Journal of Health Care and Research

Journal of Health Care and Research

Yi Zhang¹, Jianhong Ren², Rurong Wang^1,3iD*
1Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
²Department of Anesthesiology, Chengdu Shuangliu District Maternity Child Health Care Hospital, Chengdu, China
³Department of Anesthesiology, Cheng Du Shangjin Nanfu Hospital, Chengdu, Sichuan, China

Corresponding Author: Rurong Wang ^{ORCID iD}
Address: Department of Anesthesiology, West China Hospital, Sichuan University, No. 37, Guoxue Valley, Wuhou District, Chengdu, Sichuan, 610000, China.
Received date: 10 April 2025; Accepted date: 28 April 2025; Published date: 05 May 2025

Citation: Zhang Y, Ren J, Wang R. Risk Factors for Carpal Tunnel Syndrome: Mendelian Randomization Study. J Health Care and Research. 2025 May 05;6(1):26-36.

Copyright © 2025 Zhang Y, Ren J, Wang R. This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

Keywords: Carpal Tunnel Syndrome, Mendelian Randomization, Risk Factors

Abstract

Background: This study aimed to investigate potential risk factors for carpal tunnel syndrome (CTS). We used a Mendelian randomization (MR) approach to identify causal associations. It is contributing to the understanding of CTS development.
Methods: We employed MR analysis to investigate the potential links between 88 different risk factors and CTS. The analysis was conducted using data from a genome-wide association study (GWAS) that involved a large cohort of individuals with European ancestry, including 48,843 cases of CTS and 1,190,837 controls.
Results: Among the 88 potential risk factors, 19 traits, including Type 2 diabetes, obesity-related factors, psychiatric factors, hormone-related factors, lifestyle factors, and socioeconomic status, were significantly associated with CTS risk. Additionally, suggestive associations were observed with 17 other factors, including fasting glucose, depression, sleep duration, alcohol intake, and vitamin levels. However, no causal evidence was found for associations between autoimmune diseases, inflammatory biomarkers, acromegaly, and wrist fractures with the risk of CTS.
Conclusion: This Mendelian randomization study identifies several potential risk factors for CTS, shedding light on its multifactorial nature. These findings underscore the importance of metabolic, hormonal, lifestyle, and socioeconomic factors in CTS development, providing valuable implications for preventive measures and interventions.

Introduction

As the most common peripheral nerve entrapment syndrome worldwide, carpal tunnel syndrome (CTS) is a condition where the median nerve in the carpal tunnel of the wrist becomes compressed, leading to nerve entrapment. CTS is a common cause of work disability, affecting approximately 1–5% of the general population, and it imposes significant healthcare costs on society [1,2].

Several observational studies have identified several factors associated with CTS, including gender, age, race, obesity, alcohol consumption, drug toxicity and exposure to toxins, endocrine diseases, and specific occupations [1,3–9]. Nearly all research findings indicate that CTS is more prevalent in females, particularly during pregnancy and breastfeeding, with incidence rates up to three times higher than in males [7,10]. This gender difference can be partially attributed to hormonal factors. Postmenopausal women who take oral contraceptives or hormone replacement therapy in the first year after menopause, as well as those who undergo oophorectomy, seem to have a lower incidence of CTS [6,11,12]. The likelihood of developing CTS is 2.5 times higher in individuals who are obese compared to those who are not obese. Research suggests that trauma, such as wrist fractures, and inflammatory conditions like rheumatoid arthritis, may increase the capacity of the carpal tunnel, leading to CTS [13]. Some cases of CTS are associated with endocrine disorders, such as hypothyroidism, acromegaly, and diabetes.

Additionally, carpal tunnel narrowing due to trauma or inflammation caused by conditions such as inflammatory rheumatic diseases also pose risk factors. Extensive research has shown a positive correlation between CTS and occupations involving highly repetitive wrist motion, the use of vibratory tools, increased hand force, and prolonged or repetitive flexion/extension of the wrist [14,15]. In 2008, Thomsen et al. [16] and in 2014, Mediouni et al. [17] further investigated this association between computer work and CTS. Despite the fact that carpal tunnel pressure increases during computer keyboarding and mouse use, it remains below levels that are considered harmful. However, associations from observational studies can be potentially biased by confounding and reverse causality.

