
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39312384
MD-D-24-06390
00085
10.1097/MD.0000000000039723
3
6900
Research Article
Observational Study
Polymyalgia rheumatica and giant cell arteritis: A bidirectional Mendelian randomization study
https://orcid.org/0009-0000-1866-0347
Teng Lin MMed 924434166@qq.com
a
Li Lei MD 274936626@qq.com
a
Cui Dinglu MMed cuidinglu@126.com
a
An Rongxian MMed 15567666633@163.com
a
https://orcid.org/0000-0002-3985-2513
Jin Jingchun MD a*
a Yanbian University Hospital, Yanji, China.
* Correspondence: Jingchun Jin, Yanbian University Hospital, 1327 Jvzi Street, Yanji, Jilin 133000, China (e-mail: jingchun680928@163.com).
20 9 2024
20 9 2024
103 38 e3972306 6 2024
06 8 2024
26 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Polymyalgia rheumatica (PMR) and giant cell arteritis (GCA) as 2 types of autoimmune diseases are frequently concomitant, and Mendelian randomization (MR) was applied in this study to assess the causal relationship between them. In this study, single-nucleotide polymorphism (SNP) was used as the instrumental variable for Mendelian analysis, and the SNP data of GCA and PMR were obtained from the FinnGen Biobank databases. SNPs are significantly correlated with GCA and PMR and were screened based on preset thresholds. Inverse variance weighted analysis was used as the main analysis, supplemented with MR-Egger and weighted median. The evidence of the impact of GCA on PMR risk was found in inverse variance weighted results (odds ratio, 1.22 [95% confidence interval, 1.11–1.34]; P < .01), and the evidence of the impact of PMR on GCA risk has also been found (odds ratio, 1.58 [95% confidence interval, 1.28–1.96]; P < .01). Finally, the stability and reliability of the results were tested using the retention method, heterogeneity test, and horizontal gene pleiotropy test. MR analysis indicates that GCA increases the risk of PMR and PMR is an important risk factor for GCA, with a causal relationship. The potential value of reasonable management of PMR in patients with GCA has received high attention. In addition, novel GCA therapeutics may be indicated for PMR, and it is a potential for further investigation.

autoimmune disease
causal inference
giant cell arteritis
Mendelian randomization
polymyalgia rheumatica
National Nature Science Foundation of ChinaNo.82360441 Jingchun JinScience and Technology Department of Jilin ProvinceNo.20200201492JC Jingchun JinOPEN-ACCESSTRUE
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pmc1. Introduction

Polymyalgia rheumatic (PMR) is the second most common systemic rheumatic disease in adults, and its specific cause is still unknown. During the acute attack, there is pain in both shoulders accompanied by morning stiffness. The neck, pelvic girdle, and proximal thigh are less commonly involved.[1,2] The lifetime risk of PMR is 2.4% for women and 1.7% for men. The incidence rate starts to increase after 50 years old, and the incidence rate of 70 to 80 years old reaches the highest.[3] Due to no specific indicators for clinical diagnosis and laboratory testing, thus, making the diagnosis of this disease is very difficult.[4]

Giant cell arteritis (GCA), also known as temporal arteritis, is a type of vasculitis of large and medium-sized blood vessels. GCA can involve intracranial vessels in addition to the aorta and great vessels. GCA is the most common form of idiopathic systemic vasculitis, often presenting with systemic symptoms, and the involvement of the carotid extramuscular and extracranial branches is typical for intracranial GCA. As a systemic rheumatic disease, GCA is more common in Western countries, and the incidence increases after 50 years old, with the highest in the 71 to 80-year-old group, and women are 2.5% more likely to develop GCA than men.[5,6] The highest prevalence of GCA has been reported in Scandinavian countries and Minnesota, USA.[7]

