Skip to main content

Genetic causal association between frozen shoulder and carpal tunnel syndrome: a two-sample mendelian randomization



Observational studies have suggested an association between frozen shoulder (FS) and carpal tunnel syndrome (CTS). However, due to challenges in establishing a temporal sequence, the causal relationship between these two conditions remains elusive. This study, based on aggregated data from large-scale population-wide genome-wide association studies (GWAS), investigates the genetic causality between FS and CTS.


Initially, a series of quality control measures were employed to select single nucleotide polymorphisms (SNPs) closely associated with the exposure factors. Two-sample Mendelian randomization (MR) was utilized to examine the genetic causality between FS and CTS, employing methods including Inverse-Variance Weighted (IVW), MR-Egger, Weighted Median, Simple Mode, and Weighted Mode approaches. Subsequently, sensitivity analyses were conducted to assess the robustness of the MR analysis results.


IVW analysis results indicate a positive causal relationship between CTS and FS (p < 0.05, OR > 1), while a negative causal relationship between the two conditions was not observed. Heterogeneity tests suggest minimal heterogeneity in our IVW analysis results (p > 0.05). Multivariable MR testing also indicates no pleiotropy in our IVW analysis (p > 0.05), and stepwise exclusion tests demonstrate the reliability and stability of the MR analysis results. Gene Ontology (GO) pathway analysis reveals enrichment of genes regulated by the associated SNPs in the TGFβ-related pathways.


This study provides evidence of the genetic causal association between frozen shoulder and carpal tunnel syndrome and provides new insights into the genetics of fibrotic disorders.

Peer Review reports


Adhesive Capsulitis, commonly known as frozen shoulder (FS), is a prevalent degenerative condition. The incidence of FS is 8% in males and 10% in females, with an average prevalence of approximately 2–5% [1, 2]. It typically manifests between the ages of 40 to 60 years [3]. FS is characterized by a gradual reduction in shoulder joint mobility accompanied by shoulder pain. It may develop after prolonged immobilization or be associated with systemic illnesses, often exhibiting an extended clinical course [4]. Patients often experience three overlapping phases: the inflammatory phase (pain and decreased range of motion), the frozen phase (stiffness), and the thawing phase (symptom resolution) [5]. The pathological hallmark of FS is fibroproliferative tissue fibrosis, characterized by the transformation of myofibroblasts from fibroblasts that predominantly produce Type I and Type III collagen, accompanied by inflammation, neovascularization and new nerve innervation. These processes ultimately result in fibrotic contraction and stiffness of the shoulder joint capsule [6]. The exact etiology of FS remains unclear, underscoring the imperative need for proactive exploration to inform treatment strategies and interventions for this condition.

Carpal tunnel syndrome (CTS) is the most prevalent surgically treated problem in the hand with the prevalence of 1–3% [7]. CTS primarily presents with symptoms such as pain, sensory abnormalities, and numbness in the hand, resulting from compression of tendons and the median nerve within the carpal tunnel. The pathophysiology of CTS remains poorly understood, with tendons and the median nerve within the carpal tunnel encapsulated by multiple layers of surrounding tissue known as subsynovial connective tissue (SSCT). Studies indicate that histological examinations of SSCT in CTS patients most commonly reveal non-inflammatory fibrosis within the carpal tunnel, leading to increased pressure within the carpal tunnel and nerve dysfunction [8]. Furthermore, the diminished capacity of fibrotic SSCT to withstand any new mechanical loads increases the likelihood of recurring fibrosis, thus perpetuating a vicious cycle [9].

Research indicates that there are certain commonalities in the pathogenesis of CTS and FS. However, further investigation is needed to elucidate the relationship between these two conditions and their respective mechanisms of onset. In epidemiological studies, Mendelian randomization (MR) is increasingly employed for inferring genetic causal relationships [10]. MR utilizes genetic markers closely associated with specific potential exposure factors as “instrumental variables” to assess the relationship between exposure factors and outcomes. Individual variations in genetic alleles, known as single nucleotide polymorphisms (SNPs), are exploited in MR because genetic markers (or alleles) are randomly allocated at conception, thereby leveraging this natural genetic variation to mitigate issues of confounding and reverse causation [11]. To date, there is a lack of research on the causal relationship between CTS and FS. Therefore, this study aims to investigate the causal relationship and underlying mechanisms of CTS and FS through a two-sample Mendelian randomization approach, utilizing the IEU OpenGWAS database. The goal is to provide novel strategies and treatment approaches for the diagnosis and management of these two diseases.

