Integrating Mendelian randomization and enzyme-linked immunosorbent assay validation identifies candidate mediators across the rheumatoid arthritis disease continuum

Abstract

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disease whose pathological process involves multiple consecutive stages, such as immune activation, local inflammation, and tissue destruction. Although inflammatory cytokines play a central role in the pathogenesis of RA, existing studies still lack an in-depth understanding of how key pathogenic factors exert their effects in a causal and stage-specific manner across the disease continuum.

AIM

To investigate causal links between inflammatory cytokines and RA via Mendelian randomization (MR) and experimental validation and propose a hypothesized spatiotemporal cascade model.

METHODS

A 2-sample MR analysis was conducted using genome-wide association study summary data to assess causal associations between 91 inflammatory cytokines and RA risk, with sensitivity analyses testing for heterogeneity and pleiotropy. For experimental validation, serum levels of CD40, C-C motif chemokine ligand 28 (CCL28), matrix metalloproteinase-1 (MMP-1), and interleukin-18 receptor 1 (IL-18R1) were quantified by enzyme-linked immunosorbent assay (ELISA) in 41 patients with active RA and 41 age- and sex-matched healthy controls. Intergroup differences and correlations were statistically evaluated.

RESULTS

MR analysis showed that CD40 [false discovery rate (FDR)-adjusted P =0.0020] and IL-18R1 (FDR-adjusted P = 0.0256) had significant causal associations with RA risk, while CCL28 and MMP-1 showed nominal associations (FDR-adjusted P > 0.05) and were considered exploratory. Sensitivity analyses supported the robustness of these findings, with no statistical evidence of heterogeneity or horizontal pleiotropy detected. ELISA results showed that serum levels of CD40, CCL28, MMP-1, and IL-18R1 in patients with RA were significantly higher than those in healthy controls (all P < 0.05), and there were significant positive correlations between all 6 pairs of the 4 molecules (r = 0.436-0.609, all P < 0.05).

CONCLUSION

Under its core assumptions, MR supports causal associations of CD40 and IL-18R1 with RA risk, while the associations for CCL28 and MMP-1 remain exploratory. The discordance between the MR and ELISA findings for IL-18R1 remains unresolved. A spatiotemporal model is proposed.

Key Words: Rheumatoid arthritis; Mendelian randomization; Inflammatory cytokines; CD40; C-C motif chemokine ligand 28; Matrix metalloproteinase-1; Interleukin-18 receptor 1; Enzyme-linked immunosorbent assay

Core Tip: This study integrates Mendelian randomization with enzyme-linked immunosorbent assay validation to identify CD40, C-C motif chemokine ligand 28, and matrix metalloproteinase-1 as candidate mediators in rheumatoid arthritis, while interleukin-18 receptor 1 shows discordant Mendelian randomization (protective) and enzyme-linked immunosorbent assay (elevated) findings requiring mechanistic reconciliation. A hypothesized spatiotemporal cascade links CD40 to lymphoid immunity, C-C motif chemokine ligand 28 to synovitis, and matrix metalloproteinase-1 to cartilage degradation. Their positive intercorrelations suggest a potential synergistic loop, supporting further exploration of composite biomarker panels and combination strategies. Experimental validation demonstrates protein-level consistency rather than proof of causality, and all findings should be considered hypothesis-generating.

INTRODUCTION

Rheumatoid arthritis (RA) is a chronic autoimmune disease. It is characterized primarily by persistent synovial inflammation and subsequent destruction of articular cartilage and bone. This ultimately leads to joint deformity and functional loss[1]. RA not only severely affects patients’ quality of life but can also trigger systemic complications such as cardiovascular disease and pulmonary fibrosis. With a global prevalence of approximately 0.5% to 1.0%, it imposes a heavy burden on society and families. The pathogenesis of RA is extremely complex. It involves interactions among genetic, environmental, and immune factors. Existing research has established that an imbalance in the immune cytokine network is a core driver of RA progression. Key mediators include tumor necrosis factor-α (TNF-α), interleukin (IL)-1, and IL-6[2,3]. These pro-inflammatory factors exacerbate joint damage by promoting synovial fibroblast proliferation, activating osteoclasts, and inducing matrix metalloproteinase (MMP) production. They thereby form a self-perpetuating inflammatory cycle[2,3].

Based on these findings, cytokine-targeted therapies such as anti-TNF biologics and IL-6 receptor inhibitors have become core treatment strategies for RA. They effectively alleviate symptoms and delay disease progression[4]. However, despite significant success, current treatments face major challenges due to the complexity of the RA inflammatory network. Most existing strategies aim for systemic immunosuppression. This approach not only overlooks the spatial organization and functional continuity of the disease process but also leads to large individual variations in treatment response and potential risks of side effects[5,6]. A critical knowledge gap remains regarding how certain inflammatory factors function in a stage- and site-specific manner. Genetic evidence suggests that these factors may be causal. Yet we do not know how they act across different anatomical sites (e.g., lymphoid tissue, synovium, cartilage) to propel the disease continuum—from systemic immune activation and local synovitis to final tissue destruction. This lack of a spatiotemporally resolved causal model severely hinders the development of novel therapeutic strategies. Such strategies would need to intervene precisely at specific stages and sites within the RA disease continuum.

