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. 2024 Nov 1;103(44):e40271. doi: 10.1097/MD.0000000000040271

Impact of immune cells on the risk of frozen shoulder: A 2-sample Mendelian randomization study

Yinji Luo a, Xinyu Wang b, Bin Wang a,*
PMCID: PMC11537608  PMID: 39496049

Abstract

The pathogenesis of frozen shoulder (FS) remains unclear, and current research primarily focuses on immune responses. Increasing evidence suggests that immune cells play a significant role in FS development. However, the causal relationship between the two remains poorly understood. Therefore, we aimed to investigate this using Mendelian randomization (MR) analysis. Single nucleotide polymorphisms closely associated with 731 immune phenotypes were obtained from publicly available GWAS datasets as instrumental variables. FS was used as the outcome with a sample size of 451,099 cases. Causal effects were analyzed using the inverse variance-weighted method. We conducted sensitivity tests, including the intercept of the MR-Egger and MR-PRESSO analyses. The presence of heterogeneity was evaluated using Cochran Q test. We identified potential causal relationships in terms of increased risk for FS with 5 immune phenotypes: CD25++ CD45RA+ CD4 not regulatory T cell %CD4+ T cells (odds ratio [OR] = 1.0273, 95% confidence interval [CI]: 1.0093–1.0457, P = .0028), CD25++ CD45RA+ CD4 not regulatory T cell %T cell (OR = 1.0240, 95% CI: 1.0057–1.0427, P = .0098), CD127 on CD28+ CD4+ T cells (OR = 1.0398, 95% CI: 1.0121–1.0682, P = .0046), CD4 on human leukocyte antigen DR+ CD4+ T cells (OR = 1.0795, 95% CI: 1.0316–1.2195, P = .0009), and human leukocyte antigen DR on CD14− CD16+ monocytes (OR = 1.0533, 95% CI: 1.0136–1.0945, P = .0081). Few significant heterogeneities or horizontal pleiotropies were observed. Through MR analysis, we identified distinct 5 types of immune cells that were positively correlated with the occurrence and development of FS, providing guidance for clinical intervention in FS.

Keywords: frozen shoulder, genome-wide association studies, immune cells, Mendelian randomization, SNPs

1. Introduction

Frozen shoulder (FS), alternatively referred to as adhesive capsulitis, is a debilitating condition characterized by pain and gradual restriction of both active and passive movements of the shoulder joint.[1] Rough estimates suggest that FS may affect approximately 2% to 5% of the general adult population,[2] with a higher incidence in women aged 40 to 70.[3] Other risk factors include diabetes,[4] hyperthyroidism,[5] infrequent immunization,[6] and so on, which substantially increase the prevalence in these populations. Although there may be a natural tendency for symptom improvement in shoulder impingement, studies indicate that some patients may experience persistent symptoms and loss of motion.[3] King et al[7] identified the predicaments faced by patients with FS, including restricted mobility, sleep deprivation, delayed diagnosis, and uncertainty regarding effective treatment.

The etiology and pathogenesis of FS remain unclear and are commonly considered idiopathic. Current research primarily focuses on the role of the immune response. Patients with FS exhibit notable increases in the expression levels of IL-1α, IL-1β, COX-1, COX-2, and TNF-α within the joint capsule and subacromial bursa.[8] In a prospective case-control study, Kabbabe et al[9] found increased mRNA expression levels of inflammatory and fibroblast cytokines, such as IL-6 and IL-8, in the synovium of patients with FS. Histological analysis of affected tissues in patients with FS by Hand et al[10] revealed evidence of infiltration by inflammatory cells, including T cells, B cells, macrophages, and mast cells. Several studies have indicated a close association between immune cells, inflammatory responses, and FS. However, further investigations are required to elucidate the precise pathological processes involved.