PMR and GCA are the same autoimmune diseases and often occur combined in patients. In currently published cross-sectional studies and case-control studies, about 40% to 60% of patients diagnosed with GCA have signs and symptoms of PMR.[8,9] In studies of patients with PMR, 16% to 21% of patients with PMR showed histologic features of GCA on temporal artery biopsy.[8,10] In a recent European study, 22% of patients (288) with PMR without symptoms of GCA showed GCA on ultrasound.[11] In some cases, PMR may occur before GCA, while, in other cases, GCA may occur first. Sometimes, both can occur almost simultaneously. Meanwhile, the peak incidence and circulatory patterns observed in several studies suggested that interleukin (IL)-6 and infection factors may play a role in the pathogenesis of both diseases, namely, PMR and GCA.[12] Therefore, we proposed the hypothesis of whether PMR and GCA have similar pathophysiological mechanisms and genetic susceptibility and validated it by statistical analysis at the genetic level using the Mendelian randomization (MR) approach.

MR is a technique that uses genetic variation as an instrumental variable (IV) to assess whether observed associations between exposure factors and outcomes are consistent with causal effects.[13] MR is based on 3 assumptions: genetic variation is associated with risk factors, genetic variation is not associated with confounders, and genetic variation affects outcomes only through risk factors.[14,15] Since genetic variation is not influenced by other factors such as the external environment and social behavior, it is a stable exposure factor over time. Therefore, analysis can minimize bias by avoiding the influence of confounders and reverse causality on correlation effects in observational studies through the MR method. In recent years, MR methods have been widely used in studies assessing the causal relationship between exposure and outcome.[16] Therefore, in this article, MR was used to investigate the causal relationship between PMR and GCA.

2. Materials and methods

2.1. Study design

In this study, a 2-sample MR was used to investigate the causal relationship between PMR and GCA. PMR was first used as an exposure factor, single-nucleotide polymorphism (SNP) was significantly associated with PMR as an IV, and GCA was used as an outcome variable (Fig. 1). The 2-sample MR analysis was used to analyze the causality of the 2 samples, followed by a heterogeneity test of the results using the Cochran Q test. Finally, a sensitivity analysis was performed to verify the reliability of the results. All of the analyses mentioned above were performed using the TwoSampleMR package (version 0.5.5) in R (version 4.0.2).

Figure 1. Schematic of Mendelian randomization study design. CR = causal relationship.

2.2. Sources of data

SNPs were used as IVs for Mendelian analysis, and SNP data for both GCA and PMR were obtained from a Finnish database (https://r10.finngen.fi/; n = 412,181) containing 399,355 control patients and 1066 patients with GCA, and 399,355 control patients and 3871 patients with PMR. The above data were obtained from publicly available genome-wide association study data, and no additional ethical approval was required. The genetic background of the study population was derived from European ancestry to avoid bias due to race-related confounders (Fig. 2).

Figure 2. Flowchart of the study process. GCA = giant cell arteritis, MR = Mendelian randomization, PMR = polymyalgia rheumatic, SNP = single-nucleotide polymorphism.

2.3. Choice of IVs

MR analysis was performed on the principle that SNP must be significantly correlated with exposure, and the loci of SNP that were significantly associated with PMR or GCA were selected according to (P < 5 × 10−8). However, using a strict threshold of 5 × 10−8 excluded most SNP. Therefore, based on a previous study,[17] we set a relatively loose but still very important threshold of 5 × 10−6 to include the conditions of r2 < 0.001 and kb > 10,000 to eliminate chain imbalance. In addition, we removed weakly validated SNPs with F-values < 20 to ensure the strength of association between IVs and exposure factors. To avoid the possibility of some bias in the results of the study, we calculated the effect of sample overlap (https://sb452.shinyapps.io/overlap/).[18] Our calculations found that the bias of the hypothesized sample overlap at 100% is only 0.011 for its impact on the results, with a category 1 error percentage of 0.05, which is not enough to affect our conclusions. These screening conditions ensured the credibility of our findings.

