Causal effects of type 2 diabetes, obesity, gout, and hypothyroidism on carpal tunnel syndrome: a univariable and multivariable Mendelian randomization study
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Carpal tunnel syndrome (CTS) is a prevalent peripheral neuropathy with unclear pathogenesis. This study investigated the causal relationships between metabolic diseases and CTS using univariable and multivariable Mendelian randomization (MR) analyses.
Methods
Single nucleotide polymorphisms from large genetic databases served as instrumental variables. Genome-wide data on type 2 diabetes (T2D), obesity, gout, hypothyroidism, and CTS were obtained from the IEU OpenGWAS Project (Integrative Epidemiology Unit Open Genome-Wide Association Studies Project). Univariable MR analysis assessed individual causal effects, and multivariable MR evaluated combined effects. Sensitivity analyses examined heterogeneity and pleiotropy.
Results
The univariable MR analysis identified significant associations of CTS with obesity (odds ratio [OR], 1.0135; 95% confidence interval [CI], 1.0118–1.0151; P<0.001), T2D (OR, 1.0028; 95% CI, 1.0011–1.0045; P=0.0011), gout (OR, 1.0559; 95% CI, 1.0152–1.0982; P=0.0067), and hypothyroidism (OR, 1.0517; 95% CI, 1.013–1.0919; P=0.0084). The multivariable MR analysis confirmed obesity (OR, 1.0129; 95% CI, 1.0088–1.0171; P<0.001) and T2D (OR, 1.002; 95% CI, 1.0006–1.0033; P=0.0042) as significant independent risk factors. Gout and hypothyroidism lost significance after adjustment. No notable pleiotropy was observed.
Conclusion
Obesity and T2D independently increase CTS risk, whereas the univariable associations for gout and hypothyroidism were attenuated after adjustment for other metabolic conditions, suggesting mediation by or confounding with obesity and glucose metabolism in this dataset.
Carpal tunnel syndrome (CTS) is a common peripheral neuropathy that primarily manifests as numbness, pain, and sensory loss in the hands. In severe cases, it can affect daily life and work ability [1]. CTS pathogenesis is complex and involves multiple factors such as genetics, environment, lifestyle, and comorbidities. The incidence rate is approximately 1% to 5% [2,3]. Although CTS pathogenesis is diverse, recent studies have shown that metabolic diseases such as diabetes, obesity, and metabolic syndrome may significantly increase CTS risk [2].
Metabolic diseases are a group of conditions characterized by insulin resistance, chronic inflammation, and lipid metabolism disorders. They not only threaten health but also increase the risk of CTS through multiple mechanisms. Diabetes mellitus can result in microangiopathy and neuropathy [4,5], while obesity is associated with weight gain and systemic inflammation [6,7]. Gout is characterized by the deposition of urate crystals and subsequent inflammation in the periarticular tissues [3,8], and hypothyroidism may impair neurological function through metabolic dysregulation [8,9]. Nevertheless, the majority of existing research comprises observational studies, which are susceptible to confounding variables and reverse causality, thereby complicating the establishment of a definitive causal relationship between metabolic disorders and CTS.
Mendelian randomization (MR) is a research methodology based on genetic variation [10]. By employing genetic variants as instrumental variables (IVs), MR can significantly mitigate the influence of confounding factors and reverse causality, thereby facilitating a more precise assessment of the causal relationships between exposure factors and diseases [11]. Through MR analysis, genetic IVs can be associated with metabolic diseases and CTS risk, enabling the exploration of the causal impact of these metabolic diseases on CTS risk.
While a previous MR study primarily focused on obesity and type 2 diabetes (T2D) using FinnGen CTS data and performed further mediation analyses [2], the present study extends the evidence base by leveraging the IEU OpenGWAS (Integrative Epidemiology Unit Open Genome-Wide Association Studies) summary statistics and evaluating a broader set of metabolic disorders, including gout and hypothyroidism, in both univariable and multivariable MR (MVMR) frameworks. By modeling multiple metabolic exposures simultaneously, we aim to clarify which metabolic conditions exert independent causal effects on CTS risk, determine which observed associations may be attributable to shared metabolic pathways, and facilitate the optimization and implementation of pertinent prevention and treatment strategies.