Mendelian randomization (MR) is a statistical technique that uses genetic variants as instrumental variables (IVs) to infer causal relationships between traits and diseases, particularly in situations in which randomized controlled trials are difficult to conduct.[11] It leverages the principles of Mendel laws of inheritance, which state that alleles are randomly assigned as they are passed from parent to offspring. As a result, the genotype of the offspring is typically unrelated to confounding factors. Furthermore, genetic variation is fixed at the time of conception and generally predates the measured variables; therefore, the possibility of reverse causation is circumvented.[12] The purpose of this study was to investigate the causality between immunophenotypes and FS, which may help gain a deeper understanding of the pathophysiological processes of FS and refine current preventative and therapeutic measures.

2. Methods

2.1. Data sources

We extracted GWAS summary statistics for 731 distinct immunological traits based on research published by Orrù et al.[13] The 731 immunophenotypes examined included absolute cell counts, median fluorescence intensities, surface antigen levels, morphological parameters, and relative cell counts. We obtained summary statistics of FS from 451,099 individuals of European descent. In total, 15,184,371 single nucleotide polymorphisms (SNPs) were included in this study.[14] Detailed information on the data sources is provided in Table 1. The goal of MR analysis is to uncover causal linkages between either single or multiple exposures and a specific outcome, achieved through the utilization of genetic variation as a proxy for risk factors. A comprehensive research methodology is outlined in Figure 1. The GWAS datasets employed are openly accessible to the public, with no individual-level data, and thus, do not require ethical approval.

Table 1.

Details of the genome-wide association studies and datasets used in our analyses.

Exposure of outcome Sample size Ancestry List of data download
Frozen shoulder 451,099 European ancestry https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90000512/
Genetic variation for immunophenotypes 3757 European ancestry https://gwas.mrcieu.ac.uk/datasets/
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Figure 1.

Figure 1.

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Mendelian randomization flowchart for immunophenotypes and frozen shoulder. IV = instrumental variable, MR = Mendelian randomization, SNP = single nucleotide polymorphisms.

2.2. Instrumental variables

The selection of IVs in MR analysis requires satisfying 3 assumptions: IVs exhibit a direct relationship with exposure; IVs are not influenced by or correlated with confounding variables; and IVs exert an influence on the outcome solely through their association with exposure.[15] The 3 major assumptions of MR are shown in Figure 2.

Figure 2.

Figure 2.

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Assumptions in MR studies: a brief overview. MR = Mendelian randomization.

In this study, we applied a threshold of statistical significance set at P < 1.0 × 10−5 to ensure a strong association between IVs and immunophenotypes. We applied specific criteria (linkage disequilibrium[16] r2 threshold <0.001 within 10,000 kb distance[17]) to ensure independence among IVs, aiming to minimize potential bias from genetic linkage disequilibrium. In genetic studies, the absence of a specific SNP in the outcome dataset can be addressed using proxy SNPs that are in linkage disequilibrium. To guarantee a robust association between the proxy SNPs and the target SNPs, we set an r2 threshold >0.8 to allow the proxy SNPs to effectively function as IVs in place of the target SNPs. It is crucial to highlight that palindromic SNPs, characterized by A/T or G/C alleles, may introduce ambiguity in identifying the causal allele in both exposure and outcome GWAS. To address this, we excluded palindromic SNPs with an effect allele frequency ranging from 0.3 to 0.7. To conduct the analysis, we utilized the “TwoSampleMR” package and leveraged 1000 Genomes EUR data. We calculated the strength of each IV with the F-statistic and discarded SNPs with F < 10 using the equation F = β2/SE2 to avoid bias from weak IVs.[18] Moving forward, we focused on the prioritized SNPs with the most significant P value (P < .05) for each trait, resulting in the identification of 18,732 independent SNPs linked to 731 immune cell characteristics.

