The causal relationship between multiple cardiovascular diseases and glioblastoma: A Mendelian randomization study.

1. Introduction

Glioblastoma (GBM) is one of the most common and aggressive primary brain tumors in the central nervous system, accounting for approximately 49% of primary malignant brain tumors, [ 1 ] and it is part of a heterogeneous group of malignant tumors originating from glial cells, the incidence of which increases with age. [ 2 ] It has been shown that tumor-induced hypercoagulability and bed-ridden treatment after surgery lead to an increased risk of postoperative venous thromboembolism in patients with GBM, which, in turn, increases the incidence of cardiovascular diseases (CVDs) such as pulmonary embolism (PE), deep vein thrombosis, coronary heart disease (CHD), and myocardial infarction (MI). [ 3 – 5 ] Therefore, patients with GBM often present with multiple CVDs. However, the causal relationship between CVDs and GBM is unclear.

CVDs, also known as circulatory diseases, can occur in any organ and tissue that transports blood in the body, with a wide range of lesions, and are the leading cause of death worldwide. [ 6 ] CVDs includes a wide range of diseases, including hypertension, CHD, MI, heart failure, atherosclerosis, and stroke, with CHD accounting for the highest percentage of cases. [ 7 ] CVDs involves complex pathophysiological changes, such as endothelial cell damage, chronic inflammation, dyslipidemia, endothelial lipid deposition, and atheromatous plaque formation. [ 8 – 10 ] Inflammatory factors, hypertension, and lipid metabolism disorders usually damage endothelial cells, resulting in endothelial cell dysfunction and lipid deposition, exacerbating the process of atherosclerosis and thrombosis, and thus increasing the prevalence of CVDs, such as CHD, MI, and venous thromboembolism (VTE). [ 11 – 13 ] When venous thrombosis is present, blood platelet levels are elevated, and platelets contain many growth factors that can be released upon activation to provide a pro-tumor microenvironment. [ 14 , 15 ] Thus, platelets may be involved in the progression of GBM. At the same time, GBM cells are capable of inducing platelet aggregation and exacerbating VTE. [ 16 ] Studies have shown that increased platelet counts in GBM patients preoperatively and after radiotherapy are associated with poorer survival. [ 17 ] In addition, it has been demonstrated that high expression of apolipoprotein E (APOE) and proteasome activity are involved in both a variety of CVDs and also play a role in the progression, invasion, and metastasis of GBM. [ 18 , 19 ] These studies suggest that CVDs and GBM are interrelated, but their causal relationship has not been reported. Therefore, the present study attempted to explore the correlation between CVDs and GBM using Mendelian randomization (MR) analysis to provide a new reference for the prevention and treatment of GBM.

Genome-wide association study (GWAS) have identified more than 450 genetic variants associated with an increased risk of tumors, providing an agnostic approach for studying susceptibility, survival prognosis, and genetic basis of complex diseases. Clinical applications of GWAS data offer opportunities for cancer prevention. [ 20 ] Whereas MR is a method of analysis of genetic variables that follows Mendelian laws of inheritance, it uses genetic variation as an instrumental variable to infer causal relationships between exposure factors and clinical outcomes. [ 21 ] Among other things, two-sample Mendelian randomization (TSMR) analyses can utilize single-nucleotide polymorphism (SNP) exposure and outcome associations from independent GWAS and combine them into a single causal estimate. With the rapidly increasing number of GWAS for cardiovascular disease and GBM, large-scale summary statistics have become more widely available, allowing TSMR analyses with significantly improved statistical efficacy. [ 22 , 23 ] Traditional observational studies have limitations in exploring the relationship between CVDs and GBM. This type of research is difficult to comprehensively and accurately control for confounding factors, and is susceptible to the influence of reverse causal relationships, resulting in the inability to clarify the causal direction between the 2 and complicating causal inference. [ 24 ] MR introduces instrumental variables (IVs) as genetic predictors, and the association between genes and diseases is not affected by common confounding factors such as environment, socio-economic factors, and individual behavior, which can effectively avoid the influence of confounding factors and exclude reverse causal relationships. [ 25 – 27 ] Therefore, this study used TSMR analysis to explore the causal relationship between CVDs and GBM, which is of great significance for clarifying the pathogenesis of GBM, evaluating risk factors, and developing new treatment strategies.