Materials and methods

Data search and filtering

The study was conducted using a two-sample Mendelian randomization (MR) approach. Firstly, we conducted a search in the IEU OpenGWAS database ( using the keyword “Carpal tunnel syndrome” to obtain the “ukb-d-G6_CARPTU” dataset, which was utilized as the “exposure factor.” Subsequently, we performed a search using the keyword “Frozen shoulder” to obtain the “ebi-a-GCST90000512” dataset, which was used as the “outcome " (forward MR analysis). Following that, we conducted an analysis in the reverse direction (reverse MR analysis) using the “ukb-d-G6_CARPTU” dataset as the “outcome " and the “ebi-a-GCST90000512” dataset as the “exposure factor”, enabling bidirectional investigation of the relationship between the exposure factor and outcome. The initial GWAS had obtained approval from the relevant institutional review board, and a detailed description of the research process can be found in our previously published study [12].

Data pre-processing

The exposure factor was retrieved and instrumental variables were selected using the “extract_instruments” function in the R package “TwoSampleMR”. The selection criteria included: p-value threshold of 5*10− 7, identifying SNPs significantly associated with the exposure factor; clump = TRUE, removing linkage disequilibrium from instrumental variables; r2 = 0.001; and kb = 10,000.

The outcome variable data was obtained using the “extract_outcome_data” function in the R package “TwoSampleMR”. Relevant SNPs associated with the outcome were extracted and filtered in conjunction with instrumental variables from the exposure factor. The filtering criteria included: proxies = TRUE; rsq = 0.8, excluding SNPs with intermediate allele frequencies.

Mendelian randomization (MR) analysis

We employed the “harmonise_data” function within the R package “TwoSampleMR” to standardize effect alleles and effect sizes. Subsequently, we conducted Mendelian randomization (MR) analysis using the “mr” function, incorporating five algorithms (MR Egger, Weighted median, Inverse variance weighted, Simple mode, Weighted mode). To assess the randomness of our analysis, for example, whether MR conforms to Mendel’s Second Law of random assortment, we created funnel plots by combining the β and standard error (SE) of each instrumental variable.

Sensitivity analysis

To evaluate the reliability of the MR analysis results, we conducted sensitivity analysis, including heterogeneity testing (using the “mr_heterogeneity” function in the R package “TwoSampleMR”), horizontal pleiotropy testing (using the “mr_pleiotropy_test” function in the R package “TwoSampleMR”), and leave-one-out analysis (employing the “mr_leaveoneout” function in the R package “TwoSampleMR”).

Expression quantitative trait loci (eQTL) analysis

To explore the role of instrumental variables in causal mediation between the exposure factor and outcome, we leveraged the instrumental variable SNPs obtained from MR analysis. Combining these SNPs with data from the eQTLGen database, we investigated genes that regulate gene expression from the perspective of cis-eQTL (cis-expression quantitative trait loci). Gene ontology (GO) enrichment analysis of the involved genes was performed using the R package “clusterProfiler” to explore gene expression regulation patterns.


Details of the datasets used in this study are provided in Table S1 (see Supplementary materials).

Characteristics of instrumental variables

After rigorous selection, we identified 13 SNPs associated with CTS (exposure factor). Combining these with the outcome (FS), we further filtered out SNPs with intermediate allele frequencies, leaving us with a set of 12 instrumental variables (SNPs) related to the outcome (Table S2). Applying the same approach, we reversed the exposure and outcome variables, treating FS as the exposure factor and CTS as the outcome, ultimately yielding 8 instrumental variables (SNPs) related to the outcome (Table S2).