One way to address this issue is through MR analysis, which can help overcome the limitations of observational studies related to confounding factors and reverse causality [18,19]. The estimated familial occurrence of CTS is 17–39% [20,21] and the heritability of CTS has been estimated to be 0.46 in women [22]. A better understanding of potentially pathogenic risk factors for CTS will lead to better prevention of the disease. In this study, two-sample MR analysis was used to explore the causal relationship between potential risk factors and CTS.

Methods

MR Design:

Our study was conducted in accordance with the Declaration of Helsinki revised in 2013, and the methods followed the STROBE-MR checklist [23,24]. As our MR study was based on publicly available summary statistics, no IRB approval or informed consent was necessary. A total of 88 possible risk factors were enrolled and categorized into the following 14 groups: T2D-related factor, Obesity-related factor, Psychiatric factor, Hormone-related factor, Lifestyle factor, Socioeconomic status, lipids, Mineral, Amino acid, Inflammatory biomarker, Autoimmune disorder, Plasma fatty acid, Vitamin, and Other 14 factors.

Data Sources:

Genetic associations with CTS were obtained from the publicly available GWAS among individuals of European ancestry (n_case = 48,843 and n_control = 1,190,837) from Iceland, the UK, Denmark, and Finland (Suppl. Table-S1, data available at https://www.decode.com/summarydata/) [25]. Instrumental variables for the 88 exposures were identified from genome-wide association studies (GWASs) and are detailed in Table-1.

Table-1: Data Sources and Instrumental Variables used for Exposures Included in the MR Analyses

Selection and Validation of SNPs:

SNPs that met a significance threshold of P < 5e-08 were selected as instrumental variables (IVs). To ensure variable independence and account for LD effects, an LD parameter (r²) of 0.001 and a genetic distance of 10,000 kb were utilized. We removed palindromic SNPs from the instrumental SNPs that were chosen for analysis. The F statistic (F = beta²/se²) was employed to exclude weak instrumental biases, with SNPs having an F statistic < 10 being excluded [26]. Variance estimation was based on the formula R² = 2 × MAF × (1 − MAF) × (beta/SD) (MAF indicates minor allele frequency; beta estimation was based on MAF). Due to the limited number of SNPs associated with CTS at the p < 5e-08 significance level for Acromegaly and pituitary gigantism, the genetic instruments were adjusted to p < 1e-05.

MR Analysis:

The primary analysis method employed was the inverse variance-weighted (IVW) approach [27]. Additionally, MR Egger, weighted median, simple mode, and weighted mode methods were utilized. The estimates were reported as odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs). A Bonferroni-corrected significance level of p < 5.68e-04 (0.05 divided by 88 risk factors) was applied. To balance Type I and II error rates, P-values ranging from 5.68e-04 to 0.05 were considered indicative of suggestive associations (Suppl. Table-S2).

Pleiotropy and Heterogeneity Analysis:

Cochran’s Q test was performed to assess heterogeneity among individual causal effects, with significance defined as a Q_P-value < 0.05, or I² statistics > 25% indicating heterogeneity. We utilized MR-Egger regression to evaluate whether there is a presence of directional pleiotropy of instrumental variables [28] (Suppl. Table-S3). We identified exposures with horizontal pleiotropy for MR-Egger regression, performed Mendelian randomization pleiotropic residual and outlier (MR-PRESSO) analysis, and leave-one-out analysis to identify any outlier instrumental variables [29]. If outlier SNPs were found to impact the MR estimates, they were removed and MR analysis was performed again to confirm the stability of results (Suppl. Table-S4 and Suppl. Table-S5).