In recent years, Mendelian randomization (MR), a genetic epidemiology method using single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs), has provided a new perspective for inferring the causal effects of exposures on diseases. It can help overcome confounding and reverse causation issues inherent in traditional observational studies[7,8]. Although existing MR studies have successfully identified several individual inflammatory cytokines associated with RA risk, these studies typically treat these factors as isolated entities. They do not integrate them into a continuous pathological model[9,10]. Moreover, the causal clues provided by genetic analyses lack systematic validation through clinical experimental evidence. This is particularly true regarding the interactions among these cytokines and their specific roles in the multistage pathogenic mechanisms of RA. Thus, a significant research gap remains.

Addressing these shortcomings, this study proposes an integrated immune-inflammation-tissue destruction spatiotemporal cascade and provides supportive evidence through both genetic and protein-level analyses. We utilized large-scale genome-wide association study (GWAS) data to identify inflammatory cytokines [CD40, C-C motif chemokine ligand 28 (CCL28), MMP-1, and IL-18R1] that show genetically predicted causal associations with RA risk under the MR framework. Building on this, we further employed a high-sensitivity enzyme-linked immunosorbent assay (ELISA) to quantitatively measure the expression levels of CD40, CCL28, MMP-1, and IL-18R1 in the serum of a case-control population (patients with RA vs healthy controls) to evaluate whether serum protein expression is consistent with the MR findings. This study attempts to extend traditional single-association approaches by integrating genetic clues with protein-level experimental evidence, with the goal of placing candidate factors into a broader spatiotemporal framework. We hypothesize that RA progression is driven by a series of causal mediators that play dominant roles at different anatomical sites: CD40 may contribute to adaptive immune activation in lymphoid tissues; CCL28 may promote inflammation locally in the synovium; and finally, MMP-1 may contribute to matrix degradation in cartilage tissue. Meanwhile, the role of IL-18R1 may span multiple stages, with its protective or pathogenic effects depending on dynamic changes in the local microenvironment. By testing this hypothesis, this study aims to elucidate the pathogenic or protective roles of these factors in RA pathogenesis. We also aim to provide supporting experimental evidence for the MR findings. Ultimately, we seek to provide a preliminary biological rationale for developing tissue-specific therapeutic strategies that may target distinct stages of the disease continuum.

MATERIALS AND METHODS

Data sources and study design

This study employed a 2-sample MR design to systematically investigate the potential causal relationship between circulating inflammatory cytokines and the risk of developing RA. Summary-level genetic association data were sourced from publicly available GWAS; no individual-level data were used. All original GWAS were approved by relevant institutional review boards and complied with the Declaration of Helsinki[11]. Genetic association data for inflammatory cytokines were derived from a large-scale GWAS evaluating 91 inflammatory cytokines[12]. RA summary statistics were obtained from the FinnGen biobank, specifically using the phenotype dataset “RHEUMA_SEROPOS_STRICT” (comprising 5103 strictly defined seropositive RA cases and 402554 controls of European ancestry) (https://r10.finngen.fi/pheno/RHEUMA_SEROPOS_STRICT)[13]. The MR analysis adhered to 3 core assumptions: (1) The selected genetic variants are strongly associated with the exposure (relevance); (2) the genetic IVs are independent of confounders (independence); and (3) the genetic variants influence the outcome (RA) only through the exposure, with no alternative pathways (exclusion restriction)[14]. A prespecified directed acyclic graph was constructed to illustrate the assumed causal relationships among genetic instruments, exposure, outcome, confounders, and potential pleiotropic pathways (Figure 1A). The analytical workflow of the 2-sample MR design is summarized in Figure 1B. Protein levels in the exposure GWAS were reported in normalized protein expression units, a log₂-scale measure of relative protein abundance as provided by the original GWAS. The odds ratios from the MR analysis represent the change in the odds of RA per 1 standard deviation increase in genetically predicted protein level (in normalized protein expression units). The exposure GWAS[12] and the outcome GWAS (FinnGen R10) were derived from distinct cohorts with no known sample overlap. The exposure GWAS included multiple European cohorts, while the FinnGen dataset is exclusively Finnish. Given the independent origins of these GWAS, substantial sample overlap is unlikely.

Figure 1 Description of the study design in this Mendelian randomization study. A: Mendelian randomization analyses depend on 3 core assumptions; B: Schematic of the study design. IVs: Instrumental variables; IVW: Inverse-variance weighted; kb: Kilobase; MR: Mendelian randomization; PRESSO: Pleiotropy RESidual Sum and Outlier; R²: Coefficient of determination; SNPs: Single-nucleotide polymorphisms.

Selection of IVs

We screened SNPs associated with inflammatory cytokines from GWAS summary data to serve as IVs. Given the low heritability of some target cytokines, applying a strict genome-wide significance threshold (P < 5 × 10^-8) would yield too few valid instruments, potentially compromising statistical power and introducing weak instrument bias. To ensure feasibility while maintaining an adequate number of genetic instruments, we therefore referenced methods from similar studies[15] and primarily adopted a more lenient threshold (P < 5 × 10^-6) for initial screening. We fully acknowledge that this strategy, compared with the gold standard, may increase the risk of type I error; consequently, we conducted stringent sensitivity tests in subsequent analyses to assess the robustness of the results.