Genetic causal relationship between CTS and FS

In the forward Mendelian randomization (MR) analysis, the Inverse Variance Weighted algorithm exhibited statistical significance (p < 0.05) with an OR > 1 (Table 1). The overall effect of the SNPs on both CTS and FS demonstrated a positive correlation trend, with effect sizes consistently greater than zero (Fig. 1A), implying that CTS is a risk factor for FS. Conversely, in the reverse MR analysis, none of the five algorithms showed statistical significance (p > 0.05, Table 1). Scatterplots and forest plots revealed an indistinct overall trend (Fig. 1B), suggesting that FS does not significantly impact CTS.

Table 1 Mendelian randomization (MR) analysis of FS and CTS
Fig. 1
figure 1

The overall effect of the instrumental variable SNPs on both CTS and FS. A) Scatterplot (a) and forestplot (b) of SNP effects on CTS as exposure and FS as outcome; B) Scatterplot (a) and forestplot (b) of SNP effects on FS as exposure and CTS as outcome

The randomness of the MR analysis

To determine if the analysis adhered to Mendel’s Second Law of random assortment. Funnel plots were constructed by combining the β and standard error (SE) of each instrumental variable (Fig. 2). In the forward MR analysis (Fig. 2A), instrumental variables were distributed symmetrically on both sides of the Inverse Variance Weighted (IVW) line, suggesting that the forward MR analysis conforms to randomness. Similarly, in the reverse MR analysis (Fig. 2B), instrumental variables exhibited relative symmetry on both sides of the IVW line, indicating that the reverse MR analysis also adheres to randomness.

Fig. 2
figure 2

Funnel plots constructed by combining the β and standard error (SE) of each instrumental variable in the forward MR analysis (CTS as exposure and FS as outcome, A) and the reverse MR analysis (FS as exposure and CTS as outcome, B)

The sensitivity analysis of the MR study

  1. 1)

    Heterogeneity analysis: In this MR analysis, as we conducted a two-sample approach using SNP-exposure and SNP-outcome data, potential differences in populations and sequencing methodologies could introduce heterogeneity among the samples, which might impact the analysis results. Therefore, to assess the influence of heterogeneity, we conducted a heterogeneity analysis. The results indicated that all P-values were greater than 0.05 (Table 2), signifying the absence of heterogeneity between the two sets of samples.

  2. 2)

    Horizontal pleiotropy test: to evaluate the presence of confounding factors in this study, we performed a horizontal pleiotropy test (Table 2). The results showed that the P-value was also greater than 0.05, indicating the absence of confounding factors in the study, further ensuring the reliability of the analysis results.

  3. 3)

    Leave-one-out analysis: to assess whether individual instrumental variables significantly altered the outcome, we conducted a leave-one-out analysis. The purpose was to systematically remove each SNP, calculate the meta-effect of the remaining SNPs, and observe if the results changed, and this analysis employed the inverse variance weighted method (Fig. 3). The results demonstrated that the effect of the remaining SNPs on the outcome did not change significantly with the stepwise removal of each SNP, indicating the reliability and stability of the MR analysis results.

Table 2 Sensitivity analysis of the MR analysis results of FS and CTS
Fig. 3
figure 3

Leave-one-out analysis employing the inverse variance weighted (IVW) method in the forward MR analysis (CTS as exposure and FS as outcome, A) and the reverse MR analysis (FS as exposure and CTS as outcome, B)

SNP and SNP-regulating gene pattern analysis

To explore the role of instrumental variables in the causal mediation between the exposure factor and the outcome, we leveraged instrumental variable SNPs obtained through MR analysis and combined them with data from the eQTLGen database to investigate genes that regulate gene expression from a cis-expression quantitative trait locus (cis-eQTL) perspective. In the set of instrumental variables from the forward MR analysis, a total of 14 genes were identified from 12 SNPs: R3HCC1L, UMPS, KIAA1715, LOXL4, CHMP2B, ITGB5, MYO1F, AGAP3, PYROXD2, SMEK2, GIMAP8, KCNH2, SMAD6, and CTD-2054N24.2, exhibiting cis-regulatory control over gene expression. The GO enrichment analysis of these 14 genes yielded 211 significant results (p < 0.05), encompassing 163 biological processes, 16 cellular components, and 32 molecular functions related to various pathways (Fig. 4). Notably, ITGB5 and SMAD6 were enriched in the transforming growth factor beta (TGFβ) receptor signaling pathway, indicating potential involvement of the TGFβ/SMAD (Suppressor of Mothers against Decapentaplegic, a well-conserved family of transcriptional factors) regulatory pathway in the development of CTS and FS.