In this study, the data used was obtained from the GWAS database, and this data was analyzed in conjunction with TSMR. In this study, GBM was used as the outcome and a total of 18 cardiovascular-related diseases and events were used as exposure factors, including coronary artery disease, MI, coronary angioplasty, lower extremity deep vein thrombosis, PE, VTE, other embolisms and thrombosis, pulmonary heart disease, pulmonary circulation disease, coronary artery disease, major coronary events, and major coronary events other than hemodialysis. Exploring the causal relationship between multiple CVDs and GBM is significant for further revealing the pathogenesis of GBM.

2. Materials and methods

2.1. Data sources and study design

Genome-wide association study identity document (GWASid) for outcomes and exposure factors were searched and identified through the publicly available GWAS database ( https://gwas.mrcieu.ac.uk/ ). The datasets for GBM initially comprised 91 cases alongside 218,701 control samples. Validation of these findings was subsequently carried out using an additional dataset, which included 91 cases and 174,097 control samples. A total of 18 cardiovascular-related diseases and events were identified as exposure factors, including coronary artery disease, MI, coronary angioplasty, lower extremity deep vein thrombosis, PE, VTE, other embolisms and thrombosis, pulmonary heart disease, pulmonary circulatory disease, and major coronary events excluding hemodialysis, with specific details summarized in Table 1 and Table S1, Supplemental Digital Content, https://links.lww.com/MD/R405 below.

A TSMR analysis (version 0.5.6) [ 28 ] was used to explore the impact of multiple CVDs on GBM production. The flowchart of the study design was as below (Fig. 1 ). The 3 main assumptions of classical MR analyses were satisfied throughout the analysis: the independence assumption (there is no correlation between IVs and any confounders); the association assumption (IVs directly affect exposure); the exclusivity assumption (exposure is the only way that genetic variation can affect the outcome).

2.2. Selection of IVs

Exposure factors were read and IVs were selected using the extract_instruments function from the R package TwoSampleMR (version 0.5.7, https://github.com/qingyuanzhao/mr.raps ). First, SNPs significantly associated with CVDs were identified at the genome-wide significance level ( P < 5 × 10 − 8 ). Then, SNPs with linkage disequilibrium were trimmed using the clump function ( r 2 threshold of r 2 < 0.001 and kb > 10,000). [ 29 ] Followed by calculating the F statistic values of each SNP and deleting the SNPs with F statistic values of <10. The calculation method of the F -value was as follows: F=R21-R2×N-k-1k, R2=2×(1-MAF)×MAF×β2, N was the total sample size of the sample. Beta β was the SNP serving as an instrumental variable for the exposure. MAF was the minor allele frequency. k was the number of IVs. An F statistic value of <10 indicated a weak association between the instrumental variable and the exposure factor, which might lead to estimation biases. Therefore, such weak IVs needed to be removed to ensure the accuracy of the research results. [ 30 ] IVs with SNP ≥ 3 were selected for subsequent analysis. [ 30 ] In the subsequent MR analysis, a causal relationship was considered statistically significant if the P -value from the inverse variance weighted (IVW) method was <.05. Additionally, SNPs not associated with the outcome were extracted using the function extract_outcome_data. These rigorously selected SNPs were used as the final IVs for MR analysis.

2.3. TSMR analysis

Firstly, effect alleles and effect sizes were harmonized by the R package TwoSampleMR function harmonise_data to ensure that the alleles of each SNPs between multiple CVDs and GBM were consistent. Then, the causal relationship between them was assessed by TSMR analysis. In TSMR analysis, the IVW [ 31 ] approach served as the primary method to test the causal effect and was combined with the weighted median, [ 32 ] MR Egge, [ 33 ] simple mode, [ 28 ] and weighted mode [ 34 ] methods to infer causality. Among these 5 algorithms, the results derived from the IVW method were primarily adopted. When the corresponding P -value was <.05 and the odds ratio (OR) was >1, CVDs were identified as risk factors for GBM. Conversely, when the OR was <1, CVDs were considered protective factors against GBM. Specifically, it was assumed that there were n IVs. For each instrumental variable i , the effect estimate was denoted as β i , and the standard error was denoted as SE i . In the IVW method, the weight ω i of each instrumental variable was calculated by the formula ω i = 1 S E i 2 . The final causal effect estimate β IVW was: β I V W = ∑ i = 1 n ω i β i ∑ i = 1 n ω i . Additionally, the scatter plots, forest plots, and funnel plots were drawn for the purpose of exhibiting the result of MR intuitively. The scatter plot showed the relationship between the genetic instruments and the outcome variable, enabling us to visually assess the direction and strength of the association. The forest plot presented the results of each individual instrument and the overall effect estimate. It clearly depicted the magnitude and significance of the effects, facilitating the comparison of the roles of different instruments. The funnel plot was used to detect publication bias or heterogeneity in the data. Next, the mr_pleiotropy_test function was used to conduct an Egger intercept test to detect the presence of horizontal pleiotropy. This function evaluated whether the effect of SNPs on the outcome was consistent with the effect of SNPs on the exposure through regression analysis. If the intercept term was significant ( P < .05), it indicated the presence of horizontal pleiotropy. At the same time, the simple modes and weighted modes were applied to measure the robustness of the IVW method results.

