Genetic Link Between Primary Biliary Cholangitis and Rheumatoid Arthritis: A Two-Sample Mendelian Randomization Study

doi:10.21203/rs.3.rs-1444566/v1

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Genetic Link Between Primary Biliary Cholangitis and Rheumatoid Arthritis: A Two-Sample Mendelian Randomization Study

https://doi.org/10.21203/rs.3.rs-1444566/v1

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Background

Although observational studies have shown that primary biliary cholangitis (PBC) and rheumatoid arthritis (RA) frequently coincide, it is unclear whether a causative relationship exists between the two diseases. This study aimed to assess the causal associations between PBC and RA.

Methods

We performed a two-sample Mendelian randomization (MR) analysis. Following a strict assessment, genome-wide association study datasets were used to select potential candidate single-nucleotide polymorphisms. Sensitivity analyses were performed to determine whether the MR results were affected by heterogeneity and pleiotropy.

Results

We found that genetically increased PBC risk had a positive causal effect on RA (inverse-variance weighted odds ratio [IVW OR] 1.149, 95% confidence interval [CI] 1.063–1.241, P < 0.001). Horizontal pleiotropy was unlikely to influence causation according to the MR-Egger regression (intercept=-0.028, P = 0.263). Sensitivity analyses using the leave-one-out method further confirmed that the MR analysis results were reliable and robust. In contrast, the reverse MR analysis found no evidence of a causal effect of RA on PBC (IVW OR 1.132, 95% CI 0.881–1.454, P = 0.333).

Conclusions

Our findings provide evidence that PBC has a unidirectional effect on RA in the European population. Given the increased risk of PBC in RA and both being autoimmune conditions, awareness and thorough screening are imperative in clinical practice.

primary biliary cholangitis

rheumatoid arthritis

Mendelian randomization

causal relationship

Primary biliary cholangitis (PBC), formerly known as primary biliary cirrhosis, is a common autoimmune liver disease. Anti-mitochondrial antibodies (AMAs) and the destruction of small- to medium-sized intrahepatic bile ducts are hallmarks of PBC. As a chronic liver disease, PBC can lead to liver failure and intractable complications [1]. PBC-specific loci, similarly to those found in other autoimmune diseases, have been linked to the development of other autoimmune conditions [2]. Although the genetic or immunological mechanisms underlying this phenomenon have not been elucidated, the 'Kaleidoscope of autoimmunity' has been proposed to describe it [3]. Patients with PBC have also been reported to have extrahepatic autoimmune diseases (EHAIDs) [3, 4]. Moreover, the current clinical guidelines on PBC recommend early identification and management of EHAIDs [5, 6], which can enhance patient outcomes and quality of life.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that affects the joints and causes cartilage degradation, bone destruction, and systemic complications. It is one of the most common autoimmune diseases, with a global frequency of 0.5–1.0% and shows no signs of decline [7]. The pathophysiological pathways and genetic vulnerabilities of PBC and RA are analogous because both are autoimmune diseases. Concomitant arthritis is observed in patients with PBC, and PBC in RA has also been reported [3, 8–10], raising the possibility of a common etiology [9, 11, 12]. However, there is no compelling evidence that these two diseases are correlated. Exploring the causality between these diseases is crucial, as it may enhance our understanding of the pathogenesis of PBC and RA and help us develop more appropriate therapies.

As a novel statistical method, Mendelian randomization (MR) provides an opportunity to determine whether a causal effect can be inferred from an observational relationship between exposure and outcome [13]. The rationale for MR lies in the equal, random, and independent genetic variation during meiosis, and it uses genetic variations as instrumental variants (IVs) [13, 14]. When performing MR analysis, three basic assumptions should be made: the genetic variant (1) is related to the exposures, (2) is not related to the confounders, and (3) exclusively affects the outcomes through the exposure [15]. Confounding and reverse causality bias can be minimized because of the properties of MR analysis [14]. Therefore, this study aimed to evaluate the causal association between PBC and RA using a two-sample MR analysis.

Source of data and study design

The PBC genome-wide association study (GWAS) summary data were derived from a genome-wide meta-analysis, the latest and largest PBC GWAS to date, which included 10,516 cases and 20,772 controls [16]. To reduce the possibility of population stratification bias, only summary-level data from the European population (8021 cases and 16,489 controls) were collected for this study.

The UK Biobank project is a prospective cohort study that has gathered extensive genetic and phenotypic data on approximately 500,000 individuals across the United Kingdom, allowing for the identification of novel genetic correlations and genetic origins of complex traits [17, 18]. Research on non-cancerous illnesses served as the foundation for RA GWAS (5201 cases and 457,732 controls).