Fig. 4
figure 4

The GO enrichment analysis of the 14 genes from a cis-eQTL perspective of 12 SNPs in the forward MR analysis (CTS as exposure and FS as outcome)


In this study, we conducted a bidirectional Mendelian randomization analysis combined with GWAS datasets to examine and explore the relationship between carpal tunnel syndrome (CTS) and frozen shoulder (FS) from a genetic perspective. Firstly, we identified 12 instrumental variables (SNPs) associated with CTS, which effectively predicted an increased risk of FS. However, the impact of FS on CTS was not significant. Next, we subjected the results of both forward and reverse Mendelian randomization analyses to sensitivity analyses, which included tests for heterogeneity, horizontal pleiotropy, and leave-one-out assessments. The findings revealed the following: (1) There was no heterogeneity between the two-sample datasets chosen for this analysis. (2) Exposure factors did not exhibit additional confounding factors when influencing the outcome through instrumental variables; (3) Overall, the instrumental variables showed stable effect estimates on the outcome. These three aspects collectively ensure the reliability of the MR analysis results. Last but not least, we conducted a Gene Ontology (GO) analysis for the 12 SNPs associated with CTS and increasing the risk of FS, along with the 14 genes they regulate. The analysis revealed enrichment in the TGFβ receptor signaling pathway, suggesting potential involvement of the TGFβ/SMAD regulatory pathway in the development of both CTS and FS.

From a pathogenic perspective, both FS and CTS are closely associated with connective tissue fibrosis. Research has indicated that specific differential finger movements may cause damage to the subsynovial connective tissue (SSCT) and lead to fibrosis due to traumatic injury [13]. Injured SSCT thickens and accumulates interstitial fluid, resulting in a narrowing of the carpal tunnel space, which in turn restricts the movement of tendons and the median nerve. In the case of FS, pain precedes stiffness in the course of the disease. This suggests an evolution from inflammation to fibrosis in the shoulder joint capsule during the development of FS [14]. Arthroscopic findings have revealed pathological features similar to palmar fascia contracture syndrome, characterized by connective tissue fibrosis around the biceps tendon and surrounding muscle tissues [4, 15]. Histologically, it is indicated that myofibroblasts replace fibroblasts, and an excess secretion of type III collagen fibers densely arranged around the joint capsule leads to restricted movement [16]. However, these findings are observed in patients with severe late-stage FS and may not be applicable to early stages. The differences in the fibrotic stage over the course of the disease may explain why CTS can serve as a risk predictor for FS, but not vice versa. Simultaneously, recognizing CTS as a manifestation of a systemic disorder may contribute to the early intervention in other fibrotic disorders such as adhesive capsulitis in knee and hip and elbow stiffness. This suggests that uncovering the genetic causal relationship between CTS and FS holds significant clinical implications for the diagnosis and treatment of fibrotic lesions in peripheral joints.

Research has demonstrated that TGFβ can promote fibrosis through three mechanisms [17, 18]: (1) TGFβ1 inhibits extracellular matrix (ECM) degradation by suppressing matrix metallopeptidase (MMP) expression or promoting its inhibitor TIMP expression; (2) TGFβ1 induces myofibroblast formation through the EMT effect; (3) TGFβ1 induces ECM production through SMAD3-dependent or non-SMAD-related mechanisms. The transcription factor SMAD proteins, as key intracellular effectors of TGFβ1, have been extensively studied [19]. Generally, SMAD3 and SMAD4 promote fibrosis, while SMAD2 and SMAD6/7 inhibit it. SMAD3 deficiency suppresses the expression of type I collagen and prevents the transformation of epithelial cells into myofibroblasts (EMT). In contrast, SMAD2 deficiency upregulates the expression of type I collagen. SMAD4 plays a crucial role in fibrotic diseases by enhancing SMAD3 promoter activity, whereas SMAD6/7 downregulates the fibrotic processes induced by SMAD3. Increasing evidence suggests that the TGFβ/SMAD signaling pathway is involved in organ fibrosis processes, including the liver [20] and kidneys [21]. It is noteworthy that the fibrotic changes mediated by the TGFβ/SMAD pathway are also crucial pathological alterations in elbow stiffness [22, 23], characterized by disrupted extracellular matrix and collagen fiber structure, elevated level of inflammatory cytokines and infiltration of myofibroblasts, ultimately leading to thickening of the elbow joint capsule [24]. These findings align with our results in this study, indicating a significant role of the TGFβ/SMAD signaling pathway in peripheral joint capsule fibrosis. However, conclusive molecular biology evidence is still required to substantiate these findings. Moreover, the genetic correlation among CTS, FS and elbow stiffness is currently under further investigation.