To ensure the independence of the selected SNPs, linkage disequilibrium clumping was performed using a threshold of r² < 0.001 and a clumping window of 10000 kilobase pairs[16]. The strength of each IV was assessed using the F-statistic, and only SNPs with an F-statistic > 10 were retained to minimize potential weak instrument bias (Supplementary Table 1)[17]. Statistical power was assessed via the F-statistic for each IV, with all F-statistics exceeding the conventional threshold of 10, indicating adequate instrument strength. The sample sizes of the exposure GWAS (multiple European cohorts totaling > 10000 individuals) and the FinnGen outcome GWAS (5103 cases and 402554 controls) provided high statistical power to detect the reported effect sizes. Harmonization of exposure and outcome summary statistics was performed using the harmonise_data function in the TwoSampleMR R package. Palindromic SNPs (A/T and G/C) were first aligned based on allele frequencies using the 1000 Genomes European reference panel; those with a minor allele frequency > 0.42 were excluded to avoid potential strand-flip errors. This procedure aligned effect alleles between the exposure and outcome datasets to the same reference allele, ensured that effect sizes corresponded to the same direction of association, and checked for strand ambiguity. Only SNPs with concordant effect alleles across both datasets were retained for analysis. No proxy SNPs were used in this analysis; only directly genotyped or imputed SNPs that met the selection criteria were included as IVs.

MR analysis

Two-sample MR analysis was performed using the TwoSampleMR package and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) in R software (version 4.1.2). The primary analytical method was the random-effects inverse-variance weighted (IVW) model, which provides unbiased estimates under the balanced pleiotropy assumption. Additionally, sensitivity analyses were conducted using MR-Egger regression, the weighted median estimator, simple mode, and weighted mode methods[18] to assess the robustness of the causal estimates. To account for multiple testing in the exploratory MR screen of 91 inflammatory cytokines, Benjamini-Hochberg false discovery rate (FDR) correction was applied to the IVW P values. Findings with FDR-adjusted P < 0.05 were considered statistically significant; findings with nominal P < 0.05 but FDR-adjusted P ≥ 0.05 were interpreted as exploratory and hypothesis-generating, warranting independent validation in future studies.

Assessment of heterogeneity and pleiotropy

Heterogeneity among SNPs was assessed using Cochran’s Q test for the IVW analysis and Rucker’s Q test for the MR-Egger regression analysis. A P value > 0.05 indicated no statistically significant heterogeneity[19]. Directional horizontal pleiotropy was evaluated using the MR-Egger intercept test and the MR-PRESSO global test[20]. The MR-Egger intercept test results were not significant (P > 0.05), and MR-PRESSO analysis indicated no evidence of horizontal pleiotropy. Steiger directionality testing was performed to confirm the assumed causal direction from inflammatory cytokines to RA.

Sensitivity and robustness analysis

To further assess the robustness of the MR study results, we performed a leave-one-out analysis, sequentially excluding each SNP to determine whether any single variant disproportionately influenced the causal estimate. If no disproportionate influence was found, the results were deemed stable[21].

Study sample

This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University, No. 2026-E0384. An independent case-control cohort was established for this study. The experimental group comprised 41 patients with active RA [Disease Activity Score 28-joint count using Erythrocyte Sedimentation Rate (DAS28-ESR) > 2.6] who met the 2010 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria. All patients were treatment-naive, with no prior use of disease-modifying antirheumatic drugs, glucocorticoids, or biologics, and had RA as the sole autoimmune disease, without other concurrent autoimmune disorders or comorbidities (hypertension, diabetes, or cardiovascular, hepatic, renal, or malignant diseases). Patients with acute or chronic infections within the preceding 4 weeks, pregnancy or lactation, or contraindications to blood sampling were excluded. The control group consisted of 41 age- and sex-matched healthy volunteers undergoing routine health examinations at the same center, with no history of autoimmune diseases, chronic inflammatory conditions, or malignancies. All patients with RA were recruited from the Department of Rheumatology and Immunology, and healthy controls were recruited from the Health Examination Center of the First Affiliated Hospital of Guangxi Medical University between 2024 and 2025. Demographic and clinical data, including age, sex, smoking history, disease duration, and DAS28-ESR scores, were systematically collected for all patients with RA.

Serum sample collection and processing

Serum levels of CD40, IL-18R1, CCL28, and MMP-1 were quantified using commercial ELISA kits (Ruixin Biotechnology, Quanzhou, China; catalog Nos. RXM106392H, RXM2D199886, RXM2D1012826, and RXM105780H, respectively) according to the manufacturer’s protocols. Calibration ranges and detection limits were as follows: CD40, 10-320 pg/mL and < 1.0 pg/mL; IL-18R1, 6.25-200 pg/mL and < 1.0 pg/mL; CCL28, 93.75-3000 pg/mL and < 10 pg/mL; and MMP-1, 0.25-8 ng/mL and < 0.1 ng/mL. Frozen serum samples were thawed, centrifuged (3000 r/minute, 15 minutes), and tested without dilution; samples exceeding the upper limit of the calibration curve were reassayed at appropriate dilutions, with final concentrations adjusted accordingly. Following incubation and washing (Tianshi 988 washer; Beijing Tuopu Analytical Instruments), optical density was read at 450 nm using a Rayto RT-6100 microplate reader (Rayto Life and Analytical Sciences). Standard curves were fitted using a 4-parameter logistic model. Intra- and inter-assay coefficients of variation were < 10% and < 15%, respectively, as reported by the manufacturer. All samples were processed in a single batch using identical lot numbers to minimize inter-batch variability. The operator was blinded to clinical information and group assignments throughout the entire procedure.

Statistical analysis

SPSS version 26.0 (IBM Corp.) and GraphPad Prism version 9 (GraphPad Software) were used for analysis. Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data are presented as mean ± SD, and group comparisons were performed using independent-samples t-tests; non-normally distributed data were compared using the Mann-Whitney U test. Pearson correlation analysis was used to assess correlations among serum levels of CD40, CCL28, MMP-1, and IL-18R1. All statistical analyses were 2-sided, and P < 0.05 was considered statistically significant.