2.4. MR analysis

This study used a 2-sample MR analysis, which used inverse variance weighted (IVW) analysis as the primary outcome. It has strong causality detection efficacy since it assumes that the tool can affect the outcome only through exposure and no other alternative pathways (the intercept is restricted to zero).[19] Before the MR analysis, a pleiotropy residual sum and outlier (PRESSO) method was adopted to test the horizontal polytropy of the data and correct for horizontal polytropy before effect assessment, aiming to ensure the reliability of the MR analysis. Although it is known that SNP is associated with confounding and eliminated as much as possible in this study, there are still many unknown confounding elements that could lead to biased results. Therefore, other analyses were also introduced as a complement,[20] including the MR-Egger method, the weighted median method, and the weighted model. Although the simple modeling method is not as effective as the IVW method for detection, it exhibits resistance to multiplicity and stability characteristics.[21] Weighted models show higher sensitivity when choosing a model to estimate the bandwidth, and the MR-Egger method and the weighted median method provide more stable estimates in a wider range of scenarios, but the effect sizes may be lower.[22] P < .05 was considered statistically significant.

2.5. Sensitivity analysis

Heterogeneity in the effect of PMR and GCA single-gene polymorphisms on the outcome was assessed using the Cochran Q test.[23] When the P value of the Cochran Q test was >.05, the SNPs did not show heterogeneity among themselves. Using MR-PRESSO or MR-Egger intercept to test for potential horizontal pleiotropy,[19] P > .05 indicates that there is no horizontal pleiotropy in the study. In addition to this, we used the leave-one-out method for sensitivity analysis and looked closely at the extent to which each SNP had an impact on causality after removing the final included SNPs one by one. Data were analyzed, and statistical plots were drawn throughout this study using the TSMR tool (0.5.7) in R, version 4.3.1. Statistical results were expressed as odds ratio (OR) and 95% confidence interval (CI), and P < .05 was considered statistically significant. α = 0.05 was the test level.

3. Results

3.1. IVs and outcomes

We screened 12 SNPs each significantly associated with GCA and PMR according to the set thresholds and subsequently removed the chain imbalance. Since GCA is closely associated with PMR disease occurrence, we performed MR-PRESSO analysis to remove outliers and, finally, extracted 7 SNPs associated with GCA and 8 SNPs associated with PMR, which were used to analyze the causality of GCA on PMR. All these SNPs showed strong association strength (F > 20). Detailed information on the SNPs used for the analysis is shown in Table 1.

Table 1 Detailed description of GCA and PMR source SNPs.

Exposure	Outcome	SNP	EA	OA	β	SE	P	F	
GCA	PMR	rs117524157	G	C	0.554807	0.116544	1.93 × 10−6	22.66	
		rs137917219	A	G	0.659687	0.139133	2.12 × 10−6	22.48	
		rs142891233	A	C	0.454973	0.0988478	4.17 × 10−6	21.19	
		rs17072101	G	A	0.289676	0.0567305	3.29E×10−7	26.07	
		rs62103719	T	C	0.273138	0.0597917	4.92 × 10−6	20.87	
		rs6553430	T	C	−0.233145	0.0498797	2.95 × 10−6	21.85	
		rs72734886	G	A	−0.257246	0.0549516	2.85 × 10−6	21.91	
PMR	GCA	rs1057373	A	C	−0.274211	0.0349502	4.30 × 10−15	61.56	
		rs141113629	T	C	−0.366053	0.0727891	4.93 × 10−7	25.29	
		rs2524084	G	A	0.159207	0.0231191	5.72 × 10−12	47.42	
		rs604203	A	C	−0.110139	0.0233266	2.34 × 10−6	22.29	
		rs7731626	A	G	−0.166028	0.0263485	2.95 × 10−10	39.71	
		rs7765391	G	A	0.150193	0.0301676	6.40 × 10−7	24.79	
		rs80173645	T	C	−0.230557	0.0500842	4.16 × 10−6	21.19	
		rs9277362	G	A	−0.170701	0.0273219	4.16 × 10−10	39.03	
EA = effect allele, GCA = giant cell arteritis, PMR = polymyalgia rheumatica, SE = standard error, SNP = single-nucleotide polymorphism, OA = other allele, SE = standard error.