Methods
Ethics statement
Only publicly available summary data were used in this study; therefore, no ethical approval was required. The genetic and phenotypic data used in this study were obtained from the public IEU OpenGWAS database.
Study design and data sources
This study was reported in accordance with the STROBEMR (Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization) guidelines, and the completed checklist is provided in Supplement 1. The three fundamental assumptions and the comprehensive study design flowchart associated with the MR study are displayed in Figure 1: (1) the IVs are robustly associated with metabolic diseases; (2) the IVs are independent of known confounding factors; and (3) the IVs affect CTS only and not through alternative pathways. This study followed the appropriate guidelines and regulations [12]. Genetic instruments for exposures (T2D, obesity, gout, and hypothyroidism) and the outcome (CTS) were extracted from the IEU OpenGWAS (https://gwas.mrcieu.ac.uk/). This project provides public data from large-scale genome-wide association studies (GWAS). These datasets include genotype and phenotype data from tens of thousands of participants, ensuring the reliability and representativeness of the research results. Detailed information on the relevant data sources used in the analysis is provided in Table 1.
Genetic instrument selection
Single nucleotide polymorphisms (SNPs) associated with metabolic diseases, including T2D, obesity, gout, and hypothyroidism, were identified at a genome-wide significance threshold (P<5×10–8) using data from the IEU OpenGWAS. Linkage disequilibrium (LD) among these SNPs for each exposure was calculated using the PLINK clumping method, with reference to the 1000 Genomes LD reference panel (European population). We clumped at r2<0.01 within a 10,000-kB window, retaining the SNP with the lowest P-value. The F-statistic was used to ensure the robustness of the selected IVs. Variables with an F-statistic >10 were typically classified as strong, whereas those that did not meet this threshold were excluded from the analysis. The F-statistic was calculated using the formula: F=(β/SE)2.
Statistical analysis
Univariable MR analysis was conducted to evaluate the independent causal effects of various metabolic diseases on CTS, using SNPs as IVs. The inverse-variance weighted (IVW) method served as the primary statistical approach [12], whereas the MR-Egger [13], weighted median [14], simple mode [15], and weighted mode methods [16] were employed as secondary analytical techniques. MR-Egger employs a weighted regression technique to estimate causal effects and remains valid even when certain IV assumptions are violated. This method operates under more lenient assumptions, such as the presence of weak instruments or the absence of measurement error. Conversely, the weighted median method assumes that at least 50% of the genetic instruments are valid IVs, thereby providing reliable association estimates. Cochran’s Q statistic within the IVW and MR-Egger models was used to evaluate SNP heterogeneity. Statistical significance was set at P<0.05. Based on the univariable analysis, a MVMR method was employed to adjust for the influence of other metabolic diseases and to evaluate the independent causal effect of each metabolic disease on CTS risk. The MVMR analysis is instrumental in distinguishing the independent contributions of each metabolic disease to CTS risk, thereby mitigating the impact of potential confounding factors. All statistical analyses were conducted using the TwoSampleMR, MVMR, and MendelianRandomization packages [17] in the R software environment ver. 4.3.2 (The R Foundation).
Results
Univariable MR analysis of metabolic diseases and CTS
The relationships between the four metabolic factors (body mass index [BMI], T2D, gout, and hypothyroidism) and CTS are shown in Figure 2. The outcomes of the univariable MR analyses indicated no potential weak-instrument bias, as all F-statistics were >10.
BMI and CTS
The IVW method demonstrated a positive association between BMI and the risk of CTS (odds ratio [OR], 1.0135; 95% confidence interval [CI], 1.0118–1.0151; P<0.001). MR-Egger showed an OR of 1.016 (95% CI, 1.011–1.021; P<0.001), and weighted median showed an OR of 1.0135 (95% CI, 1.0112–1.0158; P<0.001). A scatter plot of the MR estimates for BMI in relation to CTS is shown in Figure 3A.
T2D and CTS
The IVW method demonstrated a positive association between T2D and CTS risk (OR, 1.0028; 95% CI, 1.0011–1.0045; P=0.0011). MR-Egger showed an OR of 1.0003 (95% CI, 0.9965–1.0042; P=0.8711), and weighted median showed an OR of 1.0017 (95% CI, 0.9996–1.0037, P=0.1176). A scatter plot of the MR estimates for T2D in relation to CTS is shown in Figure 3B.