RESULTS

MR study results

This 2-sample MR analysis systematically evaluated the causal effects of 91 inflammatory cytokines on RA risk. The primary analysis used the IVW random-effects model, supplemented by sensitivity analyses using MR-Egger regression, the weighted median estimator, simple mode, and weighted mode methods. MR analysis showed that CD40 [odds ratio (OR) = 1.1893, 95% confidence interval (CI): 1.0823-1.3069, nominal P = 0.0003; FDR-adjusted P = 0.0020] and IL-18R1 (OR = 0.9160, 95%CI: 0.8582-0.9777, nominal P = 0.0084; FDR-adjusted P = 0.0256) had significant causal associations with RA risk after FDR correction. CCL28 (OR = 1.1468, 95%CI: 1.0066-1.3066, nominal P = 0.0396; FDR-adjusted P = 0.0989) and MMP-1 (OR = 1.1333, 95%CI: 1.0107-1.2708, nominal P = 0.0321; FDR-adjusted P = 0.0850) showed nominal associations that did not survive correction and are therefore considered exploratory findings requiring independent validation (Supplementary Tables 2-4).

Sensitivity analysis and robustness

The observed associations for CD40, CCL28, MMP-1, and IL-18R1 were robust across multiple sensitivity analyses. MR-Egger regression, weighted median, simple mode, and weighted mode methods all yielded consistent effect directions, supporting the validity and reliability of the IVW estimates (Figure 2, Supplementary Figures 1-4).

Figure 2 Forest plot of Mendelian randomization estimates for the causal effects of C-C motif chemokine ligand 28, CD40, interleukin-18 receptor 1, and matrix metalloproteinase-1 on rheumatoid arthritis risk. Effect sizes (odds ratios) and 95% confidence intervals are shown for the inverse-variance weighted, Mendelian randomization-Egger, weighted median, simple mode, and weighted mode estimators. CCL28: C-C motif chemokine ligand 28; CI: Confidence interval; IL-18R1: Interleukin-18 receptor 1; MMP-1: Matrix metalloproteinase-1; MR: Mendelian randomization; nsnp: Number of single-nucleotide polymorphisms; OR: Odds ratio.

Heterogeneity assessment

No statistically significant heterogeneity was detected in the associations between the 4 cytokines and RA risk. Cochran’s Q test results showed no significant heterogeneity at the IV level for CD40 (Q = 30.752, P = 0.101), CCL28 (Q = 43.800, P = 0.175), MMP-1 (Q = 26.526, P = 0.380), and IL-18R1 (Q = 5.757, P = 0.889). Similarly, Rucker’s Q test showed no statistically significant heterogeneity for CD40 (Q = 29.938, P = 0.093), CCL28 (Q = 42.298, P = 0.185), MMP-1 (Q = 25.900, P = 0.358), and IL-18R1 (Q = 4.730, P = 0.908) (Supplementary Figure 5, Supplementary Table 5).

Assessment of horizontal pleiotropy

No evidence of horizontal pleiotropy was found. As shown in Supplementary Figure 6 and Supplementary Table 6, the MR-Egger intercept test showed no statistically significant intercepts for CD40 (intercept = -0.007, P = 0.458), CCL28 (intercept = 0.014, P = 0.276), MMP-1 (intercept = -0.009, P = 0.454), and IL-18R1 (intercept = -0.016, P = 0.335). Furthermore, the MR-PRESSO global test provided no statistical evidence of horizontal pleiotropy for CD40 [observed residual sum of squares (RSS) = 50.014, P = 0.163], CCL28 (observed RSS = 46.061, P = 0.171), MMP-1 (observed RSS = 28.216, P = 0.404), and IL-18R1 (observed RSS = 8.698, P = 0.828) (Supplementary Table 7). Steiger directionality testing confirmed the assumed causal direction for all 4 cytokines, with a proportion correct of 1.0 for each (Table 1).

Table 1 Summary of heterogeneity and horizontal pleiotropy test results for CD40, C-C motif chemokine ligand 28, matrix metalloproteinase-1, and interleukin-18 receptor 1.

Exposure	Outcome	Heterogeneity tests		Horizontal pleiotropy test	MR-PRESSO		Steiger directionality (proportion correct)
		Cochran’s Q test (P value, IVW)	Rucker’s Q test (P value, MR-Egger)	MR-Egger intercept (P value)	Distortion test (outliers)	Global test (P value)
CD40	RA	0.101	0.093	0.458	NA	0.163	1.0
CCL28	RA	0.175	0.185	0.276	NA	0.171	1.0
MMP-1	RA	0.380	0.358	0.454	NA	0.404	1.0
IL-18R1	RA	0.889	0.908	0.335	NA	0.828	1.0

CCL28: C-C motif chemokine ligand 28; IL-18R1: Interleukin-18 receptor 1; IVW: Inverse-variance weighted; MMP-1: Matrix metalloproteinase-1; MR: Mendelian randomization; MR-PRESSO: Mendelian randomization Pleiotropy RESidual Sum and Outlier; NA: Not available; RA: Rheumatoid arthritis.

Robustness of causal inference

Leave-one-out analysis indicated that the causal estimates for the 4 cytokines were not significantly influenced by any single SNP, further supporting the robustness of the MR study results (Supplementary Figure 7)[22]. Complete results of the heterogeneity and pleiotropy tests are summarized in Table 1.