The IVs in this two-sample MR analysis were single-nucleotide polymorphisms (SNPs) that were significantly associated with PBC and RA. To increase the statistical power of genetic variants, we eliminated SNPs with a minor allele frequency of less than 1%. To reduce linkage disequilibrium, we removed variants with a physical distance of less than 10,000 kb and r² < 0.001 [19]. We used IVs for preprocessed exposure data to identify genetic variants that met the genome-wide association threshold (P < 5×10^− 8).

Statistical analysis

To maintain the effect alleles to continually refer to the same allele, we harmonized the exposure and outcome datasets. By generating the SNPs-PBC and SNPs-RA estimations, the causal effects of PBC on RA were assessed using the inverse-variance weighted (IVW), weighted median, and MR-Egger regression methods. Since all of the SNPs chosen must be valid IVs, the IVW method delivers the most accurate results [20–22]. The MR-Egger sensitivity test was applied to analyze the direct pleiotropy effect to ensure that the IVs satisfied the fundamental assumptions of MR analysis [23]. To investigate the constancy of effect sizes and to determine the specific SNP that had an improper influence on the correlation, a leave-one-out sensitivity analysis was conducted, which involved deleting each SNP in turn and utilizing the IVW method on the effect of the remaining SNPs [24]. All statistical analyses were performed using R (version 4.0.3, using the TwoSampleMR package; R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at P < 0.05.

Stage 1: Impact of PBC on RA

To investigate the causal effect of PBC on RA, 28 SNPs associated with PBC were retrieved (Table 1). Genetic variations in PBC were causally related to the risk of RA (odds ratio [OR] 1.149, 95% confidence interval [CI] 1.063–1.241, P < 0.001), according to the IVW estimations. The MR-Egger (OR 1.301, 95% CI 1.037–1.631, P = 0.031) and weighted median methods (OR 1.171, 95% CI 1.062–1.292, P = 0.002) also yielded similar results (Fig. 1A, Table 2). Sensitivity analyses using the leave-one-out method further confirmed the credibility of the MR analysis results (Fig. 1B). As shown in Fig. 1C and Table 2, the MR-Egger regression analysis revealed no significant directional horizontal pleiotropy between PBC and RA (intercept = -0.028, P = 0.263). There was no obvious heterogeneity in the estimations of the SNPs included (Cochran’s Q = 37.168, P = 0.072) (Fig. 1D).

Table 1

Genome-wide significant variants of PBC and their association with RA
SNP	EA	NE	EAF	PBC			RA
				β	SE	P-value	β	SE	P-value
rs10802191	A	T	0.712	-0.208	0.037	2.81×10^− 8	0.008	0.053	0.873
rs11117432	A	G	0.913	-0.273	0.027	2.82×10^− 24	-0.046	0.040	0.252
rs1119132	A	G	0.851	-0.203	0.033	6.58×10^− 10	-0.052	0.047	0.268
rs12123169	A	T	0.803	0.218	0.025	9.75×10^− 18	-0.046	0.039	0.243
rs12531711	G	A	0.001	0.420	0.031	8.57×10^− 41	0.185	0.051	0.000
rs1800693	C	T	0.700	0.180	0.022	1.19×10^− 16	0.032	0.033	0.327
rs1808094	T	C	0.468	0.128	0.021	2.79×10^− 9	-0.012	0.032	0.709
rs2293370	A	G	0.793	-0.299	0.029	5.54×10^− 25	-0.012	0.041	0.774
rs2304256	A	C	0.734	-0.205	0.024	4.43×10^− 17	-0.114	0.035	0.001
rs2546890	G	A	0.440	-0.144	0.022	2.51×10^− 11	-0.015	0.032	0.649
rs34655300	T	C	0.673	0.137	0.022	5.23×10^− 10	0.056	0.033	0.090
rs3745516	A	G	0.471	0.274	0.024	2.65×10^− 30	-0.017	0.038	0.653
rs3771317	C	T	0.727	0.290	0.030	2.40×10^− 22	0.073	0.047	0.120
rs3784099	A	G	0.603	-0.203	0.024	8.31×10^− 17	0.043	0.036	0.226
rs55734382	T	C	0.887	-0.140	0.023	2.06×10^− 9	0.020	0.034	0.552
rs589446	T	G	0.820	-0.353	0.022	1.96×10^− 55	-0.038	0.032	0.243
rs60600003	G	T	0.955	0.253	0.035	4.70×10^− 13	0.099	0.054	0.066
rs6550965	A	C	0.587	0.163	0.022	3.65×10^− 14	-0.033	0.033	0.320
rs7097397	A	G	0.641	-0.144	0.023	2.42×10^− 10	-0.059	0.033	0.077
rs72699866	A	G	0.901	-0.195	0.029	2.89×10^− 11	-0.041	0.041	0.319
rs7674640	C	T	0.556	-0.216	0.022	1.56×10^− 22	-0.029	0.032	0.364
rs7805218	A	G	0.878	0.129	0.023	4.12×10^− 8	0.022	0.035	0.521
rs79577483	G	A	0.874	0.212	0.031	1.23×10^− 11	0.066	0.049	0.175
rs859767	G	A	0.620	-0.139	0.023	1.54×10^− 9	0.057	0.034	0.095
rs9533122	A	G	0.384	-0.155	0.022	5.83×10^− 13	-0.026	0.032	0.410
rs9591325	C	T	0.967	-0.452	0.050	2.14×10^− 19	-0.074	0.064	0.252
rs9652601	A	G	0.672	-0.240	0.024	6.69×10^− 24	-0.052	0.034	0.127
rs9876137	G	A	0.619	0.144	0.022	8.06×10^− 11	0.028	0.034	0.397
EA, effect allele; NE, non effect allele; EAF, effect allele frequency; β, effect sizes; SE, standard error; P-value, P value for the genetic association