However, this study has certain limitations. Firstly, the subjects of this study are of European ethnicity, so caution should be exercised when applying the results of this study to other ethnicities. Confounding factors such as age, gender, and other environmental factors also have some impact on MR analysis. Nevertheless, this study still provides new insights from a genetic perspective into the genetic causality between CTS and FS, as well as the impact of TGFβ-mediated fibrosis on the pathogenesis of these conditions.

In summary, this study, through GWAS and Mendelian randomization analysis, has preliminarily established carpal tunnel syndrome as a genetic risk factor for frozen shoulder and has also suggested that the fibrosis and myofibroblast transformation mediated by TGFβ/SMAD may play important roles in the pathogenesis of both conditions. The results of this study contribute novel insights to the genetics of frozen shoulder.

Data availability

The summary-level data of CTS and FS came from the IEU OpenGWAS database (, accessed on September 1st, 2023). The MR analysis was performed through the TwoSampleMR packages (version 0.5.6)


  1. Walker-Bone K, Palmer KT, Reading I, Coggon D, Cooper C. Prevalence and impact of musculoskeletal disorders of the upper limb in the general population. Arthritis Rheum. 2004;51(4):642–51.

    Article  PubMed  Google Scholar 

  2. Sarasua SM, Floyd S, Bridges WC, Pill SG. The epidemiology and etiology of adhesive capsulitis in the U.S. Medicare population. BMC Musculoskelet Disord. 2021;22(1):828.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kingston K, Curry EJ, Galvin JW, Li X. Shoulder adhesive capsulitis: epidemiology and predictors of surgery. J Shoulder Elbow Surg. 2018;27(8):1437–43.

    Article  PubMed  Google Scholar 

  4. Millar NL, Meakins A, Struyf F, Willmore E, Campbell AL, Kirwan PD, Akbar M, Moore L, Ronquillo JC, Murrell GAC, Rodeo S. A., Frozen shoulder. Nat Reviews Disease Primers. 2022;8(1):59.

    Article  PubMed  Google Scholar 

  5. Blessing WA, Williamson AK, Kirsch JR, Grinstaff MW. The prognosis of Arthrofibroses: prevalence, clinical shortcomings, and future prospects. Trends Pharmacol Sci. 2021;42(5):398–415.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Robinson CM, Seah KT, Chee YH, Hindle P, Murray IR. Frozen shoulder. J Bone Joint Surg Br Vol. 2012;94(1):1–9.

    Article  CAS  Google Scholar 

  7. Padua L, Coraci D, Erra C, Pazzaglia C, Paolasso I, Loreti C, Caliandro P, Hobson-Webb LD. Carpal tunnel syndrome: clinical features, diagnosis, and management. Lancet Neurol. 2016;15(12):1273–84.

    Article  PubMed  Google Scholar 

  8. Festen-Schrier V, Amadio PC. The biomechanics of subsynovial connective tissue in health and its role in carpal tunnel syndrome. J Electromyogr Kinesiology: Official J Int Soc Electrophysiological Kinesiol. 2018;38:232–9.

    Article  CAS  Google Scholar 

  9. Wiberg A, Ng M, Schmid AB, Smillie RW, Baskozos G, Holmes MV, Künnapuu K, Mägi R, Bennett DL, Furniss D. A genome-wide association analysis identifies 16 novel susceptibility loci for carpal tunnel syndrome. Nat Commun. 2019;10(1):1030.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Lv X, Hu Z, Liang F, Liu S, Gong H, Du J, Deng X, Qian JH, Nie Q, Luo J. Causal relationship between ischemic stroke and its subtypes and frozen shoulder: a two-sample mendelian randomization analysis. Front Neurol. 2023;14:1178051.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Pierce BL, Kraft P, Zhang C. Mendelian randomization studies of cancer risk: a literature review. Curr Epidemiol Rep. 2018;5(2):184–96.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018, 7.