3.2. Causality and sensitivity analysis of GCA and PMR

When GCA was used as an exposure, the results of the IVW method of analysis found that there may be a potential causal relationship between GCA and PMR, as shown in Figure 3 and Table 2, and the presence of GCA may increase the risk of PMR by 22% (OR, 1.22 [95% CI, 1.11–1.34]; P < .01). The MR-Egger method (OR, 1.14 [95% CI, 0.84–1.54]; P = .44), the weighted median method (OR, 1.20 [95% CI, 1.06–1.36]; P < .01; P = .004), and the weighted mode method also showed consistent results. As shown in Figure 3, horizontal pleiotropy (P = .65) and heterogeneity (Q_pval > 0.05) were not detected when the MR-Egger method was used. The leave-one-out analysis indicated that the results of the IVW analysis of the remaining 7 SNPs were similar to those of the analysis that included all SNPs after excluding each SNP in turn, and no SNPs were found to have a large impact on the causal association estimates. The sensitivity test proved that there was no horizontal polytropy present, and all of the above indicated that the results of the MR’s analysis were reliable.

Table 2 Mendelian randomization analysis in both directions of GCA and PMR.

Exposure	Outcome	Methods	OR	95% CI	P	
GCA	PMR	Inverse variance weighted	1.221077393	(1.11–1.34)	1.8746 × 10−5	
		MR-Egger	1.137701329	(0.84–1.54)	.445230888	
		Weighted median	1.200258843	(1.06–1.36)	.004012754	
		Weighted mode	1.188409066	(1.00–1.41)	.09028302	
PMR	GCA	Inverse variance weighted	1.581744697	(1.28–1.96)	2.60316 × 10−5	
		MR-Egger	1.536146793	(0.76–3.10)	.27692317	
		Weighted median	1.609782846	(1.22–2.12)	.000728227	
		Weighted mode	1.583965319	(1.12–2.23)	.034468315	
CI = confidence interval, GCA = giant cell arteritis, MR = Mendelian randomization, OR = odds ratio, PMR = polymyalgia rheumatic.

Figure 3. Mendelian randomization (MR) is used to estimate the causal relationship between giant cell arteritis (GCA) and polymyalgia rheumatic (PMR). (A) Forest plot of single-nucleotide polymorphism (SNP) is associated with PMR and its risk to GCA. (B) Scatterplot of SNP is associated with PMR and its risk to GCA. (C) Leave-one-out method eliminates an SNP to assess the effect of the remaining SNP.

3.3. Causality and sensitivity analysis of PMR and GCA

When setting GCA as the outcome and PMR as the exposure factor, the results of the IVW method of analysis showed that there may be a potential correlation between the performance of GCA and PMR, as shown in Figure 4 and Table 2, and the presence of PMR may increase the risk of GCA by 58% (OR, 1.58 [95% CI, 1.28–1.96]; P < .01). The results of the MR-Egger method (OR, 1.54 [95% CI, 0.76–3.10]; P = .27), the weighted median method (OR, 1.61 [95% CI, 1.12–2.12]; P < .01), and the weighted mode method were also consistent with our study. No single SNP significantly biased the causal effect of PMR on GCA. There was no significant directed-level pleiotropy (P = .93) or heterogeneity (Q_pval > 0.05) between PMR and GCA in MR-Egger regression analyses. The leave-one-out analysis illustrated that the results of the IVW analysis of the remaining 8 SNPs were similar to those of the analysis that included all SNPs after excluding each SNP in turn, and no SNP was found to have a large impact on the causal association estimates. All of the above results indicate that the analytical results of MR are reliable.