Gout and CTS
The IVW method demonstrated a positive association between gout and CTS risk (OR, 1.0559; 95% CI, 1.0152–1.0982; P=0.0067). MR-Egger showed an OR of 1.0573 (95% CI, 0.9861–1.1336; P=0.1349), and weighted median showed an OR of 1.0695 (95% CI, 1.014–1.1281; P=0.0135). A scatter plot of the MR estimates for gout in relation to CTS is shown in Figure 3C.
Hypothyroidism and CTS
The IVW method demonstrated a positive association between hypothyroidism and CTS risk (OR, 1.0517; 95% CI, 1.013–1.0919; P=0.0084), MR-Egger showed an OR of 1.0754 (95% CI, 0.9808–1.1791; P=0.129), and weighted median showed an OR of 1.0544 (95% CI, 1.0026–1.1089; P=0.0393). A scatter plot of the MR estimates for hypothyroidism in relation to CTS is shown in Figure 3D.
Sensitivity analyses supported a potential causal association between metabolic diseases prognosticated by the four genes and CTS. The outcomes derived from the four methods—MR-Egger, weighted median, simple mode, and weighted mode—were predominantly consistent with the directional findings obtained using the IVW method (Figure 3). In the Cochran’s Q statistic, the P-value for gout exceeded 0.05, indicating no heterogeneity. Conversely, the P-values for the other three diseases (BMI, T2D, and hypothyroidism) were all below 0.05, indicating heterogeneity. The P-values of the MR-Egger intercept test for metabolic diseases were as follows: BMI, 0.3051; T2D, 0.171; gout, 0.965; and hypothyroidism, 0.6059, indicating the absence of horizontal pleiotropy (Supplement 1). In addition, the leave-one-out analysis revealed that the overall results remained unaltered after the exclusion of any individual SNP. Despite the presence of heterogeneity factors such as BMI, T2D, and hypothyroidism, the primary findings remained consistent across various analytical methods. This heterogeneity could potentially be attributed to biological pleiotropy and population diversity. However, the absence of horizontal pleiotropy bolsters the reliability of the study outcomes.
MVMR analysis of metabolic factors and CTS
The outcomes of the MVMR analysis are shown in Figure 4. This analysis revealed that gout and hypothyroidism did not exhibit a significant association with CTS risk following mutual adjustment (gout: OR, 1.0461; 95% CI, 0.9339–1.1717; P=0.4366; hypothyroidism: OR, 1.0114; 95% CI, 0.9415–1.0864; P=0.7568). Conversely, the IVW models demonstrated a persistent causal association between genetically predicted BMI and T2D and CTS risk (BMI: OR, 1.0129; 95% CI, 1.0088–1.0171; P<0.001; T2D: OR, 1.002; 95% CI, 1.0006–1.0033; P=0.0042). The study findings suggest a direct causal link between BMI and T2D and increased susceptibility to CTS.
Discussion
This study systematically evaluated the effects of metabolic diseases, such as diabetes, obesity, gout, and hypothyroidism, on CTS using univariable and MVMR methods. The results indicated that T2D and BMI substantially increased the risk of CTS, whereas gout and hypothyroidism exhibited weaker associations. In the MVMR analysis, T2D and BMI were independent risk factors for CTS after controlling for other metabolic disorders. These findings imply that T2D and obesity may play a critical role in CTS pathogenesis.
Our findings are broadly consistent with those of Mi and Liu [2], who reported that genetically predicted BMI and T2D increased CTS risk using FinnGen as the outcome dataset and further quantified the mediation proportion. However, several design features distinguish the present study. First, regarding data sources, they used FinnGen CTS summary statistics, whereas we used CTS GWAS summary data available through the IEU OpenGWAS platform, which may differ in terms of case definition, ascertainment, and sample composition. Second, regarding analytical scope, they mainly evaluated obesity- or glycemic-related traits and performed mediation MR, whereas we extended the exposure space to include gout and hypothyroidism and focused on MVMR to estimate independent causal effects when multiple metabolic disorders co-occur. Third, for effect estimates, they reported an OR of 1.66 per 1-standard-deviation increase in BMI, whereas our ORs were smaller in magnitude, likely reflecting differences in exposure scaling, outcome GWAS definitions, and instrument selection. Importantly, despite differences in scale, the directionality and conclusion that obesity and T2D are causal risk factors remain consistent across studies. Collectively, our study provides incremental evidence by clarifying that the apparent associations of gout and hypothyroidism with CTS are attenuated after mutual adjustment, suggesting that obesity and T2D may be the dominant independent metabolic drivers of CTS risk within a genetically informed framework.