  13. Lluch AL. Thickening of the synovium of the digital flexor tendons: cause or consequence of the carpal tunnel syndrome? J Hand Surg (Edinb Scotl). 1992;17(2):209–12.

    Article  CAS  Google Scholar 

  14. Hand GC, Athanasou NA, Matthews T, Carr AJ. The pathology of frozen shoulder. J Bone Joint Surg Br Vol. 2007;89(7):928–32.

    Article  CAS  Google Scholar 

  15. Shaffer B, Tibone JE, Kerlan RK. Frozen shoulder. A long-term follow-up. J Bone Joint Surg Am Vol. 1992;74(5):738–46.

    Article  CAS  Google Scholar 

  16. Kilian O, Pfeil U, Wenisch S, Heiss C, Kraus R, Schnettler R. Enhanced alpha 1(I) mRNA expression in frozen shoulder and dupuytren tissue. Eur J Med Res. 2007;12(12):585–90.

    CAS  PubMed  Google Scholar 

  17. Border WA, Noble NA. Evidence that TGF-beta should be a therapeutic target in diabetic nephropathy. Kidney Int. 1998;54(4):1390–1.

    Article  CAS  PubMed  Google Scholar 

  18. Lan HY. Tubular epithelial-myofibroblast transdifferentiation mechanisms in proximal tubule cells. Curr Opin Nephrol Hypertens. 2003;12(1):25–9.

    Article  CAS  PubMed  Google Scholar 

  19. Morikawa M, Derynck R, Miyazono K. TGF-β and the TGF-β family: context-dependent roles in cell and tissue physiology. Cold Spring Harb Perspect Biol 2016, 8 (5).

  20. Xu F, Liu C, Zhou D, Zhang L. TGF-β/SMAD pathway and its regulation in hepatic fibrosis. J Histochem Cytochemistry: Official J Histochem Soc. 2016;64(3):157–67.

    Article  CAS  Google Scholar 

  21. Ma TT, Meng XM. TGF-β/Smad and renal fibrosis. Adv Exp Med Biol. 2019;1165:347–64.

    Article  CAS  PubMed  Google Scholar 

  22. Cohen MS, Schimmel DR, Masuda K, Hastings H 2nd;, Muehleman C. Structural and biochemical evaluation of the elbow capsule after trauma. J Shoulder Elbow Surg. 2007;16(4):484–90.

  23. Masci G, Cazzato G, Milano G, Ciolli G, Malerba G, Perisano C, Greco T, Osvaldo P, Maccauro G, Liuzza F. The stiff elbow: current concepts. Orthop Rev. 2020;12(Suppl 1):8661.

    Google Scholar 

  24. Mellema JJ, Lindenhovius AL, Jupiter JB. The posttraumatic stiff elbow: an update. Curr Rev Musculoskelet Med. 2016;9(2):190–8.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


We thank Dr. Yan Wu (The Second Affiliated Hospital Zhejiang University School of Medicine) for project and bioinformatic analysis guidance.


Jian Chen was supported by Zhejiang Provincial Education Department Project (grant number Y202249777). Yongxing Zhang was supported by National Natural Science Foundation of China (grant number 82302774) and Natural Science Foundation of Zhejiang Province (grant number LQ22H160034).

Author information

Authors and Affiliations



Y.C. and J.C. designed the project and wrote the main manuscript text, Y.C. X.W. and Y.Z. conducted the data analysis, Y.C. X.W. prepared Figs. 1, 2, 3 and 4; Table 1, and 2. All authors reviewed the manuscript.

Corresponding author

Correspondence to Jian Chen.

Ethics declarations

Ethics approval and consent to participate

The source of the data was a publicly available database, and no human participants were involved; hence, ethical parameters are not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Wu, X., Zhang, Y. et al. Genetic causal association between frozen shoulder and carpal tunnel syndrome: a two-sample mendelian randomization. BMC Musculoskelet Disord 25, 58 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: