Malignant tumors of the central nervous system (CNS) usually have the poorest prognosis among all cancers, leading to the highest estimated life lost when compared with any other cancers (1). Gliomas are the most common CNS tumors (2), accounting for about 80% of all malignant tumors and 30% of all primary brain ones. Besides, gliomas have the highest death rate of all deaths caused by primary brain tumors (3, 4). Several possibly adjustable factors for gliomas have been revealed in some epidemiological studies, including adiposity (5, 6), alcohol consumption (7), smoking (8, 9), diabetes (10, 11), and coffee intake (12). The role of insulin resistance, inflammation, and altered hormone levels may take part in the potential mechanisms, underlying the association between adiposity, diabetes, lifestyle factors, and glioma risk (5–12).

Previous research on the association between the abovementioned factors and the risks of gliomas has been inconsistent, with some studies reporting significant associations while others have found no association (8, 13). The inconsistency could be caused by a variety of factors. But we believe that the most significant one is that all of these studies are observational ones. Therefore, the observational conclusions could be influenced by some unknown confounders, misclassification, and even reverse causality (14). Determining the causal effect of potentially modifiable risk factors with gliomas is very important for us to understand the etiology of this disease, and also to help us prevent and manage the disease in clinical practice.

Mendelian randomization (MR) design is a powerful tool to extract genetic variants from previously published genome-wide association studies (GWASs) as instrumental variables (IVs) to determine the causal inference between exposures and outcomes (15). The MR study can reduce the residual confounding because the genetic IVs were randomly assorted at conception, which was finished before the onset of the diseases or other environmental factors (16). In addition, the MR study is a useful tool to minimize reverse causality since the germline variants are used in the MR analysis and the genetic IVs will not change during the development or progression of the disease (17).

In this study, a two-sample MR study was performed to examine the causal associations between adiposity, diabetes, smoking and alcohol, and coffee intake and the risk of gliomas.

This is the first study to explore the causal associations between genetically predicted BMI, WC, T2D, smoking, alcohol drinking, coffee intake, and the risk of gliomas using MR design. And our results showed that there were no causal roles of adiposity, diabetes, lifestyle factors, and the risk of gliomas.

Although many observational studies have explored the associations between the specific diet or certain other lifestyle choices and the risk of the development of glioma (3), little consensus has been achieved since the inconsistency of these conclusions in different studies.

Many studies have been designed to evaluate the potential association between adiposity and the risk of glioma (including general and abdominal adiposity). A nationwide population-based cohort study of Koreans enrolled 6,833,744 people older than 20 years and 4,771 glioma cases were documented during the median follow-up period of 7.30 years (5), which revealed that individuals with higher BMI and WC had a higher risk of glioma. Besides, a meta-analysis enrolled 22 studies (including 2,418 glioma cases in a total cohort size of 10,143,803 subjects) and 8 case-control studies (1,265 glioma cases and 8,316 controls) (6). This study found that adiposity (measured by BMI) was a risk factor for gliomas among females and no such associations in males (6). The authors concluded that controlling adiposity may help reduce the risk of developing glioma (5, 6). Observational studies on the associations between lifestyle factors and glioma risk have yielded inconsistent results, which can be attributed to several factors. Firstly, observational studies are prone to bias, including selection bias, recall bias, and confounding. Selection bias occurs when the study population is not representative of the general population, leading to biased estimates of the association between the exposure and outcome variables. Recall bias occurs when participants have difficulty recalling past exposures accurately, leading to misclassification of exposure status. Confounding occurs when there are other factors that are associated with both the exposure and outcome variables, leading to spurious associations between the two variables. Secondly, observational studies often have small sample sizes, which may limit their statistical power to detect a true association between the exposure and outcome variables. This can lead to inconsistent results across studies. Thirdly, observational studies often rely on self-reported data, which may be subject to social desirability bias. Participants may provide responses that they believe are socially acceptable, rather than their true behaviors or exposures.

MR analysis can help address some of these limitations of observational studies. By using genetic variants as instrumental variables, MR analysis can minimize the impact of confounding and reverse causation, as genetic variants are randomly assigned at conception and are not affected by environmental or lifestyle factors. Our MR revealed that the genetically predicted BMI and WC were not causally associated with a higher risk of gliomas.

Glioma, a rare brain tumor originating from glial cells, affects a small proportion of the population. It is a relatively rare condition, accounting for only about 1% of all cancers and affecting approximately 5 in 100,000 people per year (10). In contrast, diabetes, a metabolic disease, is a known risk factor for many types of malignant tumors (23, 24), including gliomas (10), affecting around 463 million adults worldwide (23, 24). Interestingly, recent research has revealed a potential association between glioma and diabetes. Several studies have investigated the association between diabetes and glioma risk, and some have also examined the relationship between diabetes and glioma survival (25). However, the findings from these studies are contradictory. While some studies suggest that long-term diabetes may reduce the risk of glioma (26), others found no association between diabetes and glioma survival (27). In our MR analysis, we found no causal relationship between genetically predicted diabetes and the development of gliomas. However, the causal relationship between diabetes and glioma survival was not assessed in our study.