2.4. Sensitivity analyses

First, the heterogeneity test was used to evaluate whether there was heterogeneity between the 2 datasets. In the event that the P -value was higher than .05, heterogeneity was not present. Secondly, the mr pleiotropy test function was applied to test whether there was horizontal pleiotropy between the CVDs and GBM datasets, if the P -value exceeded .05, it indicated the absence of any confounders, and the results were reliable. Then, the mr_leaveoneout function was used to conduct a leave-one-out analysis. The remaining SNPs were eliminated 1-by-1 to determine their effects on the study results. Finally, to detect the possible direction of the causal relationship between the exposure factor and the outcome, based on the above-mentioned IVW method, the screened exposure factors were selected for MR-Steiger filtering analysis. This analysis was used to determine whether the directionality of a single exposure factor was correct. Those with a P -value < .05 and a judgment of TRUE were retained for subsequent analysis. All statistical analyses were performed using the “TwoSampleMR” package in R software version 0.5.6.

3. Results

3.1. CVDs were a protective factor for GBM

A total of 432 SNPs were obtained as IVs for assessing the links that cause multiple CVDs with GBM (Table S2, Supplemental Digital Content, https://links.lww.com/MD/R405 ). The IVW results (Table 2 ) suggested that these CVDs were significant causal effects with GBM ( P < .05) and were protective factors for GBM (OR < 1). The scatter plot demonstrated that all of the slope trends were negative, suggesting that CVDs were protective factors against GBM (Figure S1, Supplemental Digital Content, https://links.lww.com/MD/R406 ). The distribution of SNP loci for the diagnostic efficacy of GBM in the data of these CVDs was were all <0 in the forest plot (Figure S2, Supplemental Digital Content, https://links.lww.com/MD/R406 ). SNPs were uniformly and randomly distributed on both sides of the IVW line (Figure S3, Supplemental Digital Content, https://links.lww.com/MD/R406 ), suggesting that the analyses followed Mendel second law. The above results were visualized on a forest plot (Fig. 2 ).

3.2. Results of CVDs as a protective factor for GBM were reliable

First, in the heterogeneity test, the P -value of the statistical results of the 5 algorithms was more than .05, suggesting that there was no heterogeneity between the 18 CVD datasets and the GBM dataset (Table 3 ). Secondly, in the horizontal polytropy test, the P -values of the CVDs results were all higher than .05, implying no horizontal polytropy in the study analysis (Table S3, Supplemental Digital Content, https://links.lww.com/MD/R405 ). Then, in the causal relationship between these 18 CVDs and GBM did not change significantly by excluding a SNP, which confirmed the stability of the result (Figure S4, Supplemental Digital Content, https://links.lww.com/MD/R406 ). Finally, all 18 CVDs passed the Steiger test ( P < .0001, causal direction was true), confirming the reliability of the forward analysis of causality (Table S4, Supplemental Digital Content, https://links.lww.com/MD/R405 ).