Baseline characteristics of the study population

In total, 82 participants were enrolled in this study, comprising 41 patients with active RA and 41 age- and sex-matched healthy controls. There were no statistically significant differences between the 2 groups in age, sex distribution, body mass index, or smoking status (all P > 0.05), indicating successful matching and comparability of baseline demographic characteristics. The median disease duration for patients with RA was 6 months (interquartile range: 2-8 months), and the mean DAS28-ESR score was 5.20 ± 1.27, reflecting moderate to high disease activity. Detailed baseline characteristics are presented in Table 2.

Table 2 Baseline characteristics of the study population.

Characteristics	RA group (n = 41)	Control group (n = 41)	P value
Sex, female	33 (80.49)	33 (80.49)	1.000
Age, years	56.20 ± 13.28	52.20 ± 10.33	0.132
BMI, kg/m2	22.41 ± 3.31	22.37 ± 2.92	0.346
Smoking status	5 (12.20)	4 (9.76)	1.000
Disease duration, months	6 (2, 8)	-	-
DAS28-ESR	5.20 ± 1.27	-	-

Data are presented as n (%), mean ± SD, or median (interquartile range).

BMI: Body mass index; DAS28-ESR: Disease Activity Score 28-joint count using erythrocyte sedimentation rate; RA: Rheumatoid arthritis.

Comparison of serum inflammatory cytokine levels

To validate the candidate causal pathways identified by MR, serum concentrations of CD40, CCL28, MMP-1, and IL-18R1 were measured using commercial ELISA kits. Normality was confirmed via the Shapiro-Wilk test for all 4 cytokines (all P > 0.05), and intergroup comparisons were performed using independent-samples t-tests.

All 4 cytokines were significantly elevated in the active RA group (n = 41) compared with healthy controls (n = 41). The mean CD40 level in patients with RA was 341.78 ± 69.99 pg/mL vs 207.12 ± 72.46 pg/mL in controls (mean difference: 134.66 pg/mL; 95%CI: 103.35-165.97 pg/mL; P < 0.0001). For CCL28, the RA group showed a mean of 3562.13 ± 699.25 pg/mL vs 1891.77 ± 523.32 pg/mL in controls (mean difference: 1670.36 pg/mL; 95%CI: 1398.91-1941.80 pg/mL; P < 0.0001). MMP-1 levels were 8.67 ± 1.68 ng/mL in patients with RA and 5.42 ± 1.29 ng/mL in controls (mean difference: 3.26 ng/mL; 95%CI: 2.60-3.92 ng/mL; P < 0.0001). IL-18R1 concentrations averaged 238.37 ± 47.47 pg/mL in the RA group and 140.40 ± 40.41 pg/mL in the control group (mean difference: 97.97 pg/mL; 95%CI: 78.59-117.34 pg/mL; P < 0.0001) (Figure 3).

Figure 3 Comparison of serum inflammatory cytokine levels between patients with active rheumatoid arthritis and healthy controls. Serum concentrations were measured by enzyme-linked immunosorbent assay in 41 patients with rheumatoid arthritis and 41 matched controls. All 4 cytokines were significantly elevated in the rheumatoid arthritis group compared with the control group: CD40: 341.78 ± 69.99 pg/mL vs 207.12 ± 72.46 pg/mL, mean difference = 134.66 pg/mL [95% confidence interval (CI): 103.35-165.97; P < 0.0001]; C-C motif chemokine ligand 28: 3562.13 ± 699.25 pg/mL vs 1891.77 ± 523.32 pg/mL, mean difference = 1670.36 pg/mL (95%CI: 1398.91-1941.80; P < 0.0001); matrix metalloproteinase-1: 8.67 ± 1.68 ng/mL vs 5.42 ± 1.29 ng/mL, mean difference = 3.26 ng/mL (95%CI: 2.60-3.92; P < 0.0001); interleukin-18 receptor 1: 238.37 ± 47.47 pg/mL vs 140.40 ± 40.41 pg/mL, mean difference = 97.97 pg/mL (95%CI: 78.59-117.34; P < 0.0001). A: CD40; B: C-C motif chemokine ligand 28 (CCL28); C: Matrix metalloproteinase-1 (MMP-1); D: Interleukin-18 receptor 1 (IL-18R1). RA: Rheumatoid arthritis.

Correlation analysis among key inflammatory cytokines

To further explore the internal relationships within the causal pathways suggested by MR, we analyzed the pairwise associations among the 4 key inflammatory cytokines CD40, CCL28, MMP-1, and IL-18R1 in serum. Based on the data distribution characteristics, Pearson correlation coefficients were used for assessment. The analysis showed statistically significant positive correlations between all 6 cytokine pairs (all P < 0.0001), with correlation coefficients (r) ranging from 0.436 to 0.609 (Figure 4). This tightly interconnected network suggests that these factors do not act in isolation in the RA pathological process but may synergistically participate in and amplify inflammatory signals, thereby providing experimental evidence supporting the “inflammatory cascade” hypothesis.

Figure 4 Correlation analysis of serum inflammatory cytokine levels in 41 patients with rheumatoid arthritis and 41 healthy controls (n = 82). Pearson correlation coefficients showed significant positive correlations among all pairs (r = 0.436-0.609, all P < 0.0001). A: Correlation between CD40 and C-C motif chemokine ligand 28 (CCL28); B: Correlation between CD40 and matrix metalloproteinase-1 (MMP-1); C: Correlation between CCL28 and MMP-1; D: Correlation between CD40 and interleukin-18 receptor 1 (IL-18R1); E: Correlation between CCL28 and IL-18R1; F: Correlation between MMP-1 and IL-18R1. CI: Confidence interval.