Methods

Among 88 possible risk factors, 19 traits, including Type 2 diabetes, body mass index, standing height, sitting height, waist circumference, arm fat mass (right), arm fat mass (left), trunk fat mass, whole body fat mass, short sleep duration, insomnia, age when periods started, sex hormone-binding globulin levels (SHBG), SHBG levels adjusted for BMI, coffee intake, years of schooling, qualifications, intelligence, and overall health rating, were robustly associated with the risk of CTS. There were suggestive associations with 17 factors, including fasting glucose, hemoglobin A1c, arm fat-free mass (left), arm fat-free mass (right), depression, sleep duration, frequency of tenseness/restlessness, alcohol intake frequency, smoking initiation, current tobacco smoking, ever smoked, plays computer games, HDL cholesterol, sodium in urine, osteoarthritis, hypertension, and diastolic blood pressure (Fig-1).

Fig-1:

T2D-Related Factor and Obesity-Related Factor:

Liability to Type 2 diabetes was significantly associated with an increased risk of CTS. Additionally, fasting glucose and hemoglobin A1c showed suggestive associations with an increased risk of CTS. Among the 13 obesity-related risk factors examined, eight demonstrated significant associations with CTS. Positive associations were found between CTS risk and BMI, waist circumference, arm fat mass right and left, trunk fat mass, and wholebody fat mass. In contrast, standing height and sitting height showed inverse and significant associations with CTS risk. Moreover, arm fat-free mass right and left exhibited suggestive associations with CTS risk, while birth weight, lean body mass, and adiponectin did not show any associations with CTS risk.

Psychiatric Factor:

Among the 9 psychiatric risk factors examined, five showed associations with CTS. Depression, short sleep duration, sleeplessness/insomnia, and frequency of tenseness/restlessness were positively associated with CTS risk, while sleep duration exhibited an inverse association with the risk.

Hormone-Related Factor:

Age when periods started, SHBG, and SHBG levels adjusted for BMI demonstrated a significantly inverse association with CTS risk. However, other hormone-related factors did not show any significant associations with CTS risk.

Lifestyle Factor and Socioeconomic Status:

Coffee intake was significantly associated with an increased risk of CTS. Alcohol intake frequency, age of smoking initiation, current tobacco smoking, ever smoked, and playing computer games showed suggestive associations with an increased risk of CTS. On the other hand, higher years of schooling, qualifications, and intelligence were all significantly associated with a lower risk of CTS.

Serum Lipids, Mineral, Amino Acid, Plasma Fatty Acid, and Vitamin:

Genetic predisposition to higher levels of high-density lipoprotein cholesterol was suggestively associated with a lower risk of CTS, whereas cysteine and sodium in urine were suggestively associated with a higher risk of CTS. There was limited evidence supporting causal associations of other factors with CTS.

Inflammatory Biomarker and Autoimmune Disorder:

There was no evidence of a causal association between autoimmune diseases (including Type 1 diabetes, allergic diseases, rheumatoid arthritis, and hypothyroidism/myxedema) or inflammatory biomarkers (including interleukin-6 levels, C-reactive protein, immunoglobulin E) and CTS risk.

Other Factors:

Among 14 other potential factors related to CTS, only overall health rating exhibited a statistically significant association with CTS. Three factors (osteoarthritis, hypertension, and diastolic blood pressure) were shown to be associated with CTS.

Discussion

This study utilized MR analysis to investigate the associations between a wide range of potential factors and CTS. After analyzing 88 possible risk factors, we found 19 traits significantly associated with CTS risk. Additionally, suggestive associations were observed between CTS risk and 17 other factors.

The robust associations between CTS risk and Type 2 diabetes and BMI are consistent with prior literature, supporting the role of metabolic factors in the development of CTS. Our study further explored additional factors related to these two variables. We found significant correlations between fasting glucose, HbA1c, and CTS risk, indicating that glucose dysregulation might play a role in CTS pathogenesis. Diabetes and obesity may increase pressure within the carpal tunnel, resulting in compression of the median nerve. Additionally, increased waist circumference, arm fat mass, and whole body fat mass may raise carpal tunnel pressure, leading to an elevated risk of CTS. Conversely, taller height, sitting height, and higher arm fat-free mass showed a negative correlation with CTS risk. Higher standing height and sitting height may lead to reduced pressure inside the carpal tunnel, thereby decreasing the likelihood of compression on the median nerve. Additionally, the correlation with upper limb fat-free mass suggests that muscle tissue might play a protective role in the development of CTS.

