Causal relationship between circulating cytokines and the risk of systemic sclerosis: A bidirectional Mendelian-randomization study

doi:10.21203/rs.3.rs-3754606/v1

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Causal relationship between circulating cytokines and the risk of systemic sclerosis: A bidirectional Mendelian-randomization study

https://doi.org/10.21203/rs.3.rs-3754606/v1

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Objectives

Systemic sclerosis (SSc) is an autoimmune disease characterized by immune dysfunction, vasculopathy, and fibrosis. While cytokines likely contribute to SSc pathogenesis, it is unclear whether particular cytokines play a causal role. We aimed to investigate potential causal relationships between circulating cytokines and SSc using Mendelian randomization.

Methods

We conducted a bidirectional two-sample Mendelian randomization study using summary data from published genome-wide association studies of SSc (2,313 cases, 15,881 controls) and 43 cytokines (up to 31,781 individuals). Single nucleotide polymorphisms associated with cytokine levels were selected as instruments to proxy genetically determined circulating concentrations. Potential causal relationships between exposures and outcomes were investigated primarily through inverse variance weighted Mendelian randomization analysis. To substantiate the robustness of our findings, we additionally conducted several complementary sensitivity analyses utilizing alternative Mendelian randomization methods, including MR-Egger, weighted median, simple and weighted mode, and MR-PRESSO.

Results

Among the cytokines analyzed, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) demonstrated a significant causal association, whereby genetically predicted lower TRAIL levels increased SSc risk. Other circulating cytokines including TGF-β, IL-6, and PDGF, have no causal relationship with SSc.

Conclusion

Our findings implicate reduced circulating TRAIL level as a potential causal factor in SSc risk, providing novel insights into disease pathogenesis. These findings position TRAIL as a putative upstream regulator in SSc, suggesting modulation of the TRAIL signaling cascade may represent a promising therapeutic approach in SSc.

Systemic sclerosis

cytokines

TRAIL

Mendelian randomization

GWAS

Systemic sclerosis (SSc), also known as scleroderma, is a rare, chronic autoimmune disease characterized by progressive fibrosis of the skin and internal organs, resulting in the highest mortality among rheumatic diseases[1]. Three major abnormalities, inflammation, fibrosis, and vasculopathy, are considered the most important pathological changes in SSc[2]. The heterogeneity of the disease state and organs involved in individual patients limits the understanding of causal onset in this disease. To date, the etiology of SSc is not well understood, and the complex interaction between genetic susceptibility and environmental factors is thought to play an important role in the development of the disease[1, 3, 4]. Several genome-wide screening studies indicated specific nucleotide polymorphisms (SNPs) in relevant genes related to SSc disease, such as genes coding for cytokines, chemokines, chemokine receptors, growth factors, and extracellular matrix (ECM) proteins[5, 6].

The dysregulation of circulating cytokine production and release plays an important role throughout the pathological progression of SSc, such as TGF-β, IL-6, PDGF, and TRAIL[2, 5, 7, 8]. As one of the most important profibrotic factors, TGF-βis significantly elevated in both the skin and peripheral blood of SSc patients[9, 10]. In SSc-ILD patients, IL-6 is significantly elevated in serum and peripheral blood mononuclear cell (PBMC) culture supernatants[11–13]. PDGF itself and its receptors are expressed in alveolar lavage fluids and the skin of SSc patients[14, 15]. Moreover, a study containing 30 patients with SSc showed that serum TRAIL levels were significantly elevated in SSc patients and were associated with SSc-ILD[16]. However, it remains unclear whether any circulating cytokine is the initiator of SSc.

Mendelian randomization study, which is considered randomized trial without intervention, provides convincing evidence of causal relationships between exposure factors and outcomes through instrumental variables, usually genetic variation[17, 18]. In this study, we extracted effective genetic variants from genome-wide association studies (GWAS) data of 47 inflammatory cytokines to further explore the causal relationship between cytokines and systemic sclerosis through a two-sample Mendelian randomization study. Our results suggest that downregulation of TRAIL may be associated with the onset of SSc.

Study population and data sources

This bidirectional, two-sample Mendelian randomization study utilized summary-level data from large-scale GWAS on systemic sclerosis and circulating cytokine levels. SSc GWAS data were obtained from Nakashima et al. (including 2,313 SSc cases and 15,881 controls of European ancestry), while cytokine GWAS data were derived from Ahola-Olli et al. (comprising up to 31,781 individuals of European descent). The cytokine GWAS data for TGF-β, PDGF-AA, PDGF-CC, PDGF-Rb were derived from Suhre K et al. (comprising up to 3,080 individuals of European descent).