3.3. Validation of the causal relationship between CVDs and GBM using external datasets

First, IVs were screened using the external validation set finn-b-C3_GBM_EXALLC, and a total of 432 IVs passed the screening (Table S5, Supplemental Digital Content, https://links.lww.com/MD/R405 ). Next, MR analyses were performed to assess the causal effects of CVDs-related exposure factors on GBM as the outcome in the external validation set. The results of 2-sample MR analyses indicated significant protective effects for all 18 CVDs (Table 4 ). Subsequently, the validity of the MR results was verified through correlation analysis, diagnostic performance analysis, and randomness analysis (Figure S5, Supplemental Digital Content, https://links.lww.com/MD/R407 ). Forest plots comparing CVDs datasets from different databases with GBM outcomes in the external validation set visually demonstrated significant protective causal effects of CVDs on GBM development (Fig. 3 ). Sensitivity analyses were then conducted for the 18 CVDs and GBM. Heterogeneity tests showed P -values > .05 for all 18 CVDs, indicating no significant heterogeneity between exposure and outcome datasets (Table S6, Supplemental Digital Content, https://links.lww.com/MD/R405 ). Horizontal pleiotropy tests for the exposure factors of the 18 CVDs also yielded P -values > .05, suggesting no evidence of horizontal pleiotropy (Table S7, Supplemental Digital Content, https://links.lww.com/MD/R405 ). Leave-one-out analyses were performed by systematically removing each instrumental variable, and no significant deviations were observed in the models, further validating the reliability of the MR results (Figure S6, Supplemental Digital Content, https://links.lww.com/MD/R408 ). Finally, Steiger tests were carried out to confirm the directionality of the causal effects. The results showed no reverse causality for any of the 18 CVDs, thus ruling out potential reverse causation and confirming the reliability of the forward MR analyses (Table S8, Supplemental Digital Content, https://links.lww.com/MD/R405 ).

4. Discussion

GBM, a common aggressive brain tumor, often with diffuse infiltration, is highly malignant, rapid growth and poor prognosis. [ 35 ] GBM are thought to originate from glial stem cells, and more than 90% of GBM are isocitrate dehydrogenase wild-type tumors, the incidence of which increases with age, with a higher incidence in males than in females. Risk factors for GBM are currently unknown, except for rare genetic predispositions and radiation exposure. [ 36 ]

CVDs are the main cause of morbidity and mortality worldwide, accounting for about one-third of global deaths. [ 37 ] With the improvement of quality of life and unhealthy dietary habits, most middle-aged and older adults had a history of smoking, obesity, hypertension, hyperglycemia, and hyperlipidemia, which were often the traditional risk factors for atherosclerosis. [ 38 ] Lipoproteins play an important role in lipid metabolism, and them were regulated by genes and enzymes, making them therapeutic targets for CVDs. [ 39 ] Studies on APOE have confirmed this idea. [ 40 ] APOE not only regulated inflammation and immune function, but also regulated lipid metabolism and maintains lipid homeostasis in the body. [ 18 ] Some studies have shown that APOE immunotherapy could inhibit the development of GBM while improving cerebrovascular function. [ 41 ] Increased APOE gene expression could be detected in patients with CHD and heart attack at an early stage of the disease, which could predict the risk of cardiovascular events in different populations, and APOE was also highly expressed in patients with GBM. [ 18 ] There were countless studies on the molecular characterization, therapeutic approaches, and influencing factors of GBM, [ 36 , 42 ] whereas studies on the causal relationship between various CVDs and GBM had not yet been reported. Therefore, in this study, 91 cases and 218,701 control samples were screened in the GBM dataset based on comprehensive genetic data from a large European population. Eighteen cardiovascular-related diseases and events were identified as exposure factors to infer the effects of these cardiovascular diseases on GBM. Ultimately, our findings showed that these cardiovascular-related diseases had a significant causal effect ( P < .05) with GBM and were are potential protective factors for GBM. This provides a new reference for the prevention and treatment of GBM.

