterogeneity or horizontal pleiotropy (Supplemental File 1). Moreover, the association between MIP-1B and gout exhibited moderate heterogeneity, as indicated by I² and Cochrane’s Q (I²: 63%; p = 0.0015) (Supplemental Table S3). Therefore, we employed an IVW random effect model for further analysis.

Figure 2.

Forest plot of influence of 41 inflammatory cytokines on gout (Forward).

Figure 3.

Scatter plot of three inflammatory cytokines (GROA, MIF, MIP-1B).

Influence of gout on 41 inflammatory cytokines

MR analysis employing the IVW method demonstrated a significant connection between gout and elevated growth-regulated oncogene alpha (GROA) (OR, 1.12, 95%CI: 1.05-1.20, p = 0.00057) and macrophage migration inhibitory factor (MIF) (OR, 1.12, 95%CI: 1.04-1.19, p = 0.001) level, indicating our findings’ robustness and reliability (Figure 4 forest plot). MR-Egger, simple model, weighted model, and weighted median analyses all resulted in similar findings, further affirming the association of gout with GROA and MIF and validating the consistency and reliability of the results, as depicted in the scatter plot (Figure 3). Additionally, the MR-Egger intercept revealed no horizontal pleiotropy for GROA (p = 0.773) and MIF (p = 0.458) (Supplemental Table S4-6). Also, the leave-one-out method did not show any signs of heterogeneity or horizontal pleiotropy (Supplemental File 1).

Figure 4.

Forest plot of influence of gout on 41 inflammatory cytokines (Reverse).

Confounding analysis, replication analysis, and meta-analysis

Although these 41 known cytokines passed the heterogeneity and horizontal pleiotropy tests, the association between these cytokines and other phenotypic IVs was further examined. Our query results on the Phenoscanner website revealed no confounding factors associated with the SNPs of MIP-1B, GORA, and MIF. To validate the findings of the preliminary MR analysis, we obtained independent gout genetic data from the GWAS catalog dataset for replication analysis. The results revealed consistent trends for these three cytokines observed in the preliminary analysis. Subsequently, we performed a meta-analysis by combining the two MR analyses’ results and again confirmed that MIP-1B (OR: 1.08, 95%CI: 1.03-1.13, p = 9.61 × 10^-4), GORA (OR: 1.04, 95%CI: 1.01-1.08, p = 0.02), and MIF (OR = 1.04, 95%CI: 1.01-1.07, p = 0.0079) are causally connected to gout (Figure 5).

Figure 5.

Meta-analysis of the causal associations of 41 inflammatory cytokines with gout. OR, odds ratio; CI, confidence interval.

LDSC analysis

Our LDSC analysis unveiled no significant genetic association between gout and MIF (Rg = −0.110, Se = 0.244, p = 0.651) and MIP-1B (Rg = 0.244, Se = 0.144, p = 0.089), and GROA exhibits negative heritability, making it impossible to calculate genetic correlation, which implies that our findings are unaffected by the shared genetic components.

Discussion

This study investigated the causal associations of 41 inflammatory cytokines with gout utilizing the FinnGen dataset and the GWAS catalog dataset. Our MR analysis revealed that elevated MIP-1B levels might increase the risk of gout, positioning it as a potential risk factor for gout. Additionally, our reverse MR study revealed increased GROA and MIF levels in individuals with gout, potentially contributing to the downstream development of gout. These findings further deepen our comprehension of the involvement of inflammatory cytokines in gout and offer potential targets for future treatment strategies.

Gout, featured by the accumulation of MSU crystals in joints, is a form of inflammatory arthritis with a global prevalence of about 2%–4%.³¹ Numerous studies have shown that acute gout attacks trigger the production of IL-1β, IL-6, IL-8, TNF-α, and CXCL-8^32–34 and substantial accumulation of neutrophils in joint synovial fluid and synovium.^35,36 In severe cases or when multiple areas are affected, a large amount of IL-1, IL-6, TNF-α, and other inflammatory factors may enter the circulation, leading to systemic inflammatory symptoms.^35,37 Furthermore, interactions between MSU crystals and macrophages prompt the infiltration of inflammatory cells and the release of cytokines, like IL-1β, TNF-α, IL-6, and CXCL1, leading to acute gouty arthritis. Animal studies have also revealed that such stimulation causes mononuclear macrophages to become inflammatory M1-like macrophages, producing various inflammatory factors.^36,38