Figure 4. Mendelian randomization (MR) is used to estimate the causal relationship between polymyalgia rheumatic (PMR) and giant cell arteritis (GCA). (A) Forest plot of single-nucleotide polymorphism (SNP) is associated with GCA and its risk to PMR. (B) Scatterplot of SNP is associated with GCA and its risk to PMR. (C) Leave-one-out method eliminates an SNP to assess the effect of the remaining SNP.

4. Discussion

In this study, a 2-sample MR analysis of the correlation between the risk of developing PMR and GCA was conducted by using the published genome-wide association study dataset in the FinnGen (R10) database. Meanwhile, our results confirmed the PMR risk, and there may be a potential causal relationship between GCA. Also, neither heterogeneity nor horizontal pleiotropy was found after sensitivity analyses using different methods, which suggests that the results were more plausible.

As 2 inflammatory diseases, GCA and PMR share the common feature that they always occur after the age of 50. In the Finnish data population analyzed in this study, the mean age of first illness for both GCA and PMR reached 68.15 and 69.67 years. In addition, GCA and PMR are frequently associated clinically.[24] As a result, the 2 are sometimes considered to be different stages of the same disease, and current studies have hypothesized that these 2 autoimmune diseases may have similar underlying pathophysiologic mechanisms.[25,26] This conjecture is supported by our findings that the presence of GCA may increase the risk of PMR by 22% (OR, 1.22 [95% CI, 1.11–1.34]; P < .01), while the presence of PMR may increase the risk of GCA by 58% (OR, 1.58 [95% CI, 1.28–1.96]; P < .01). This suggests that PMR and GCA may have a potential causal relationship at the genetic level. Autoimmune diseases are caused by a combination of genetic, epigenetic, immune, and environmental factors, with genetic susceptibility playing a decisive role. Therefore, we hypothesized that the new GCA treatment might also apply to PMR and vice versa.

GCA and PMR have multiple susceptibility factors, including older age, a predominance of female patients, and a high prevalence of Nordic ancestry. In the Finnish population, the prevalence of GCA was 0.26%, with a higher prevalence in girls (0.28%) than in men (0.23%). Similarly, the prevalence of PMR was 0.97%, with the prevalence still higher among women (0.97%) than among men (0.9%). Recent studies have also confirmed that GCA and PMR are more common in populations of Nordic origin than in other populations, with an increased incidence. This also leads us to hypothesize that the 2 diseases may share many similarities at the genetic and pathogenic levels. In a large-scale genetic analysis study, human leucocyte antigen (HLA) class I and class II molecules were found to be strongly associated with GCA susceptibility.[27] Although an association between the HLA-major histocompatibility complex allele and PMR/GCA overlap has been noted in studies, its contribution to the susceptibility to isolated PMR varies in different populations (increased C frequency of the IL-6-174 allele is more common in HLA-DRBI *04-negative patients with GCA).[28] In a research of several cytokines, it was discovered that IL-6 promoter polymorphisms may increase susceptibility to GCA and/or PMA,[29] and elevated serum levels of IL-6 may also be related to disease activity in GCA and PMR.[30] In a study of the effects of cardiovascular disease on GCA and PMR, it was also revealed that diastolic blood pressure higher than 90 mm Hg was associated with the development of PMR, smoking increased the risk of GCA, and PMR and GCA shared common risk factors for the development of vascular lesions, which suggested a common underlying tendency.[31] All of the above studies have proved that GCA and PMR are identical or similar in a variety of susceptibility factors, which also informs subsequent studies exploring the pathogenesis of both diseases.