Obesity has emerged as a significant global health concern and is closely associated with numerous chronic diseases and health complications. Recent observational studies and investigations into biological mechanisms have highlighted the association between obesity and CTS, indicating that obesity is an independent risk factor for CTS [18,19]. Furthermore, these studies suggest that obesity may increase CTS risk through various mechanisms [6,7]. For instance, the accumulation of adipose tissue in the wrists and hands can lead to increased localized mechanical pressure. Furthermore, obesity is significantly associated with chronic, low-grade systemic inflammation; inflammatory mediators, such as tumor necrosis factor-alpha and interleukin-6, secreted by adipose tissue, may induce inflammatory responses within the carpal tunnel, thereby exacerbating compression of the median nerve. Additionally, individuals with obesity frequently experience fluid retention, which can further elevate the pressure within the carpal tunnel and contribute to median nerve compression. In this study, we evaluated the association between obesity, as indicated by BMI, and CTS using both univariable and MVMR methods. The findings demonstrated that an elevated BMI significantly increased CTS risk, suggesting that obesity is a critical factor in CTS pathogenesis. This observation is consistent with the results of a previous MR study conducted by Mi and Liu [2]. Furthermore, in the MVMR analysis, BMI remained an independent risk factor for CTS even after adjustment for other metabolic conditions, such as T2D, gout, and hypothyroidism, thereby elucidating the causal relationship. This finding underscores the significance of obesity management in preventing CTS, provides a scientific foundation for clinical practice, and advocates early intervention in populations with elevated BMI to mitigate CTS risk.
Diabetic neuropathy, a common complication associated with diabetes, substantially impairs the nervous system and affects approximately 50% of individuals diagnosed with the condition [4,5]. This neuropathy not only markedly diminishes patients’ quality of life but also can result in severe complications. Previous studies have yielded inconsistent findings regarding the association between diabetes and CTS. Numerous cross-sectional and longitudinal studies have demonstrated that CTS prevalence is significantly higher among individuals with diabetes than among those without diabetes, indicating an elevated risk in the diabetic population [20]. In a systematic review and meta-analysis, diabetes was identified as a risk factor for CTS, with the association being modest and not significantly different between type 1 and T2D [21]. Several related studies have elucidated potential underlying mechanisms contributing to this association [6,22]. For instance, prolonged hyperglycemia can result in microvascular lesions surrounding the median nerve, leading to nerve ischemia and subsequent damage. Moreover, diabetes-induced systemic inflammatory responses and oxidative stress may elevate pressure within the carpal tunnel by exacerbating local inflammatory processes, thereby increasing CTS risk. Additionally, hyperglycemia can contribute to the accumulation of advanced glycation end products in connective tissues, which increase tendon and ligament stiffness. This can further alter the structural integrity of the carpal tunnel and increase CTS risk. However, some studies have demonstrated no association between diabetes and CTS incidence [23]. Although observational studies and biological mechanisms support the association between diabetes and CTS, these studies may be influenced by confounding factors and reverse causality. To more accurately assess the causal relationship between diabetes and CTS, this study employed the MR method as a robust tool for evaluating the causal link between T2D and CTS risk. The findings demonstrated that T2D increases CTS risk, suggesting that T2D plays a significant role in CTS pathogenesis.
The relationship between gout and CTS has not been studied as extensively as that between diabetes and obesity; however, several observational studies have found that CTS prevalence in patients with gout is higher than that in the general population [3,8]. The association between gout and CTS may be partially attributed to the deposition of urate crystals within the carpal tunnel, which induces local inflammation, resulting in tissue swelling and edema. Additionally, the deposition of urate crystals in joints and soft tissues can directly compromise tissue integrity, leading to inflammation and hypertrophy of the synovium and tendon sheath. These pathological changes elevate pressure within the carpal tunnel, thereby exacerbating compression of the median nerve [3,8]. However, divergent perspectives exist, as evidenced by the findings by Rhee et al. [24] in an 11-year study of the Korean population, which concluded that gout does not increase CTS risk. Despite the space-occupying characteristics of gouty tophi, a literature review indicates that CTS secondary to gout is infrequent, with most previous studies limited to case reports. This rarity is attributed to the infrequent involvement of the wrist in gout, which becomes even less common with appropriate treatment. In our study, the univariable MR analysis indicated an association between gout and CTS risk. However, this association was not corroborated by the MVMR analysis, implying that the relationship between gout and CTS may be mediated by concurrent metabolic disorders through shared metabolic pathways, such as hyperuricemia.