Our study unveiled that elevated MIP-1B levels may play a role in gout development, suggesting its involvement in the early disease stages. Previous research has shown that MIP-1β, or chemokine C-C motif ligand 4 (CCL4), is a type of C-C chemokine that affects the movement of immune cells by targeting the C-C chemokine receptor type 5 receptor.^39–42 Neutrophils respond to MSU crystal stimulation by releasing monocyte chemoattractant CCL4, indicating MSU’s significant role in inducing genes like CCL4 in human neutrophils, thereby attracting monocytes through CCL4 secretion.⁴³ Previous studies have identified the MSU-induced binding sites in the CCL4 gene promoter, which is crucial for activating human neutrophils, along with the selective induction of proteins like CCL4. Beyond neutrophil/monocyte chemotaxis, MIP-1β (CCL4) is a high-affinity CCR5 ligand that promotes the recruitment and retention of CCR5⁺ Th1-polarized effector/memory T cells, reinforcing a type-1 cytokine environment (e.g., IFN-γ) within inflamed joints.⁴⁴ Given the established roles of Th1/Th17 responses in gout pathogenesis—where IL-17 enhances neutrophil influx and amplifies NLRP3/IL-1β⁴⁵–driven inflammation—CCL4–CCR5–mediated T-cell trafficking provides a plausible adaptive-immune mechanism linking our genetic evidence (higher MIP-1β associated with gout risk) to downstream cellular events.⁴⁶ This framework suggests that upregulated CCL4–CCR5 signaling may (i) increase local Th1 effector density, (ii) potentiate macrophage/dendritic-cell–T-cell crosstalk, and (iii) indirectly modulate Th17 activity via antigen-presenting-cell–derived cytokines, thereby sustaining gout flares.^47,48 These findings highlighted a complex process involving multiple signaling pathways, including spleen tyrosine kinase, TGF-β activated kinase 1, p38 mitogen-activated protein kinase, MAPK/ERK kinase (MEK)/extracellular signal-regulated kinase, phosphoinositide 3-kinase/Ak strain transforming, and transcription factors, such as nuclear factor kappa-light-chain-enhancer of activated B cells, CCAAT/enhancer binding protein, and cAMP response element-binding protein.^49–51

This study also revealed that GROA and MIF likely exert a significant role in gout development. GROA, also known as CXCL1,⁵² belongs to a subfamily of chemokines, acting as a chemotactic cytokine that guides immune cells.⁵³ Previous studies have demonstrated that reducing circulating GROA levels and its tissue expression can alleviate symptoms, such as leukocyte infiltration, tissue necrosis, edema, and synovial inflammation, in mouse joints.⁵⁴ GROA, as a key chemokine in the MAPK-peroxisome proliferator-activated receptor gamma signaling pathway, is crucial in reducing serum GROA levels when MSU-induced monocytes and neutrophils are activated, potentially easing the inflammatory response in gout.⁵⁴ MIF is a versatile cytokine with diverse functions in inflammatory responses and immune regulation.^55,56 Studies on MIF-deficient mice and in vivo MIF suppression^57–60 have indicated that MIF promotes leukocyte recruitment during inflammatory responses. Moreover, MIF-deficient mice reduces inflammation levels.^61–64 Previous research has identified MIF as a pro-inflammatory cytokine essential in gouty arthritis, with increased MIF levels in response to MSU crystals, indicating its significant role in the early inflammation of acute gouty arthritis.⁶⁵ A study involving 98 gout patients has revealed significantly higher MIF levels in the synovial fluid during acute gout and elevated serum MIF in intermittent gout, along with a positive connection between MIF levels in synovial fluid and leukocyte, neutrophil counts, and IL-8 levels.¹¹ Other investigations have unveiled the crucial involvement of MIF in inflammation triggered by MSU crystals, particularly in a mouse model where elevated MIF levels in synovial fluid are positively correlated with IL-1β concentrations.⁶⁶ These findings illustrate MIF as a crucial mediator in the early gout stages, facilitating neutrophil accumulation and IL-1β production, two key elements in acute gout pathogenesis. Our study indicates that changes in these cytokines are likely the consequences of gout progression. If cellular inflammatory factors are specifically linked with outcomes during gout onset, they could potentially serve as therapeutic targets for gout treatment and control.

