Testing for causality between systematically identified risk factors and glioma: a Mendelian randomization study

Abstract

Background: Whilst epidemiological studies have provided evidence of associations between certain risk factors and glioma onset, inferring causality has proven challenging. Using Mendelian randomization (MR), we assessed whether associations of 36 reported glioma risk factors showed evidence of a causal relationship. Methods: We performed a systematic search of MEDLINE from inception to October 2018 to identify candidate risk factors and conducted a meta-analysis of two glioma genome-wide association studies (5739 cases and 5501 controls) to form our exposure and outcome datasets. MR analyses were performed using genetic variants to proxy for candidate risk factors. We investigated whether risk factors differed by subtype diagnosis (either glioblastoma (n = 3112) or non-glioblastoma (n = 2411)). MR estimates for each risk factor were determined using multiplicative random effects inverse-variance weighting (IVW). Sensitivity analyses investigated potential pleiotropy using MR-Egger regression, the weighted median estimator, and the mode-based estimator. To increase power, trait-specific polygenic risk scores were used to test the association of a genetically predicated increase in each risk factor with glioma onset. Results: Our systematic search identified 36 risk factors that could be proxied using genetic variants. Using MR, we found evidence that four genetically predicted traits increased risk of glioma, glioblastoma or non-glioblastoma: longer leukocyte telomere length, liability to allergic disease, increased alcohol consumption and liability to childhood extreme obesity (> 3 standard deviations from the mean). Two traits decreased risk of non-glioblastoma cancers: increased low-density lipoprotein cholesterol (LDLc) and triglyceride levels. Our findings were similar across sensitivity analyses that made allowance for pleiotropy (genetic confounding). Conclusions: Our comprehensive investigation provides evidence of a causal link between both genetically predicted leukocyte telomere length, allergic disease, alcohol consumption, childhood extreme obesity, and LDLc and triglyceride levels, and glioma. The findings from our study warrant further research to uncover mechanisms that implicate these traits in glioma onset.

Evaluation of the association of ERCC2 rs13181 polymorphism with different types of gliomas in patients in Northeast Brazil

“…There was no difference between family history of cancer and gliomas, perhaps because this association is more common in certain types of syndromes, such as Li Fraumeni, Turcort, neuro bromatosis, among others [17], which did not occur in this study. There was also no association between gliomas and smoking or alcohol consumption; however, some authors reported a higher risk for high-grade gliomas in cases of excessive smoking [18] and alcohol use [19,20]. High-grade gliomas occur in older patients [14][15][16], corroborating the present study results, as cellular aging increases immunological senescence, telomere shortening, chronic in ammation with antigenic stimulation, genomic instability, and mutations forming more aggressive neoplasms such as high-grade gliomas [17].…”

The Role of Network Science in Glioblastoma

“…In another branch of causal discovery, Mendelian randomization (MR) uses genetic variants, such as single-nucleotide polymorphisms (SNPs), that are robustly associated with an exposure as proxies for the risk factor of interest [ 154 ], therefore eliminating confounding and reverse causation effects between the exposure of interest and outcome. Howell et al (2020) [ 155 ] used MR to assess the causal relationship of associations of 36 reported glioma risk factors obtained from a systematic MEDLINE search on observational epidemiology studies. A meta-analysis of two glioma genome-wide association studies (GWASs) was then performed, and a two-sample MR analysis using the GWAS exposure and outcome datasets investigated the causal relationships between the risk factors identified and glioma incidence.…”

The COX10-AS1/miR-641/E2F6 Feedback Loop Is Involved in the Progression of Glioma

“…Glioblastoma multiforme (GBM) is the most lethal type of glioma according to the grade of malignancy, with an average survival time of 12 to 14 months and a five-year survival rate of only 4% to 5% ( 3 , 4 ). The mortality rate of glioma has been stable at 4-5/100,000, ranking among the top 10 in tumour mortality ( 5 ). At present, the treatment of glioma is mainly by surgery, supplemented by radiotherapy and chemotherapy.…”