Table 2

Two-sample Mendelian randomization of PBC and the risk of RA
Exposure vs outcome	Methods	SNPs	OR	95% CI	P-value	MR-Egger intercept (P-value)
PBC vs RA	MR-Egger	28	1.301	0.037–0.489	0.031
	Inverse-variance weighted	28	1.149	0.061–0.2160	<0.001	-0.028(0.263)
	Weighted median	28	1.171	0.060–0.256	0.002

Stage 2: Impact of RA on PBC

To explore the causal link between RA and PBC, we employed MR analysis, with RA as the exposure and PBC as the outcome. We used three significant and independent IV SNPs discovered in the UK Biobank for RA (Table 3). As shown in Fig. 2A and Table 4, using the IVW, MR-Egger, and weighted median methods, there was no indication of a causative relationship between the increased risk of RA and changes in the risk of PBC. A sensitivity analysis using the leave-one-out method also supported this finding (Fig. 2B). According to the MR-Egger regression, there was no evidence of horizontal pleiotropy (MR-Egger intercept=-0.025, P = 0.902). (Fig. 2C, Table 4). Furthermore, it was estimated that the included SNPs had strong heterogeneity (Cochran’s Q = 8.262, P = 0.004) (Fig. 2D).

Table 3

Genome-wide significant variants of RA and their association with PBC
SNP				RA						PBC
				β		SE		P-value		β		SE		P-value
rs10065637	T	C	0.205		0.205		0.040	3.46×10^− 8	0.909		0.032		0.502
rs189070938	A	T	0.017		0.017		0.137	1.65×10^− 9	0.006		0.101		0.581
rs2476601	A	G	0.102		0.102		0.055	2.08×10^− 14	0.973		0.035		0.001

Table 4

Two-sample Mendelian randomization of the risk of RA and PBC
Exposure vs outcome	Methods	SNPs	OR	95% CI	P-value	MR-Egger intercept (P-value)
RA vs PBC	MR-Egger	3	1.205	-0.683-1.055	0.747
	Inverse-variance weighted	3	1.132	-0.127-0.375	0.333	-0.025 (0.903)
	Weighted median	3	1.075	-0.087-0.232	0.375

In this study, the effects of IVs on the exposure (i.e., PBC) and outcome (i.e., RA) from two independent samples were used in a two-sample MR analysis. Based on summary-level data, we employed a series of statistical methods to obtain quantitative estimates of the impact of PBC on RA. Reverse MR analysis was used to examine the causality between RA and PBC. To our knowledge, this study is the first to use MR analysis and large-scale GWAS datasets to demonstrate a causal relationship between PBC and RA. Our study found that PBC has a positive causal effect on RA risk; however, RA has no causal effect on PBC risk in Europeans. We discovered that PBC could increase the likelihood of developing RA, implying that PBC and RA may have a similar pathophysiology.