The robustness of this MR study is evident in its use of a large sample population to systematically investigate the potential causal association between 41 inflammatory cytokine phenotypes and the occurrence of gout from a genetic perspective. As the first MR study to explore the causal association between gout and these 41 inflammatory cytokines, the study employed various statistical approaches, such as IVW, weighted median, simple median, maximum likelihood ratio, MR-Egger regression to ensure the results’ robustness. By addressing confounding and reverse causality through MR analysis, this study provides stronger evidence compared to traditional observational studies for evaluating the causal association between inflammatory cytokine levels and gout. However, our study has certain limitations. Firstly, the study primarily involves European descent, potentially limiting the findings’ application to other ethnic groups. Secondly, due to the absence of specific demographic information and clinical records, subgroup analysis using the data from the two large-scale GWASs used in this study was not feasible.

Conclusion

This study investigated the potential causal association between 41 inflammatory cytokines and gout using a two-sample MR approach. The findings revealed a positive correlation between MIP-1B and gout risk and suggested that MIF and GROA may play important roles in the pathogenesis of gout. These results highlight promising biomarkers, such as MIP-1B, that could facilitate early gout detection through blood-based screening in high-risk populations (e.g., individuals with hyperuricemia or family history). Furthermore, these findings may inform the development of targeted immunomodulatory therapies, such as monoclonal antibodies or receptor antagonists specifically against MIF or GROA, which could provide novel treatment options for patients refractory to conventional therapies. However, it is important to acknowledge that MR findings rely on key assumptions—including the absence of pleiotropy, confounding, and measurement error—which may introduce bias despite robust sensitivity analyses. Further in vivo and in vitro experiments are necessary to validate these associations and elucidate the underlying biological mechanisms before clinical translation can be achieved.

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material for Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis by Han Zhang, Xiaohong Huang, Mingyang Li, Ze Wang, Guanhong Chen, Zian Zhang, Yingze Zhang, Tengbo Yu, Yongtao Zhang in European Journal of Inflammation

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Supplemental Material

Supplemental Material - Mapping the influence of inflammatory cytokines on gout risk: Insights from a comprehensive bidirectional Mendelian randomization analysis

Footnotes

Acknowledgments

The generous assistance of the Young Elite Sponsorship Program of Shandong Provincial Medical Association and Qingdao Outstanding Health Professional Development Fund, which provided financial support for this research (Grant No. 2023_LC_0267 and 5325), is sincerely appreciated.

ORCID iD

Yongtao Zhang

Author contributions

Conceptualization, Z.H. and H.X.H.; methodology, L.M.Y. and C.G.H.; software,Z.Y.Z. and W.Z.; validation, Z.Z.A. and Y.T.B.; formal analysis, Z.H.; investigation, H.X.H.; resources, Z.Y.Z.; data curation, W.Z.; writing—original draft preparation, Z.H.; writing—review and editing, H.X.H and L.M.Y.; visualization, Z.Z.A.; supervision, Z.Y.T.; project administration, Y.T.B.; funding acquisition, Z.Y.T. All authors have read and agreed to the published version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for this study was provided by the Young Elite Sponsorship Program of Shandong Provincial Medical Association, China [grant number 2023_LC_0267] and supported by Qingdao Outstanding Health Professional Development Fund (5325).

Declaration of conflicting interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The authors assert that the investigation was carried out without the presence of any commercial or financial associations that could be interpreted as a possible clash of interests.

Supplemental Material

Supplemental material for this article is available online.

Data Availability Statement

The article (https://www.finngen.fi/en and ) incorporates the authentic findings of this research, and they are comprehensively documented in the supplementary material. For any additional queries, it is recommended to reach out to the corresponding authors.

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