2.3. Statistical analysis

In our analysis, the inverse variance-weighted (IVW) method was primarily employed to investigate the causal effects between immune cells and FS, as it effectively mitigates outcome bias caused by heterogeneity. IVW, a weighted averaging method that computes the reciprocal of the variance for each estimate as weights, multiplies the weights by the corresponding estimate, and sums the weighted estimates to obtain the result, was successfully employed as a random-effects method to address any potential deviations arising from heterogeneity.[19] The weighted median is a statistical method used in MR analysis that considers the weights of the data points to calculate the central value. The weighted median method enables an unbiased estimation of causal effects, even when only half of the IVs are effective.[19] MR-Egger estimates causal effects by accounting for pleiotropy, providing insights into the causal linkages between an exposure factor and an outcome variable.[19] When dealing with MR studies encompassing numerous genetic variants, sensitivity assessments such as the intercept of MR-Egger and MR-PRESSO analyses serve as complementary tools to strengthen the credibility of the findings and detect/correct for potential biases stemming from horizontal pleiotropy.[19–21] Heterogeneity among IVs was assessed using Cochran Q test.[22] The MR analysis was executed utilizing R (version 4.3.0) with the “TwoSampleMR” package (version 0.5.7).

3. Results

3.1. Selecting IV for MR analysis

After selecting and coordinating IVs, we utilized 28 SNPs for CD25++ CD45RA+ CD4 not regulatory T cell %CD4+ T cell, 22 SNPs for CD25++ CD45RA+ CD4 not regulatory T cell %T cell, 21 SNPs for CD127 on CD28+ CD4+ T cell, 21 SNPs for CD4 on human leukocyte antigen DR (HLA-DR)+ CD4+ T cell and16 SNPs for HLA-DR on CD14− CD16+ monocyte. The F-statistics for SNPs linked to 731 immune cell features spanned 19.55 to 2381.77, indicating no weak instrument bias. Detailed SNP information for each trait is provided in Tables 1 and 2, Supplemental Digital Content, http://links.lww.com/MD/N819.

3.2. The cause-and-effect relationship between FS development and immune phenotypes

Although most immune cells did not show a clear causal relationship with FS in this study, as indicated in Table 3, Supplemental Digital Content, http://links.lww.com/MD/N819, and Figure 3, we identified 5 immune cell traits that act as risk factors for FS, as shown in Table 4, Supplemental Digital Content, http://links.lww.com/MD/N819 (P < .01).

Figure 3.

Figure 3.

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Forest plot: associations of genetically determined bacterial traits with frozen shoulder risk. CI = confidence interval, HLA-DR = human leukocyte antigen DR, OR = odds ratio.

Through the IVW analysis method, we found a positive correlation between CD25++ CD45RA+ CD4 not regulatory T cell %CD4+ T cell (OR = 1.0273, 95% CI: 1.0093–1.0457, P = .0028) and FS, and the same was observed for CD25++ CD45RA+ CD4 not regulatory T cell %T cell (OR = 1.0240, 95% CI: 1.0057–1.0427, P = .0098). The MR-Egger analysis did not detect any significant outliers or indications of horizontal pleiotropy. The findings from the MR-Egger and weighted median analyses also support the conclusion drawn from the IVW. Likewise, the MR-PREESSO global test provided no evidence of horizontal pleiotropy (P = .3710 for CD25++ CD45RA+ CD4 not regulatory T cell %CD4+ T cells and P = .2830 for CD25++ CD45RA+ CD4 not regulatory T cell %T cell).

Similarly, the IVW results also indicated that both CD127 on CD28+ CD4+ T cells (OR = 1.0398, 95% CI: 1.0121–1.0682, P = .0046) and CD4 on HLA-DR+ CD4+ T cells (OR = 1.0795, 95% CI: 1.0316–1.2195, P = .0009) increased the risk of FS. The MR-Egger analysis did not identify any outliers or horizontal pleiotropy. In addition, MR-Egger and weighted median analyses further substantiated this positive association. The MR-PREESSO global test indicated the presence of horizontal pleiotropy (P = .5450 for CD127 on CD28+ CD4+ T cells and P = .3400 for CD4 on HLA-DR + CD4+ T cells).