Several studies have confirmed that GCA and PMR share many similarities in their biological pathogenesis. Dendritic cells play an important role in the initiation of GCA, can be activated by specific pattern-recognition receptors in the epithelium of the arterial wall, and subsequently trigger an inflammatory cascade involving macrophages and T cells.[32–34] Moreover, synovial macrophages and dendritic cells present in the synovium, tendon sheaths, and bursa of patients with PMR participate in the inflammatory response through the aforementioned mechanisms.[35,36] In addition, common immune pathways exist in GCA and PMR. Patients with both diseases develop a large expansion of myeloid cells (i.e., monocytes and neutrophils) and IL-17-producing T helper 17 and cytotoxic T cells.[37,38] Serum IL-6 concentrations are significantly elevated in both diseases, and anti-IL-6 receptor therapy was proven to be effective in GCA and PMR.[39,40] Arterial inflammation in patients with GCA is characterized by extensive infiltration of T helper 1 and T helper 17 cells,[37,41] as well as proinflammatory responses and tissue-degrading macrophages.[42,43] In the PMR Mechanisms of Mutual Disease in Synovial Disease Study program, it was found that macrophages that predominate in PMR synovium also produce IL-6.[44–46] Meanwhile, granulocyte-macrophage colony-stimulating factor–producing macrophages are heavily enriched in PMR synovium and inflamed arterioles of patients with GCA.[47] In summary, the combination of our MR analysis results further suggests a bidirectional causal relationship between GCA and PMR, which could provide a valuable statistical basis for the diagnosis and treatment of PMR and GCA clinically.

There are some shortcomings and limitations in this study. First, since the data source of this study was mainly the Nordic population, the applicability of the results to other populations needs to be further verified. Second, MR is an efficient causal analysis method, but MR cannot further explore the biological mechanisms between GCA and PMR. In addition, further genetic or molecular experimental studies should be carried out in the future to deeply elucidate the potential causal association between the 2 diseases. Finally, the data applied in this study were obtained from FinnGen’s pooled data, and there may be sample overlap that has an impact on our results. The weak instrumental bias from sample overlap depends on the magnitude of sample overlap. In order to avoid the possibility of some bias in the results of the study, we calculated the effect of sample overlap, which was found to have a bias of only 0.011 on the effect of the results assuming a sample overlap of 100%, with a category 1 error percentage of 0.05, which is not enough to affect our conclusions.

5. Conclusion

In summary, this study is the first to analyze the causal relationship between GCA and PMR using a 2-sample MR approach. The results suggest that GCA increases the risk of PMR, and PMR is an important risk factor for GCA. There may be a potential causal relationship between PMR and GCA, but the underlying pathophysiologic mechanisms of PMR and GCA are unclear. Data from genome-wide association studies with larger sample sizes are still needed to validate this relationship in the future, and genetic and molecular mechanisms of the association between GCA and PMR should be further strengthened. Most importantly, this research provides extensive genetic data and theoretical support for future clinical diagnosis and testing of GCA and PMR.

Acknowledgments

The authors thank all participants for the study that they conducted.

Author contributions

Data curation: Lin Teng, Jingchun Jin.

Investigation: Lin Teng, Lei Li.

Methodology: Lin Teng, Lei Li, Dinglu Cui.

Project administration: Lin Teng.

Writing – original draft: Lin Teng.

Validation: Lei Li, Rongxian An.

Formal analysis: Dinglu Cui.

Resources: Dinglu Cui.

Supervision: Rongxian An.

Writing – review & editing: Rongxian An, Jingchun Jin.

Conceptualization: Jingchun Jin.

Funding acquisition: Jingchun Jin.

Abbreviations:

CI confidence interval

GCA giant cell arteritis

HLA human leucocyte antigen

IL interleukin

IV instrumental variable

IVW inverse variance weighted

MR Mendelian randomization

OR odds ratio

PMR polymyalgia rheumatica

PRESSO pleiotropy residual sum and outlier

SNP single-nucleotide polymorphism

This research was funded by the National Nature Science Foundation of China (grant 82360441) and by a project funded by the Science and Technology Department of Jilin Province (grants 20200201492JC and YDZJ202201ZYS161).

Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Teng L, Li L, Cui D, An R, Jin J. Polymyalgia rheumatica and giant cell arteritis: A bidirectional Mendelian randomization study. Medicine 2024;103:38(e39723).
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