There is a recognized clinical association between hypothyroidism and peripheral neuropathy, and several observational studies have reported a higher CTS prevalence among patients with hypothyroidism [8,9,25]. Mechanistically, glycosaminoglycan deposition, edema, and fluid retention in hypothyroidism may increase intracarpal pressure and aggravate median nerve compression. In our univariable MR analysis, genetically predicted hypothyroidism was associated with higher CTS risk. Notably, a recent two-sample MR study by Duan et al. [25] reported a significant causal effect of hypothyroidism on CTS risk in European-ancestry data (IVW OR, 1.04). Our univariable estimate was directionally consistent with this report; however, the association was attenuated and became non-significant in our MVMR after adjustment for BMI, T2D, and gout. This discrepancy may be explained by differences in the analytic framework and covariate structure. Duan et al. [25] primarily performed univariable MR, whereas our MVMR estimates the direct effect of hypothyroidism on CTS conditional on major metabolic pathways (obesity and glucose metabolism) that are correlated with thyroid dysfunction. Therefore, our findings suggest that the hypothyroidism-CTS relationship may be partially mediated by or confounded with obesity and dysglycemia in this dataset rather than representing a fully independent pathway. Additional sources of heterogeneity may include differences in GWAS phenotype definitions, sample composition, and instrument selection. Future trans-ethnic MVMR analyses with harmonized phenotype definitions are warranted to clarify the independent contribution of hypothyroidism.
This study has several strengths and clinical implications. Compared with traditional observational studies, the MR design mitigates issues of reverse causality and the influence of potential confounding factors. In addition, this approach is cost-effective and time-efficient, providing robust evidence for evaluation. From a clinical perspective, our results support incorporating metabolic risk stratification into CTS prevention and early detection. In primary care, patients with obesity and/or T2D could be prioritized for symptom screening (e.g., nocturnal paresthesia, hand numbness, and functional impairment) and targeted counseling on weight management and glycemic control as modifiable upstream factors. For patients with early or mild-to-moderate symptoms, initial conservative management strategies (e.g., night splinting and shared decision-making regarding local corticosteroid injection) are supported by contemporary evidence-based guidance and trials. Given that CTS is a common cause of work disability, the findings also highlight opportunities for preventive practice, including ergonomic assessment and early referral for electrodiagnostic evaluation when symptoms are persistent, progressive, or associated with motor deficits, consistent with guideline-based management pathways.
This study has several limitations. First, although the MR method can mitigate the impact of confounding factors and reverse causality, the selection of genetic IVs remains susceptible to pleiotropy. Despite conducting sensitivity analyses to assess its influence, pleiotropy cannot be entirely eliminated. Second, the study used data extracted from a public GWAS database. The intrinsic heterogeneity of various data sources might potentially influence the solidity of the study outcomes and their interpretation. Third, all GWAS summary statistics used in this study were derived predominantly from individuals of European ancestry (largely from UK Biobank/Medical Research Council-IEU resources). Population-specific genetic architecture, LD patterns, exposure definitions, and baseline CTS risk may differ across ancestries. Therefore, the causal estimates reported here may not be directly generalizable to East Asian or Korean populations. Future studies should prioritize trans-ethnic MR and replication in independent East Asian GWAS datasets to assess the transportability of these findings.
In conclusion, this study demonstrated that obesity and T2D are significant independent causal risk factors for CTS. Although gout and hypothyroidism were positively associated with CTS in the univariable MR analysis, these effects were attenuated after adjustment for other metabolic conditions, suggesting mediation by or confounding with obesity and glucose metabolism in this dataset. These findings underscore the importance of obesity and T2D management for CTS prevention and motivate future transethnic and mechanism-oriented studies to clarify indirect pathways linking other metabolic disorders to CTS.
Notes
Conflict of interest
No potential conflict of interest relevant to this article was reported.