DISCUSSION

Integrating MR with ELISA experimental validation, this study systematically explored the potential causal roles and possible therapeutic implications of 4 inflammation-related molecules—CD40, CCL28, MMP-1, and IL-18R1—in the pathogenesis of RA. MR analyses provided FDR-corrected evidence supporting a positive causal association between CD40 and RA risk, as well as a protective association for IL-18R1, whereas CCL28 and MMP-1 showed nominal associations that did not survive correction for multiple testing and should therefore be interpreted as exploratory and hypothesis-generating. ELISA experiments further demonstrated that the serum expression changes of these proteins in patients with RA were directionally consistent with the MR findings, and they also revealed their interaction network, offering an integrative perspective on the multistage pathological processes of RA (Figure 5).

Figure 5 Proposed integrated spatiotemporal model of inflammatory mediator pathways potentially involved in rheumatoid arthritis. Combining Mendelian randomization (MR) analysis (which supports causal inference only under its core assumptions) with clinical enzyme-linked immunosorbent assay (ELISA) validation suggests CD40, C-C motif chemokine ligand 28 (CCL28), and matrix metalloproteinase-1 (MMP-1) as candidate mediators, and interleukin-18 receptor 1 (IL-18R1) as a context-dependent factor. These molecules are hypothesized to drive a compartmentalized 3-stage cascade: (1) CD40 may contribute to adaptive immune activation in lymphoid tissues, (2) CCL28 may amplify local synovial inflammation by recruiting immune effectors (e.g., monocytes, Th22 cells), and (3) MMP-1 may mediate cartilage matrix degradation and erosion. IL-18R1 may exert context-dependent modulatory effects. This proposed “immune-inflammation-tissue destruction” axis offers a hypothesis-generating framework for exploring stage- and tissue-associated biological mechanisms. As with all MR-based inferences, these findings should be considered supportive, and the experimental validation demonstrates consistency with, rather than proof of, the candidate relationships. APC: Antigen-presenting cell; APPC: Activated antigen-presenting cell; RA: Rheumatoid arthritis; SNPs: Single-nucleotide polymorphisms.

MR analyses provided FDR-corrected evidence supporting a positive causal association between CD40 and RA risk, which is consistent with previous preclinical and clinical observations[23,24]. In line with this, ELISA experiments revealed significantly elevated CD40 protein levels in the serum of patients with RA, supporting the consistency between genetic polymorphisms and protein-level functional changes and thereby complementing prior studies that relied solely on genetic association data[25]. CD40, a member of the TNF receptor superfamily (TNF receptor superfamily member 5), is expressed on dendritic cells, macrophages, and B cells, where it mediates immune cell activation and proliferation, contributing to adaptive immunity and inflammatory cascades[26-28]. Notably, we observed that CD40 levels positively correlated with both CCL28 and MMP-1 levels, hinting at possible synergistic effects. Mechanistically, CD40-activated B cells may secrete IL-6, which in turn upregulates CCL28 expression, potentially forming a positive feedback loop between B-cell responses and chemokine-driven inflammation[29,30]. The CD40/CD40 L interaction is hypothesized to exert dual effects: On one hand, it may inhibit synovial cell apoptosis and sustain inflammation via the TNF receptor-associated factor 1 signaling pathway[31]; on the other hand, it may promote autoreactive T-cell activation and induce phenotypic alterations in fibroblast-like synoviocytes through nuclear factor kappa B (NF-κB) signaling, thereby upregulating MMP-1 expression and contributing to inflammatory responses and bone erosion[32-34]. Together, these findings suggest that CD40 may serve not only as a genetic susceptibility marker but also as an important regulatory node in RA disease activity and inflammatory networks. Its interplay with chemokines may represent a key hub within the pathological immune landscape. These mechanistic links provide a theoretical rationale for exploring CD40/CD40 L-targeted interventions, although further pharmacogenomic studies are needed to identify predictors of response to such therapies.

This study found nominally significant MR-based evidence suggesting a potential causal relationship between elevated CCL28 levels and increased RA risk, which aligns with recent observational data[35]. However, because this association did not survive FDR correction for multiple testing (FDR-adjusted P = 0.0989), it must be considered exploratory and hypothesis-generating rather than confirmatory. ELISA experiments further showed significantly higher CCL28 concentrations in the serum of patients with RA, with positive correlations with MMP-1 levels, suggesting that CCL28 may participate in amplifying inflammatory signals through downstream effector molecules. As a chemokine, CCL28 is known to mediate immune cell migration and synovial inflammation via its receptors CCR3 and CCR10[36-38]. In the RA microenvironment, overexpression of CCL28 by macrophages and synovial fibroblasts may promote monocyte recruitment, angiogenesis, and osteoclastogenesis through CCR10-dependent extracellular signal-regulated kinase and phosphoinositide 3-kinase/protein kinase B pathways[39]. Moreover, Th22 cells attracted by the CCL28 gradient may secrete IL-22, potentially aggravating cartilage degradation[37]. Importantly, the positive correlation we observed between CCL28 and MMP-1 suggests that CCL28 might serve as a molecular link between inflammation and tissue destruction, possibly by modulating MMP-1 expression, thus reflecting a dual role in both local inflammatory amplification and matrix degradation. At the regulatory level, CD40/CD40 L signaling could enhance CCL28 production, thereby reinforcing synovial inflammation through a positive feedback loop[40-42]. Collectively, these findings expand the proposed functional role of CCL28 in RA beyond chemotaxis to include synergistic contributions to inflammation and joint destruction, providing a molecular rationale for future exploration—though, given the exploratory nature of the MR finding, these mechanistic interpretations require independent validation in larger, well-powered genetic and functional studies before definitive conclusions can be drawn.