Understanding the etiology of disease processes requires inferring causal links between phenotypes, which is a major challenge. Owing to two significant achievements in the past decade, the possibility of phenome-wide causal inference has increased dramatically. The first is the persistent success of GWAS in discovering strong genetic correlations [25]. The second is the development of statistical methods for causal inference that rely on MR principles and GWAS summary data [26].

While numerous studies have found that approximately 1.8–5.6% of patients with PBC have RA [8] and that patients with RA are at a higher risk of developing PBC than the general population [9], these findings do not prove whether PBC is etiologically related to RA. Based on the GWAS summary data and two-sample MR analysis, our results indicate that the presence of PBC with RA may be due to a causal link rather than an observation.

Our study has several advantages over prior observational studies. First, we analyzed large-scale GWAS datasets, and over 500,000 people participated in the RA GWAS alone. The participants in both studies were of European ancestry, thus minimizing any population stratification. Second, because the SNP site alleles are randomly assigned in advance of the commencement of any potential confounding circumstances, MR substantially reduces the influence of confounding factors and reverse causation [13, 20]. Finally, we validated the robustness of the results using various MR methodologies. An unbiased causal estimate could still be obtained when some instruments were missing or when there was horizontal pleiotropy.

However, our study had certain limitations. First, using summary-level statistics prevented us from performing analyses stratified by factors that were altered in the original GWAS [27]. Second, in the MR model, we only assumed a linear relationship between PBC and RA. We were also unable to investigate the nonlinearity of the relationship between PBC and RA using summary statistics. Third, we did not stratify the causative effects of PBC and RA by sex or age, despite previous research showing that they can vary by these factors [2, 28]. Individual-level data from the original GWAS were difficult to acquire. The European ancestry of the GWAS population included in the exposure and outcome analyses may have mitigated population stratification. Fourth, the minimal variance in exposures, particularly for RA using SNP instruments, may reduce the strength of the causal effects. A modest number of SNP instruments may have an impact on variance. Nevertheless, owing to the very large sample size and highly relevant instruments, we were still able to establish or exclude causal associations.

In addition to earlier evidence from genetic, immunological, and environmental levels, a causal effect of PBC on RA has been shown; therefore, it is important for the clinician to consider the association of PBC and RA in daily practice. Awareness of this combination should encourage clinicians to screen for PBC with AMAs testing in patients with abnormal liver tests, as well as to recognize that abnormal liver tests in RA patients are not always associated with drugs. Furthermore, if the pharmacological target is discovered in the pathogenesis of the two diseases, optimized management of one might possibly benefit the other. Novel effective medications (e.g., infliximab, rituximab, abatacept, and tocilizumab) hold considerable promise for the treatment of both PBC and RA [29–32]. Several have received preliminary approval, thereby increasing the likelihood that they will be identified and confirmed as beneficial.

We investigated the causality between PBC and RA for the first time, and our findings indicated that PBC increased the risk of RA. This could have several implications for clinicians in regular practice. Based on our findings, it is plausible to recommend routine PBC screening in patients with RA. Further research using updated data from larger-scale genetic studies is necessary to corroborate the findings of our two-sample MR analysis.

PCB, primary biliary cholangitis; RA, rheumatoid arthritis; AMA, anti-mitochondrial antibodies; EHAID, extrahepatic autoimmune disease; MR, Mendelian randomization; GWAS, genome-wide association study; SNP, single-nucleotide polymorphism; IV, instrumental variant; IVW, inverse-variance weighted.

Acknowledgments

We thank all the participants and researchers of the studies included in the current analyses.

Author contributions

Junyu Fan analyzed the data and wrote the manuscript. Ting Jiang revised the manuscript. Dongyi He supervised the study and revised the manuscript. All authors have approved the submitted version.

Funding

This work was supported by the National Nature Science Foundation of China (8210463), the Clinical Research Project of Shanghai Health Commission (20214Y0165), and the Program of Pujiang Rheumatic Youth Cultivation (SPROG202003).

Availability of data and materials

The data used to support the findings of this study are available from the corresponding author upon reasonable request.

Ethics and consent

This work did not require ethical approval or consent because this MR analysis was performed using publicly available data.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

¹ Department of Rheumatology, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200052, China

² Department of Rheumatology, Shanghai Guanghua Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai 200052, China

³ Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai 200052, China

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No competing interests reported.

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Genetic Link Between Primary Biliary Cholangitis and Rheumatoid Arthritis: A Two-Sample Mendelian Randomization Study

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Abstract

Background

Methods

Results

Conclusions

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Background

Methods

Source of data and study design

Statistical analysis

Results

Discussion

Conclusions

Abbreviations

Declarations

References

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