VTE, MI, and CHD are 3 major diseases associated with the cardiovascular system. VTE is a common complication of GBM, with an annual incidence of up to 30%. In a large retrospective cohort study including 450 patients with GBM, VTE occurred in 145 (32.2%), constituting the study population. Of these, 11 (7.6%) developed PE, 117 (80.7%) developed deep vein thrombosis (DVT), and 16 (11%) developed both DVT and PE. [ 43 ] It has been shown that the expression of vascular endothelial growth factor, the production of procoagulant factor-positive particles, inflammatory cytokines, and adhesion molecules have been associated with the progression of GBM, as well as being a major mechanism of cancer-related VTE. [ 44 – 46 ] In addition, adiponectin, the most abundant peptide secreted by adipocytes, is involved in both the progression of obesity, insulin resistance, and CVDs such as VTE, PE, and DVT, as well as in the regulation of GBM cell proliferation. [ 47 , 48 ] One study performed an MR analysis of circulating adipokine concentrations with VTE risk and found that adiponectin is a potential protective factor for VTE and PE. [ 49 ] Moreover, adiponectin treatment was able to block DNA synthesis and cell proliferation rate of GBM cells, leading to cell cycle arrest in G1 phase. AdipoR1-mediated microRNA-3908 was able to inhibit GBM tumorigenicity through downregulation of STAT2 associated with the AMPK/SIRT1 pathway. [ 50 ] AdipoR2 was able to inhibit GBM cell growth through the AMPK/mTOR pathway. [ 51 ] The above studies suggest that adiponectin is a protective factor for GBM, as well as for CVDs such as VTE and PE. However, the influencing factors of VTE are complex, which makes the causal relationship between GBM and VTE difficult to determine. In this study, the causal relationship between VTE and other embolic events as exposure factors was explored by MR analysis, and VTE was found to be a potential protective factor for GBM.

CHD is a kind of ischemic heart disease, coronary artery atherosclerosis or plaque aggregation caused by narrowing or occlusion of the lumen, resulting in myocardial ischemia, hypoxia or necrosis, and then chest pain, chest tightness, and other manifestations. MI mostly occurs on the basis of CHD, due to the lack of blood supply to the myocardium necrosis, resulting in impaired heart function. More and more studies have identified a possible link between CHD and MI and GBM. Proteasome is a major complex that maintains intracellular protein homeostasis, and its damage can lead to cardiac ischemia/reperfusion (I/R) injury. Increasing the expression/activity of immunoproteasome subunits, which further increases the expression of the pro-fission protein dynamin-1-like protein degradation and the pro-fusion protein mitofuscin fusion protein 1/2, significantly ameliorates I/R-mediated cardiac insufficiency by restoring the balance of mitochondrial fission/fusion and decreasing infarct size, apoptosis, and superoxide levels. [ 52 ] And TCH-165, a newly discovered inducer of immune proteasome activity, can serve as a potential therapeutic candidate for ischemic heart disease, exerting a preventive effect against cardiac machine ischemia or infarction, etc. Also, the proteasome can be used by tumors to degrade overproliferated and misfolded proteins to support their growth. [ 53 ] It has been demonstrated that the ability of the small molecule TCH-165 to activate the 20 S proteasome to remove disordered proteins in multiple myeloma and GBM is associated with disease progression in GBM. [ 19 ] C1q/TNF-related protein 1 is one of a newly discovered highly conserved adiponectin paralog protein family member, which inhibits platelet aggregation and limits the extent of I/R injury in acute MI by blocking the binding of vascular hemophilic factor to collagen. It also promotes atherosclerosis, and a surge in its concentration is associated with higher cardiovascular risk in patients with coronary atherosclerosis. [ 54 ] Moreover, it has been shown that increased levels of C1q/TNF-related protein 1 correlate with the progression of the tumor process in patients with GBM. [ 55 ] All of the above studies suggest some association between a variety of CVDs and GBM, but no study has yet analyzed and discussed the causal relationship between the 2. In this study, we found that CHD and MI, as 2 major cardiovascular diseases, are potential protective factors for GBM by MR analysis, which provides a new perspective on the research related to GBM.

This study confirms that cardiovascular disease may have a protective effect on GBM. Research has shown that there are other potential mechanisms besides adiponectin and proteasome mechanisms. Taking hypertension as an example, as an important risk factor for cardiovascular disease, it may have an inhibitory effect on the occurrence and development of GBM by regulating cerebral hemodynamics. [ 56 ] Previous studies have shown that patients with hypertension have more abundant blood circulation in the brain, which can help reduce the risk of cerebral edema. [ 57 ] And cerebral edema is a common pathological feature of GBM, and its alleviation may inhibit tumor growth. [ 58 ] In addition, the inflammatory response associated with cardiovascular disease may also play a role in this protective process. Although chronic inflammation is often seen as a factor that promotes tumor development, moderate inflammatory response can activate the immune system, enhance the recognition and clearance ability of immune cells towards tumor cells, and to some extent reduce the risk of GBM. [ 59 ] In the future, these mechanisms can be validated through various research methods.