This study provided supportive evidence that MMP-1 may act as a key mediator of joint tissue destruction in RA. Its concentration was significantly elevated in the serum of patients with RA and positively correlated with CD40 and CCL28 levels, which is consistent with previously reported associations with RA[43-45]. As with CCL28, the MR association for MMP-1 did not survive FDR correction (FDR-adjusted P = 0.0850); therefore, this finding should be interpreted as exploratory and hypothesis-generating. As a core member of the MMP family, MMP-1 is known to degrade extracellular matrix components such as collagen, thereby contributing to cartilage and bone damage[46]. Using ELISA, we observed upregulated MMP-1 protein levels in clinical samples, and the interindividual variability in its expression may reflect heterogeneity in disease progression across patients, complementing previous transcriptomic and animal-model studies[47]. At the regulatory level, pro-inflammatory cytokines, including IL-1β, TNF-α, and IL-6, can induce MMP-1 production in macrophages and fibroblast-like synoviocytes, promoting collagen breakdown and cartilage invasion[43,45]. Notably, CD40 and CCL28 may upregulate MMP-1 expression through signaling pathways such as c-Jun N-terminal kinase/NF-κB, mitogen-activated protein kinase, extracellular signal-regulated kinase, and phosphoinositide 3-kinase/protein kinase B[36,45], suggesting that MMP-1 serves as a downstream effector within an inflammation-to-destruction axis. Hence, MMP-1 may represent not only a destructive factor but also a key output node through which inflammatory signals are translated into tissue damage. These observations raise the possibility that selective MMP-1 inhibitors could alleviate cartilage erosion and improve joint function in advanced RA by targeting this pro-inflammatory-destructive axis, though this hypothesis requires further preclinical and clinical investigation. Critically, because the MR evidence for MMP-1 is exploratory, any therapeutic implications should be considered provisional and contingent upon replication in future studies.

The present study also shed light on the complex regulatory role of IL-18R1 in RA, particularly regarding the apparent discordance between its MR-based protective association (OR = 0.9160, FDR-adjusted P = 0.0256) and its elevated serum levels detected by ELISA (238.37 ± 47.47 pg/mL vs 140.40 ± 40.41 pg/mL, P < 0.0001). Of note, unlike CCL28 and MMP-1, the IL-18R1 MR association survived FDR correction, providing stronger genetic evidence for a protective effect. Reconciling these seemingly contradictory findings is challenging; rather than offering definitive explanations, we propose the following hypotheses to guide future investigation. First, elevated IL-18R1 levels in patients with RA may represent a compensatory anti-inflammatory response to chronic inflammation, serving as an endogenous attempt to restore immune homeostasis rather than acting as a purely pathogenic mediator. Second, IL-18R1 signaling is likely context- and dose-dependent: While IL-18/IL-18R1 signaling can activate pro-inflammatory cascades via MyD88 and NF-κB[48,49], IL-18R1 may also exert dual regulatory functions—promoting Th1 differentiation and interferon gamma production in some contexts, yet inducing regulatory cytokines that restrain inflammation in others[48]. Under physiological conditions, IL-18R1 might negatively modulate nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3 inflammasome activity to preserve immune homeostasis[50], whereas excessive ligand availability could overcome this regulatory threshold, leading to pathological inflammation[51]. Third, the discrepancy between MR and ELISA may reflect fundamental methodological differences: MR captures lifelong genetically determined IL-18R1 function at the population level, whereas ELISA measures transient serum protein levels influenced by acute inflammatory states, which may not reflect functional activity at the tissue level. Additionally, serum IL-18R1 may represent shed receptors rather than functional signaling. Fourth, IL-18R1 expression on different cell types may mediate opposing effects—for instance, enhancing regulatory T-cell function vs promoting Th1-mediated inflammation—and the net serum level may represent a summation of these opposing effects, complicating interpretation. These hypotheses are not mutually exclusive and may operate concurrently. Importantly, our findings do not confirm any of these mechanisms; rather, they highlight the need for mechanistic studies—including cell-type-specific functional assays and longitudinal concentration-response analyses—to systematically dissect the complex role of IL-18R1 in RA pathogenesis.

Based on the above integrative evidence, we propose a hypothetical working model of an immune-inflammation-tissue destruction spatiotemporal cascade, in which different molecules may exert site-specific functions during RA pathogenesis: CD40 is hypothesized to predominantly initiate adaptive immune activation in lymphoid tissues[52-54], CCL28 is hypothesized to attract inflammatory cells and amplify local synovial inflammation[35], and MMP-1 is hypothesized to act directly on articular cartilage to mediate matrix degradation[55,56]. It must be emphasized, however, that the proposed roles for CCL28 and MMP-1 in this cascade are based on exploratory MR findings and correlational ELISA data; as such, this model should be viewed as a hypothesis-generating framework rather than an established pathophysiological paradigm. This spatiotemporal framework highlights the anatomical specificity of each molecule and may inform the design of tissue-directed therapeutic approaches. The role of IL-18R1, however, could span multiple stages, and its tissue- and cell-type-specific contributions remain to be clarified[50,51]. Its dual protective and pathogenic effects may be modulated by dynamic shifts in the local microenvironment. Overall, this conceptual model offers a spatiotemporal perspective on the RA pathological cascade and may provide guidance for the future development of precision immunotherapy strategies, though it remains largely hypothesis-generating at this stage.