Acknowledgments
We express our gratitude to the researchers involved in the initial genome-wide association study for supplying the summary statistics. AI-based tools were used only for limited English language editing (grammar and clarity) during manuscript preparation. AI tools were not used for data analysis, result generation, or scientific interpretation.
Funding
This work was supported by the Jilin Provincial Natural Science Foundation-General Program (Grant No. YDZJ202501ZYTS104).
Data availability
All data used in this study are openly accessible without any limitations and can be obtained upon request.
Author contribution
Conceptualization: QL. Methodology: QL, WG. Software: HT. Validation: LH. Formal analysis: HT, LH. Investigation: HT, LH. Resources: QL. Data curation: WG. Project administration: QL. Visualization: LH. Supervision: QL. Writing–original draft: HT. Writing–review & editing: all authors. Final approval of the manuscript: all authors.
Overview of the Mendelian randomization (MR) study design. SNP, single nucleotide polymorphism; LD, linkage disequilibrium; BMI, body mass index; IVW, inverse-variance weighted.
Figure. 2.
Forest plot showing the results of the univariable MR analysis. SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; BMI, body mass index; MR, Mendelian randomization.
Figure. 3.
Scatter plots of the MR estimates for metabolic diseases associated with CTS. (A) MR scatter plot for BMI and CTS. (B) MR scatter plot for type 2 diabetes and CTS. (C) MR scatter plot for gout and CTS. (D) MR scatter plot for hypothyroidism and CTS. MR, Mendelian randomization; SNP, single nucleotide polymorphism; CTS, carpal tunnel syndrome.
Figure. 4.
Forest plot displaying the results of the multivariable Mendelian randomization analysis. SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; BMI, body mass index.
Table 1.
Detailed information on the included studies and consortia
GWAS, genome-wide association study/studies; SNP, single nucleotide polymorphism; BMI, body mass index; MRC, Medical Research Council; IEU, Integrative Epidemiology Unit; NA, not applicable.
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Causal effects of type 2 diabetes, obesity, gout, and hypothyroidism on carpal tunnel syndrome: a univariable and multivariable Mendelian randomization study
Figure. 1. Overview of the Mendelian randomization (MR) study design. SNP, single nucleotide polymorphism; LD, linkage disequilibrium; BMI, body mass index; IVW, inverse-variance weighted.
Figure. 2. Forest plot showing the results of the univariable MR analysis. SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; BMI, body mass index; MR, Mendelian randomization.
Figure. 3. Scatter plots of the MR estimates for metabolic diseases associated with CTS. (A) MR scatter plot for BMI and CTS. (B) MR scatter plot for type 2 diabetes and CTS. (C) MR scatter plot for gout and CTS. (D) MR scatter plot for hypothyroidism and CTS. MR, Mendelian randomization; SNP, single nucleotide polymorphism; CTS, carpal tunnel syndrome.
Figure. 4. Forest plot displaying the results of the multivariable Mendelian randomization analysis. SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; BMI, body mass index.
Graphical abstract
Figure. 1.
Figure. 2.
Figure. 3.
Figure. 4.
Graphical abstract
Causal effects of type 2 diabetes, obesity, gout, and hypothyroidism on carpal tunnel syndrome: a univariable and multivariable Mendelian randomization study
Trait
GWAS ID
Consortium
Population
Sample size (case:control)
SNP
URL
Exposure
https://gwas.mrcieu.ac.uk/
BMI
ukb-b-19953
MRC-IEU
European
461,460
9,851,867
Type 2 diabetes (adjusted for BMI)
ebi-a-GCST007516
NA
European
298,957 (48,286:250,671)
190,208
Gout
ukb-a-107
Neale Lab
European
337,159 (4,807:332,352)
10,894,596
Hypothyroidism
ukb-b-4226
MRC-IEU
European
463,010 (9,674:453,336)
9,851,867
Outcome
https://gwas.mrcieu.ac.uk/
Carpal tunnel syndrome
ukb-b-3965
MRC-IEU
European
463,010 (8,289:454,721)
9,851,867
Table 1. Detailed information on the included studies and consortia
GWAS, genome-wide association study/studies; SNP, single nucleotide polymorphism; BMI, body mass index; MRC, Medical Research Council; IEU, Integrative Epidemiology Unit; NA, not applicable.