SNP selection and quality control

Genetic instruments were selected based on the following criteria: i) a GWAS-correlated p value of 5×10^− 8. Since only a limited number of cytokines had more than two independent SNPs at this threshold, we adopted a more liberal significance level of p < 5×10^− 6 to expand the pool of eligible instrumental variables (IVs), as has been previously suggested (Supplementary Table S1). ii) Linkage disequilibrium (LD) r2 < 0.001 and < 1 MB distance from the index variant. To limit weak instrument bias, SNPs with first-stage F-statistics < 10 were excluded. SNPs associated with potential pleiotropic pathways identified through PhenoScanner screening were also filtered out. Distinct sets of nonoverlapping, LD pruned SNPs were utilized for the separate Mendelian randomization analyses of each cytokine. After removing cortical structure-related SNPs at the 5×10^− 8 threshold, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) testing was conducted to eliminate SNPs with evidence of horizontal pleiotropy prior to analysis. MR-PRESSO is optimally applicable when < 50% of instruments exhibit pleiotropy. The study framework is shown in Fig. 1.

Schematic of this bidirectional Mendelian randomization study design. SNPs that significantly associated with 47 inflammatory cytokines and systemic sclerosis were selected as instrumental variables, satisfying the relevance assumption. The SNPs are expected to be independent of confounders, meeting the independence assumption. The SNPs are anticipated to affect systemic sclerosis only through these circulating inflammatory cytokines, consistent with the exclusion restriction assumption. This bidirectional Mendelian randomization approach enabled exploration of the potential causal effects of the 47 cytokines on systemic sclerosis, as well as the causal effects of systemic sclerosis on cytokine levels, as depicted in the causal directed acyclic graph. SNP, single nucleotide polymorphisms.

Statistical analysis

Bidirectional, two-sample Mendelian randomization (MR) was performed to assess causality between genetically predicted circulating cytokine levels and systemic sclerosis risk. The primary analysis utilized the inverse-variance weighted (IVW) approach, with sensitivity analyses through weighted median and MR-Egger regression. Odds ratios (ORs) and 95% confidence intervals (CIs) were constructed per genetically predicted standard deviation increase in cytokine levels. Between-SNP heterogeneity was evaluated via the MR-Egger intercept test. Pleiotropy was assessed using the MR-Egger slope coefficient test.

Given the potential for compromised MR estimates due to heterogeneity, rigorous sensitivity analyses were conducted to determine possible pleiotropy and heterogeneity. First, the IVW and MR-Egger methods were applied, with Cochran's Q statistics (p < 0.05) generated to quantify heterogeneity. Second, the MR-Egger intercept test evaluated horizontal pleiotropy based on the regression intercept term. We also applied MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) testing to detect and correct horizontal pleiotropy through outlier SNP removal, comparing causality estimates before and after correction. MR-PRESSO exhibits reduced bias and improved precision compared to IVW and MR-Egger when < 10% of variants display pleiotropy. A leave-one-out analysis with 25 iterations was performed to evaluate whether individual SNPs disproportionately influenced the MR estimates. A Bonferroni-corrected p-value threshold of 0.002 accounted for multiple testing across the cytokines. All analyses were performed using the Two Sample MR R package.

Influence of 47 circulating cytokines on SSc

We identified 24 out of 47 circulating cytokines with significant SNPs using a genome-wide significance threshold of p<5×10^-8. To ensure adequate SNPs for subsequent Mendelian randomization analysis, we set a more liberal threshold of p<5×10^-5, yielding a total of 554 SNPs across the cytokines. The median F-statistic value for the SNPs was 22.1 (range, 11.16–789.1), indicating sufficient strength as instrumental variables. Further screening in PhenoScanner for pleiotropic associations excluded 7 SNPs related to potential confounders, such as body mass index, education attainment, alcohol intake frequency, and paternal age (rs10761731, rs12075, rs3748034, rs635634, rs7088799, rs71641308, rs9436119). (Supplementary Table S1).

Our Mendelian randomization analysis revealed a significant causal relationship between genetically predicted circulating TRAIL levels and increased systemic sclerosis risk (inverse-variance weighted method: OR=0.9998, 95% CI=0.9997-0.9999, p=0.042; Figure 2). The Scatter plots and funnel plots of Mendelian randomization (MR) analyses for TRAIL in SSc is exhibited in Figure 3. The instrumental variables for TRAIL demonstrated no significant heterogeneity (MR-Egger p=0.768; IVW p=0.70) or horizontal pleiotropy (MR-Egger p=0.891), indicating that they were valid instruments and not influenced by pleiotropic effects. In contrast, while the weighted median method suggested significant causal estimates for CTACK on SSc (OR 0.9997, 95% CI 0.9994-0.9999, p=0.028), the IVW analysis showed no such effect (OR 0.9998, 95% CI 0.9996-1.0000, p=0.130) (Supplementary Table S2).