The IVW method showed a positive correlation between HLA-DR and CD14− CD16+ monocyte (OR = 1.0533, 95% CI: 1.0136–1.0945, P = .0081), and FS risk was also observed. MR-Egger analysis showed no evidence of horizontal pleiotropy. However, in contrast to the aforementioned analysis of immune cells, while the results of the MR-Egger analysis supported IVW, the findings from the weighted median analysis were inconsistent with the IVW method and did not provide further support for a causal relationship between the two. Neither the intercept of the MR-Egger analysis (P = .2227) nor the MR-PREESSO global test (P = .3890) revealed horizontal pleiotropy.

The results of the IVW analysis consistently supported a positive correlation between these immune cells and FS risk. With the exception of the weighted median analysis for HLA-DR on CD14− CD16+ monocyte, which yielded results contrary to IVW, the MR-Egger and weighted median analyses for the remaining immune cells supported the conclusions drawn by IVW. This provides robust support for causal relationships between the studied variables. Furthermore, the lack of outliers and horizontal pleiotropy, as determined through the MR-Egger analysis and MR-PREESSO global test, confirmed the reliability of our findings (Tables 5 and 6, Supplemental Digital Content, http://links.lww.com/MD/N819). In order to provide additional evidence regarding the IVs of immunophenotypes, Cochran Q test was performed and found no significant heterogeneity among these variables, consistent with the previously discussed findings (Table 7, Supplemental Digital Content, http://links.lww.com/MD/N819).

4. Discussion

This 2-sample MR investigation uncovered a total of 5 immunophenotypes, including CD25++ CD45RA+ CD4 not regulatory T cell %CD4+ T cell, CD25++ CD45RA+ CD4 not regulatory T cell %T cell, CD127 on CD28+ CD4+ T cell, CD4 on HLA DR+ CD4+ T cell and HLA DR on CD14− CD16+ monocyte might be positively associated with the risk of FS.

Numerous studies have consistently demonstrated that chronic inflammation and tissue fibrosis are key mechanisms underlying the development of FS. Histologically, changes, such as increased vascularity, fibroblast proliferation, synovial thickening, and extracellular matrix deposition, have been identified to support this notion. Several inflammatory mediators, including TGF-β, IL-1, IL-6, IL-10, GM-CSF, M-CSF, PDGF, and TNF are involved in this process.[23]

Although some studies have suggested a potential association between abnormal activation of the immune system and FS pathogenesis, research specifically focusing on the role and mechanisms of T cells in FS is relatively limited. Most pertinent studies have been undertaken within the framework of other inflammatory states, such as arthritis, rather than specifically targeting FS. Immunocytochemical and histological staining of the FS biopsy material by Hand et al[10] revealed the presence of inflammatory infiltration, including T-cell infiltration, with significant positive staining for CD45 (leukocyte common antigen). Liu et al[24] also observed an increased percentage of CD45-immune cells in shoulder tissues, with the majority of the affected tissue consisting of lymphocytes, predominantly T cells. However, direct evidence supporting the relationship between the specific T cell subsets CD25++ CD45RA+ CD4 not regulatory T cell %CD4+ T cell and CD25++ CD45RA+ CD4 not regulatory T cell %T cell and FS has not been found.

The relationship between CD127 on CD28+ CD4+ T cells and FS has not yet been thoroughly investigated. CD127 serves as an integral component of the IL-7 receptor and plays a pivotal role in the development of T cells. Abnormal expression levels and dysfunctional behavior of IL-7 or its signaling pathway components are frequently linked to various chronic inflammatory autoimmune disorders, rheumatoid arthritis (RA) being one such prominent example. Aberrant expression and functional impairment of IL-7 or other components of the IL-7 signaling pathway are frequently linked to several chronic inflammatory autoimmune disorders, RA[25] and type 1 diabetes[26] being 2 such examples. The activation of CD28+ CD4+ T cells can promote the release of various inflammatory mediators, including IL-2, IL-4, IFN-γ, and TNF-α.

HLA-DR+ CD4+ T cells play crucial roles in inflammation and immune-related diseases such as colitis.[27] Through further analysis of T cell composition based on single-cell RNA sequencing data, Akbar et al[16] identified 8 T cell subgroups, including CD4+ T cells.