Given the positive correlations and regulatory crosstalk among CD40, CCL28, and MMP-1, one might speculate that combination strategies targeting 2 or more of these mediators may offer broader and more durable therapeutic effects than single-agent approaches, potentially disrupting the inflammatory cascade at multiple nodes simultaneously. However, such combinatorial regimens must be carefully balanced against the risk of excessive immunosuppression, and their optimal composition, dosing, and patient selection require systematic preclinical evaluation and subsequent clinical validation. Likewise, the observed correlations provisionally support the development of composite biomarker panels that reflect the integrated activity of this axis, which could aid in stratifying patients for future combination trials—though this application also requires validation in larger, independent cohorts.

This study combined MR and ELISA methods, allowing us to infer potential causality under MR assumptions from genetic data while simultaneously demonstrating expression differences at the protein level, which strengthens the credibility and translational relevance of our observations. The high sensitivity and specificity of ELISA contributed to data accuracy and reproducibility, and the MR design helped mitigate confounding and reverse causation. Based on these causal-inference-oriented findings, among the 4 molecules, CD40 (FDR-corrected) and IL-18R1 (FDR-corrected) have the strongest genetic support for their respective associations; CCL28 and MMP-1, while showing nominally significant MR signals and consistent protein-level changes, require independent replication before they can be confidently considered causal mediators. CD40, CCL28, and MMP-1 may serve not only as biomarkers but also as candidate therapeutic targets, though this suggestion is more firmly grounded for CD40 than for the exploratory CCL28 and MMP-1 findings. Future work could explore biomarker panels incorporating these molecules to guide patient stratification—for instance, immunomodulatory strategies for individuals with high CD40 expression, local anti-inflammatory approaches for those with elevated CCL28, or cartilage-protective interventions for patients showing high MMP-1 levels—although such applications remain speculative and require prospective validation, particularly given the exploratory nature of the CCL28 and MMP-1 MR findings.

Several limitations should be considered when interpreting our results, and these caveats reinforce that our findings should be viewed as supportive rather than definitive. First, regarding sample representativeness and statistical power, while our ELISA validation cohort comprised 41 patients with RA and 41 matched healthy controls and successfully detected significant expression differences, this relatively modest sample size inherently limits the statistical power for more granular analyses, such as stratification by disease subtypes or activity stages. Consequently, the observed correlations and effect sizes, although robust in our setting, should be interpreted with caution and may not fully capture the heterogeneity of the RA population. More importantly, this sample size, while adequate for preliminary mechanistic validation, is insufficient to establish the clinical utility of these molecules as routine biomarkers. Before considering clinical implementation, larger, independent, and preferably multicenter prospective cohorts are urgently needed to rigorously evaluate the diagnostic accuracy, prognostic value, and predictive performance of CD40, CCL28, MMP-1, and IL-18R1 and to determine their added value over existing clinical parameters. Second, the generalizability of our findings across different ethnic populations warrants careful consideration. The MR analyses in this study were based on summary statistics from the FinnGen biobank. Given that genetic backgrounds, allele frequencies, environmental exposures, and immune response profiles can differ substantially across ancestries, the effect sizes and even the directionality of causal associations observed in European populations may not directly translate to other ethnic groups. Therefore, replication of our MR findings in non-European GWAS datasets and validation in diverse, multiethnic clinical cohorts are essential to assess the cross-population validity of our causal inferences and biomarker candidates. Third, methodologically, this study employed a relatively liberal genome-wide significance threshold to expand the pool of IVs. Although a series of sensitivity analyses supported the robustness of the results, cautious interpretation is still needed. This is particularly relevant for CCL28 and MMP-1, whose nominal MR associations did not survive FDR correction; the liberal SNP selection threshold may have contributed to these exploratory findings, underscoring the need for replication in future studies using more stringent instrument selection criteria where feasible. Although we applied FDR correction to control the FDR in the 91-protein MR screen, the exploratory nature of this screening approach means that the nominal findings for CCL28 and MMP-1 require independent validation in larger, well-powered studies before definitive conclusions can be drawn. Future research is expected to identify stronger IVs through larger-scale GWASs to further validate the conclusions of this study. Fourth, the study design lacked long-term follow-up data, making it difficult to assess the dynamic changes in target molecules during RA disease progression and their long-term impact on prognosis. Moreover, this study examined only a limited number of inflammatory cytokines, failing to cover the complex inflammatory network of RA. Finally, batch effects between datasets may also affect the generalizability of the results. In summary, subsequent research needs to be conducted in larger, more diverse populations with long-term follow-up and combined with functional experiments to comprehensively elucidate the roles of the aforementioned molecules in RA pathological mechanisms.

CONCLUSION

In summary, by integrating MR with ELISA-based experimental validation in a preliminary cohort, this study provides FDR-corrected genetic evidence for a causal role of CD40 in RA pathogenesis and a protective association for IL-18R1. CCL28 and MMP-1 showed nominally significant but exploratory associations that require independent replication in larger, well-powered studies. For IL-18R1, the discordance between MR and ELISA findings precludes any definitive conclusion regarding its net effect; the hypotheses discussed above require rigorous mechanistic evaluation before its role in RA can be established. It is important to emphasize that MR supports causal inference only under its core assumptions, and the experimental validation demonstrates consistency with, rather than proof of, causality. Nevertheless, these findings, together with the proposed interaction network model, offer a working framework for understanding stage-specific disease progression in RA and provide a foundation for future mechanistic and therapeutic studies.

ACKNOWLEDGMENTS

We express our profound gratitude to the patients, healthy controls, and their families for their indispensable participation in this study.