Intriguingly, although PDGF Rb yielded a significant estimate via IVW (OR 0.9999, 95% CI 0.9999-2.7181, p=0.025), potential pleiotropy was detected based on the MR-Egger test (p=0.025) (Supplementary table S2). No significant causal associations were discerned between other assessed cytokines and SSc, including TGF-β and IL-6. (Supplementary Table S2). Scatter plot, and forest plot of SNPs associated with other cytokines and their risk of systemic sclerosis were shown in Supplementary Figure S1.

IV is shown by vertical and horizontal lines. The slope of the lines represents the estimated causal effect of the MR methods. B. The funnel plots show the inverse variance weighted MR estimate of TRAIL single-nucleotide polymorphism with SSc versus 1/standard error (1/SEIV). C. Leave-one-out sensitivity analysis for TRAIL in SSc. The red line indicates reliable estimations from the IVW and MR Egger methods.

Influence of SSc on 47 circulating cytokines

Given the putative role of circulating cytokines in SSc pathogenesis, we reciprocally examined the causal effect of SSc status on cytokine levels using two-sample Mendelian randomization. Twenty-five genome-wide significant (p<5×10^-8) SNPs were identified as instruments for SSc risk, with median F-statistic 35.36 (range 29.78-47.38), conferring adequate strength for this analysis (supplementary table S3). There was no significant causal relationship between SSc risk and circulating TRAIL (p>0.05 using IVW method, Figure 4). This result confirmed the causal relationship between TRAIL and SSc. However, our study revealed a significant causal relationship between increased disease risk and IL-2rα (inverse-variance weighted method: beta: 33.05, 95%CI:0.539-65.563, p = 0.046，Figure 4).

The level of circulating cytokines in patients plays an important role in SSc. In this two-sample Mendelian randomization study, we reported for the first time the causal relationship between circulating cytokines and SSc. When 47 circulating cytokines were exposed and SSc was the outcome, we found that TRAIL may be an upstream factor rather than TGF-β, IL-6, or PDGF. This indicates that other factors are more likely to be downstream factors in disease progression.

The main conclusion of this Mendelian randomization study is that circulating TRAIL may be a factor in the pathogenesis of SSc and may play a protective role at the onset of SSc. TRAIL, a member of the TNF superfamily, was first cloned and named by Wiley et al. from the human cardiac cDNA library in 1995[19]. TRAIL exists in two forms: membrane-bound and soluble. Membrane-bound TRAIL can be hydrolysed to soluble TRAIL by metalloproteinase[20, 21]. Both are consistent in function, selectively inducing apoptosis in transformed cells, tumor cells, and virus-infected cells with little harm to normal cells[22]. In physiological state, active TRAIL is usually expressed only in immune cells such as T lymphocytes, natural killer cells, and neutrophils. In disease state, the protein level of TRAIL in lymphocytes, monocytes, neutrophils and diseased cells increases, which increases the level of circulating TRAIL[23]. However, when a large amount of apoptosis is induced, it will cause excessive consumption of sTRAIL, resulting in a decrease in its level. In different states, TRAIL can play the dual roles of pro-inflammatory or anti-inflammatory, pro-apoptosis or pro-cell proliferation[23–25].It was reported that the serum level of TRAIL in patients with SSc is significantly higher than that of the control group, and the serum TRAIL level of patients with lung involvement is positively correlated with the degree of lung lesions[16]. Many studies using mouse models of induced autoimmune diseases have shown that TRAIL has immunosuppressive functions[22, 26–28]. TRAIL deficiency exacerbates bleomycin-induced pulmonary fibrosis in mice[27].