CD14− CD16+ monocytes are a nonclassical subset of monocytes that express HLA-DR more than classical and intermediate monocytes.[28] Liu et al[24] found that monocytes were one of the major infiltrating immune cells in FS. Research on the relationship between HLA-DR on CD14− CD16+ monocytes and sepsis has received more attention, but there is limited research specifically focusing on its association with FS. It has been observed that the population of CD14− CD16+ monocytes expands in the blood of patients with systemic infections, indicating their crucial role in rapid pathogen defense.[29] In many clinical situations, diminished expression of HLA-DR on monocytes in the peripheral blood has been highly correlated with infection, and decreased HLA-DR expression levels have been identified as an indicator of immune suppression.[30]

Although the current understanding of the pathological mechanisms underlying FS is limited and controversial, involvement of the immune system cannot be overlooked. Multiple pieces of evidence support the presence and role of immune cells and inflammatory factors in the occurrence and progression of FS, consistent with our findings.

Our study has several limitations. First, the data source for the analysis was primarily based on the European population in the GWAS. While this helps mitigate potential biases arising from immune feature differences among different ethnicities to some extent, it is constrained in terms of its applicability to other populations. Further analysis that incorporates data from diverse ethnic backgrounds is necessary for extrapolation to other races. Second, the available GWAS data did not provide specific individual-level information such as height, weight, and age, which would have allowed for further stratification and layered MR analysis for FS. These additional factors could have contributed to more precise causal inferences and better control of the potential confounders. Third, the threshold used for selecting IVs in our screening process was set at P < 1.0 × 10−5, a relatively less stringent criterion compared to the conventional significance level of P < 5 × 10–8. This may have introduced potential false positives. Nonetheless, we calculated the F-statistic as a measure to evaluate the potential for bias in our IVs, and the assessment revealed negligible risk.

5. Conclusions

FS remains a disease with unclear mechanisms, and there is a lack of in-depth and systematic research in the relevant areas. The findings from our research could have significant clinical implications, contributing crucial insights toward the prevention, timely diagnosis, and management of FS. Although our study offers a profound understanding of the possible causal link between immune cells and FS via MR analysis, a deeper probe into the underlying biological mechanisms is imperative. Our findings may have important clinical implications and provide crucial insights into the prevention, early diagnosis, and intervention of FS.

Author contributions

Conceptualization: Yinji Luo, Xinyu Wang, Bin Wang.

Data curation: Yinji Luo, Xinyu Wang.

Formal analysis: Yinji Luo, Xinyu Wang.

Methodology: Yinji Luo, Xinyu Wang.

Software: Yinji Luo, Xinyu Wang.

Validation: Yinji Luo, Xinyu Wang.

Visualization: Yinji Luo, Xinyu Wang.

Writing—original draft: Yinji Luo, Xinyu Wang, Bin Wang.

Writing—review & editing: Yinji Luo, Xinyu Wang, Bin Wang.

Investigation: Bin Wang.

Project administration: Bin Wang.

Supervision: Bin Wang.

Supplementary Material

Abbreviations:

FS
frozen shoulder
HLA-DR
human leukocyte antigen DR
IVs
instrumental variables
IVW
inverse variance-weighted
MR
Mendelian randomization
RA
rheumatoid arthritis
SNPs
single nucleotide polymorphisms

The authors have no funding and conflicts of interest to disclose.

No ethical approval was required for this study involving humans, as required by local legislation and institutional requirements. According to national legislation and system requirements, this study does not require written informed consent from the subjects, their legal guardians, or close relatives. No animal studies were conducted in this manuscript.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Digital Content is available for this article.

How to cite this article: Luo Y, Wang X, Wang B. Impact of immune cells on the risk of frozen shoulder: A 2-sample Mendelian randomization study. Medicine 2024;103:44(e40271).

YL and XW contributed equally to this work and shared the first authorship.

Contributor Information

Yinji Luo, Email: 2220964187@qq.com.

Xinyu Wang, Email: wangxy586@mail2.edu.cn.

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medi-103-e40271-s001.xlsx (4.7MB, xlsx)

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