We have also identified the involvement of psychological factors, hormones, lifestyle, and socioeconomic status in the development of CTS. Research has shown a positive correlation between CTS risk and major depressive disorders, sleep duration, frequency of stress/anxiety, and hypertension. Interestingly, these psychological factors are often associated with stress and disrupted sleep patterns, which may affect neural function and contribute to the development of CTS. Conversely, the negative correlation between sleep duration and CTS risk could be attributed to the role of sufficient sleep in neural repair and recovery, thereby reducing the risk of CTS. Age at menarche, levels of SHBG, and SHBG levels adjusted for BMI show a significant negative correlation with CTS risk. However, menopausal age, estradiol levels, and total testosterone levels do not exhibit a significant association with CTS. Previous observational studies consistently indicate a higher risk of CTS in females, especially during pregnancy and lactation. Gender and life-stage differences in CTS risk may partially be attributed to variations in SHBG levels, providing theoretical support for hormone replacement therapy. These findings may be related to the protective and reparative effects of hormones in the nervous system and their regulatory role in inflammation responses.

Moreover, the research reveals a significant positive correlation between coffee consumption and CTS risk, while higher education level, academic qualifications, and intelligence exhibit a significant negative correlation with CTS risk. The positive correlation with coffee consumption may be due to the stimulating effect of caffeine on the nervous system, leading to an increased risk of CTS. On the other hand, higher education and intelligence levels may be associated with healthier lifestyles and better self-management, thereby reducing the risk of CTS. These findings offer new insights into understanding the underlying mechanisms of CTS. However, apart from the association with osteoarthritis, our analysis did not find causal evidence linking autoimmune diseases (such as rheumatoid arthritis, hypothyroidism/myxedema), inflammatory biomarkers, acromegaly and pituitary gigantism, and wrist fractures to CTS risk. Although these factors were previously considered to be related to CTS, further research is needed to determine their causal relationship with CTS.

One of the strengths of this study is the utilization of Mendelian randomization analysis to assess causal relationships. By simulating randomized trials through the random distribution of genotypes, this method reduces confounding bias and reverses causality issues commonly associated with observational study designs, thus enhancing the credibility of causal inference. Additionally, the study benefits from a large sample size, comprehensively investigating 88 potential factors. The research results encompass 19 factors significantly associated with CTS risk and 17 factors with suggestive associations, providing a rich dataset for gaining deeper insights into the underlying mechanisms of CTS. However, there are limitations to the Mendelian randomization analysis method. The assumption that genotypes do not directly affect the causal variable may not hold true in certain cases. Moreover, the reliance on known genetic associations with risk factors may be limited, potentially impacting the accuracy of the study’s findings. Furthermore, the study did not cover all factors related to CTS risk, despite exploring multiple potential factors. Unconsidered genetic and environmental factors may still play a role in the development of CTS.

Conclusion

This study employed Mendelian randomization analysis to identify several potential factors significantly associated with the risk of CTS. Factors such as short sleep duration, insomnia, Type 2 diabetes, obesity, psychiatric factors, hormone-related factors, coffee intake, lifestyle, and socioeconomic status were found to be potentially related to CTS development. These findings contribute to a deeper understanding of the pathogenesis of CTS and offer novel insights for its prevention and intervention. However, it is important to acknowledge that Mendelian randomization analysis has its limitations and should be complemented by other research methods for further validation. Future studies could delve into the biological mechanisms underlying the association with CTS risk, aiming to provide more robust evidence for the prevention and management of CTS.

Ethics Approval and Consent to Participate

As our MR study was based on publicly available summary statistics, no IRB approval or informed consent was necessary.

Consent for Publication

All authors have read and agreed to the final version of the manuscript.

Availability of Data and Materials

All data utilized were sourced from publicly available databases, as detailed in Supplementary Table 1.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Authors’ Contributions

Yi Zhang: Conceptualization, Visualization, Project administration, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing.
Jianhong Ren: Visualization, Project administration, Data curation, Formal analysis, Methodology, Writing – review & editing.
Rurong Wang: Project administration, Data curation, Formal analysis, Methodology, Writing – review & editing.

Acknowledgements

We would like to thank the researchers who collected and organized the shared GWAS data.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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