This study is the 1st Mendelian analysis study to explore whether multiple CVDs are causally associated with GBM, confirming that 18 CVDs, including VTE, MI, and CHD, are protective factors against GBM, and providing a reference for GBM prevention and treatment. There are still some limitations to this study. Firstly, the sample size is relatively small. Secondly, the research population is limited to the European population, and the population specificity of genetic variations makes it difficult to generalize the research results to other races. Thirdly, the IVs lack biological validation, and the specificity of the selected SNPs and CVDs, as well as their pleiotropy on GBM risk, are unclear. The 4th issue is the lack of clinical, experimental validation, and epidemiological confirmation. Future research will expand the sample size and enrich the sample types, including people of different races and regions, conduct large-scale studies, and enhance the universality of the results, conduct in vitro and animal experiments to detect inflammatory factors and proteasome activity in the blood of cardiovascular disease patients, and explore molecular mechanisms through gene editing technology, conduct large-scale clinical studies, collect data from different populations, verify the causal relationship and potential molecular mechanisms between CVDs and GBM, and conduct epidemiological investigations to obtain clinical and experimental data, further confirming the accuracy and applicability of research conclusions. Through multidimensional and systematic follow-up research, it is expected to comprehensively and deeply reveal the relationship between cardiovascular disease and GBM, providing a more solid and reliable theoretical basis for the prevention and treatment strategies of related diseases.

5. Conclusion

GBM, as the most malignant glioma among astrocytic tumors, poses a serious risk to patients’ lives and health. However, there is poorly known about its risk factors. In this study, using MR analysis, we confirmed that 18 cardiovascular-related diseases and events had a significant causal relationship with the occurrence of GBM and were potential protective factors for GBM. This innovative discovery suggests that there may be shared genetic or metabolic pathways between the 2. Further exploration of these potential pathways will not only help clarify their underlying mechanisms, but also explore their clinical value in disease prevention, diagnosis, and treatment, providing key theoretical support and new research perspectives for optimizing the prevention and treatment strategies of GBM.

Acknowledgments

We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Tao Jiang, Yaotian Gao and Xiaoyun Tian. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. we would like to thank the foundation by the Project on the Inheritance and Innovation of Tranditional Chinese Medicine in Anhui Province for supporting our work. Without your support, this research would not have been possible.

Author contributions

Conceptualization: Keyi Lin, Tao Jiang.

Data curation: Keyi Lin, Yaotian Gao, Xiaoyun Tian, Tao Jiang.

Formal analysis: Yaotian Gao.

Funding acquisition: Tao Jiang.

Investigation: Keyi Lin, Yaotian Gao, Xiaoyun Tian.

Methodology: Keyi Lin, Yaotian Gao.

Project administration: Tao Jiang.

Resources: Tao Jiang.

Supervision: Tao Jiang.

Software: Keyi Lin, Yaotian Gao, Xiaoyun Tian, Tao Jiang.

Validation: Tao Jiang.

Visualization: Keyi Lin.

Writing – original draft: Keyi Lin.

Writing – review & editing: Keyi Lin, Tao Jiang.

Supplementary Material

Abbreviations: APOE apolipoprotein E CHD coronary heart disease CVDs cardiovascular diseases DVT deep vein thrombosis GBM glioblastoma GWAS genome-wide association study I/R ischemia/reperfusion IVs instrumental variables IVW inverse variance weighted MI myocardial infarction MR Mendelian randomization OR odds ratio PE pulmonary embolism SNPs single-nucleotide polymorphisms TSMR two-sample Mendelian randomization VTE venous thromboembolism

This research was funded by the Anhui Province Project for the Inheritance and Innovation of Traditional Chinese Medicine (2025CCCX014).

This study used publicly available data and did not require ethical approval.

The authors have no conflict of interest to disclose.

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

Supplemental Digital Content is available for this article.

How to cite this article: Lin K, Gao Y, Tian X, Jiang T. The causal relationship between multiple cardiovascular diseases and glioblastoma: A Mendelian randomization study. Medicine 2026;105:9(e47765).

Contributor Information

Keyi Lin, Email: 1033032641@qq.com.

Yaotian Gao, Email: gyt4296@163.com.

Xiaoyun Tian, Email: txyflea@163.com.

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