Except for adiposity and diabetes, other potential lifestyle factors (smoking, alcohol drinking, and coffee intake) have also been investigated, but no consistent associations were found in the previous studies (3, 4, 9, 13). In the present MR analysis, we also found no causal associations between lifestyle factors and the risk of gliomas. With regard to smoking, the gender-specific difference also existed. Li et al. (8) revealed an increased risk of gliomas in past female smokers but not in males. However, it is a pity that we cannot reconfirm such gender-specific differences in our MR analysis.

GLIPR1 is a tumor suppressor protein that has been implicated in the development and progression of glioma. Studies have shown that GLIPR1 expression is reduced in glioma cells compared to normal glial cells, and that restoration of GLIPR1 expression can inhibit glioma cell growth and induce apoptosis (28). Additionally, GLIPR1 has been shown to regulate various signaling pathways involved in glioma development and progression, including the PI3K/Akt and MAPK/ERK pathways (28). Therefore, GLIPR1 was considered as a promising target for the development of new therapies for glioma treatment. In the present study, the levels of GLIPR1was used to validate the causal effect of adiposity, diabetes, and lifestyle factors on the risk of gliomas. Similar to the findings in the main analysis, no causal association was observed, which further confirmed that there was no causal relationship between them.

In addition, glioma is characterized by numerous tumor-driver genes and distinct pathological subtypes. Previous cancer genomics studies, such as the GliomaScan study, have revealed compelling evidence supporting the genetic etiology of gliomas, including loci such as 20q13.33 (RTEL), 5p15.33 (TERT), and 9p21.3 (CDKN2BAS), as well as both loci of 7p11.2 (EGFR), 8q24.21 (CCDC26), and 11q23.3 (PHLDB1) (29). Nevertheless, we conducted a thorough search for all included SNPs in the exposures to ensure that none of them was significantly associated with glioma. This crucial assumption was necessary to ensure the reliability and robustness of our findings in the MR analysis. As a result, our study provides reliable and robust results, even in the radial MR analysis.

There are several strengths in the present MR analysis: first, only independent SNPs significantly associated with the exposures (including adiposity, diabetes, and lifestyle factors) were used in our MR analysis to satisfy the basic assumptions of MR. Besides, all the GWASs of exposures and outcomes were finished just in European ancestry populations to reduce the potential confounder. Most importantly, MR Egger regression was performed and no evidence of directional pleiotropic effects was found. Therefore, the results of the present MR are valid and robust.

Several potential limitations existed in the present MR. First, only summary-level statistics rather than individual-level data were used in this MR. Therefore, we cannot further explore the gender-specific association between adiposity, diabetes, lifestyle factors, and the risk of gliomas. Besides, this study was finished based on the data of European ancestry populations. Whether these associations existed in other ancestry populations needs further investigation due to the genetic variance in different ancestry populations (30). The potential for bias may also limit the generalizability of our findings to other populations. Future research should be conducted to address these limitations. We also tried to replicate our analysis in the Chinese Glioma Genome Atlas (CGGA) dataset (31). However, the CGGA dataset only contains whole-exome sequencing data in 286 patients and the power is too low to provide a convincing conclusion. Therefore, if more cases finished the whole-exome sequencing in the CGGA database, it may provide us with more evidence by replicating the MR analysis in this database. In the validation of the causal associations with the levels of glioma pathogenesis-related protein 1, causality was still not observed, which was consistent with the findings in glioma.

Our results suggest that there are no causal effects of adiposity, T2D, smoking, alcohol drinking, and coffee intake on the development of gliomas. Therefore, strategies aimed at reducing the risk of gliomas through the modification of these factors may not be effective, and alternative approaches should be explored.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the (patients/ participants OR patients/participants legal guardian/next of kin) was not required to participate in this study in accordance with the national legislation and the institutional requirements.

LW designed the study and revised the manuscript. XL and YW finished the analysis. YW and JZ helped with the use of the software. WS, YZ, and LC finished the draft of this manuscript. All authors contributed to the article and approved the submitted version.

This study was supported by grant from Tianjin Science and Technology Plan Project (Grant No. 22ZYQYSY00030), Tianjin Natural Science Foundation (Grant No. 21JCZDJC01270), Tianjin Health Technology Project (Grant No. TJWJ2022XK043), Bethune Charity Foundation Project (Grant No. B-0307-H20200302), and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-062B).

We acknowledged Union_of_Researchers (WeChat Subscription) for his help with the methods of our analysis.

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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