In this study, we also explored the relationship between other circulating cytokines and SSc, such as TGF-β, IL-6, and PDGF. TGF-β is currently believed to be the central link in the pathogenesis of SSc[29]. TGF-β has a powerful profibrotic effect, but the presence of the latent TGFβ library and the process of extracting latent TGF-β from tissues activates TGF-β, limiting its study in the pathogenesis of SSc. Some studies indicate that elevated TGF-β in serum in patients with SSc is positively correlated with disease activity[30, 31], but others show that activated TGF-β is inversely associated with SSc activity rather than TGF-β itself [29]. In addition, no evidence of efficacy was shown in treating SSc by neutralizing TGF-β1 antibody[32]. This indicates that the activation and regulation of TGF-β in the pathogenesis of SSc is complex. Another key factor that activates fibroblasts is IL-6. Studies have shown that serum IL-6 levels correlate with the severity of autoimmune diseases such as rheumatoid arthritis and SSc[33–35]. IL-6 contributes to pathological collagen production in dermal cells[36]. Furthermore, single nucleotide polymorphisms of the IL-6 gene may influence the development and progression of SSc[37]. However, a randomized, double-blind, controlled phase III clinical trial using tocilizumab, an IL-6 receptor inhibitor, for the treatment of SSc showed that IL-6 receptor inhibitors delayed the progression of SSc lung function, but did not significantly improve skin fibrosis[38]. Moreover, observational studies have found that PDGFs and PDGFRs are upregulated in fibrotic dermal lesions in patients with SSc[14, 39]. Elevated levels of PDGF-AA and PDGF-BB were found in lavage fluid in patients with scleroderma[15]. It was reported that PDGFs and their receptors may have causal roles in the pathogenesis of SSc. Patients with scleroderma have antibodies against PDGFR in their serum, which stimulate PDGFR by inducing ROS in Ha-Ras and ERK1/2 signaling pathway[40]. In addition, many other studies have shown that some circulating cytokines are associated with the pathogenesis of SSc, such as IL-2, IL-4, IL-10, and IL-17[2, 5, 19]. However, the causal relationship between cytokines and SSc has not been fully elucidated. In observational studies, changes in cytokine levels may be disturbed by factors such as SSc disease status, drug side effects, potential infection, lifestyle, and eating habits.

Although our study is the first to discover a causal relationship between circulating TRAIL and SSc, the regulatory role of other cytokines in the pathogenesis of SSc cannot be denied. The mechanism by which TRAIL participates in the pathogenesis of SSc by regulating downstream cytokines remains to be demonstrated by further in vivo and in vitro experiments. In addition, the circulating cytokines investigated in this study are limited. Subsequent studies could include more cytokines in the future. Since this study shows that TGF-β, IL-6, etc. have no causal relationship with the pathogenesis of SSc, other cytokines or receptor molecules related to them may be the focus of further study. Our results provide new clues for the etiology of SSc, drug treatment, early diagnosis, and establishment of animal models.

It’s the first study to reveal the casual relationship between circulating cytokines and SSc using MR analysis. Circulating TRAIL level is a potential causal factor in SSc risk. Other circulating cytokines such as TGF-β, IL-6, etc. have no causal relationship with the pathogenesis of SSc. Further large-scale studies are warranted to verify our results and the underlying mechanisms should be further explored.

CI	Confdence interval
ECM	Extracellular matrix
GWAS	Genome-wide association study
IVs	Instrumental variables
IVW	Inverse variance weighted
LD	Linkage disequilibrium
MR	Mendelian randomization
MR-PRESSO	MR pleiotropy residual sum and outlier
OR	Odds ratio
PBMC	Peripheral blood mononuclear cell
SNP	Single-nucleotide polymorphisms
SSc	Systemic sclerosis
SSc-ILD	Systemic sclerosis-interstitial lung disease
TRAIL	Tumor necrosis factor-related apoptosis-inducing ligand

Author contributions

HS and SS designed the study and edited the manuscript.

HS, and JZ, performed the statistical analysis and drafted the manuscript.

HC, ZY, SHK, DW, LS reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.

HS, JZ, and SS have contributed equally to this work and share first authorship.

Funding

This work was supported by Jiangsu Provincial Natural Science Foundation (BK20231119), and Jiangsu Provincial Medical Youth Talent (QRX17040).

Acknowledgments

The authors sincerely thank related investigators for sharing the GWAS summary statistics included in this study.

Ethics approval and consent to participate

Written informed consent to participation was not required for this study in accordance with the national legislation and the institutional requirements. Ethical review and informed consent were obtained from the original GWASs.

Consent for publication

Not applicable.

Availability of data and material

The data used in this study was obtained from public databases and previous studies. Data sources and handing of these data were described in the Methods. Further information is available from the corresponding author upon request.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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No competing interests reported.

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Causal relationship between circulating cytokines and the risk of systemic sclerosis: A bidirectional Mendelian-randomization study

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Abstract

Objectives

Methods

Results

Conclusion

Figures

Introduction

Methods

Study population and data sources

SNP selection and quality control

Statistical analysis

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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