Causal Associations Between Sleep Traits, Sleep Disorders, and Glioblastoma: A Two-Sample Bidirectional Mendelian Randomization Study

Neurology Neurosurgery Oncology Medicine
sleep traits glioblastoma mendelian randomization sleep disorders causal associations

Causal Associations Between Sleep Traits, Sleep Disorders, and Glioblastoma: A Two-Sample Bidirectional Mendelian Randomization Study

Academic Background Introduction

Glioblastoma (GBM) is the most aggressive and common type of malignant brain tumor, accounting for nearly 50% of all primary brain tumors in adults. It primarily originates from astrocytes in the brain or spinal cord and predominantly affects individuals over 40 years old. Despite advancements in treatment, the prognosis for GBM remains poor. Therefore, identifying modifiable risk factors is critical for understanding GBM development and potentially improving early detection and prevention.

Patients with GBM often experience neurological symptoms such as headaches, memory loss, confusion, and nausea, which significantly impair their quality of life. Additionally, sleep disturbances, particularly insomnia and circadian rhythm changes, are common in those affected by GBM, further reducing their quality of life. Studies have shown that circadian disruptions (such as those caused by shift work) may increase cancer risk by affecting cell cycle regulation, DNA repair, and immune function. Sleep deprivation and poor sleep quality have also been linked to increased risks of various cancers, including breast, colorectal, and prostate cancers. However, prior studies have yielded inconsistent results regarding the relationship between sleep traits, sleep disorders, and GBM risk. For example, a study on sleep duration did not find a significant association with glioma risk, while another study reported that patients with obstructive sleep apnea (OSA) had a higher risk of developing primary central nervous system cancers, including GBM. Given the lack of large-scale studies specifically exploring the causal relationship between sleep traits, sleep disorders, and GBM, conducting a bidirectional study to investigate whether these sleep variables influence GBM risk and vice versa is crucial.

Mendelian randomization (MR) is an epidemiological framework that uses genetic variants as instrumental variables (IVs) to examine the causal effect of exposures on outcomes. This method can reduce confounding factors and reverse causality commonly found in observational studies. Therefore, this study adopted a bidirectional MR approach to explore the causal associations between sleep traits (e.g., chronotype, ease of waking in the morning, midday napping, sleep duration, and sleep episodes) and sleep disorders (e.g., insomnia, narcolepsy, sleep apnea, and general sleep disorders) with GBM.

Source and Author Information

This study was conducted by Yuan Chen, Wenjun Yu, Yang Huang, Zijuan Jiang, Juan Deng, and Yujuan Qi from the Department of Neurosurgery at the Affiliated Brain Hospital of Nanjing Medical University. The paper was first published in the Journal of Neurophysiology on January 1, 2025, with DOI: 10.1152/jn.00338.2024.

Study Design and Data Sources

This study employed a bidirectional MR analysis. Data sources included two parts: GBM data were obtained from genome-wide association study (GWAS) data of the Finn Cohort, which included 91 cases, 174,006 controls, and 16,380,303 single nucleotide polymorphisms (SNPs). Sleep trait and disorder data were collected from the UK Biobank (UKB) and GWAS Catalog, with sample sizes ranging from 84,810 to 462,400. The study design adhered to the three fundamental assumptions of MR analysis: the IV must be strongly associated with the exposure, the IV should not affect the outcome through confounders, and the IV can only influence the outcome via the exposure.

Research Methods and Procedures

  1. Instrumental Variable Selection: SNPs were selected based on their significance levels (for sleep traits and disorders, p < 5×10^-8; for GBM, p < 5×10^-6) and minor allele frequency (MAF > 0.01). Further, SNPs in linkage disequilibrium (LD) were excluded to minimize confounding effects.

  2. MR Analysis: The primary analysis utilized the inverse-variance weighted (IVW) method, complemented by MR-Egger, weighted median (WM), and weighted mode methods for validation. All analyses were conducted using the “twosamplemr” package (R version 4.0.5).

  3. Sensitivity Analysis: Cochran’s Q test assessed heterogeneity, MR-Egger regression explored horizontal pleiotropy, and MR-PRESSO detected and corrected for potential outliers.

Key Research Findings

  1. Causal Effects of Sleep Traits and Sleep Disorders on GBM Risk: The study found that genetically predicted sleep duration was significantly negatively associated with GBM risk. Each standard deviation increase (1.1 hours/day) in sleep duration reduced the likelihood of GBM by 87% (OR = 0.13; 95% CI = 0.02–0.80; P = 0.027). No significant associations were found for other sleep traits or sleep disorders.

  2. Causal Effects of GBM on Sleep Traits and Sleep Disorders: The study revealed a significant positive association between GBM and chronotype, suggesting that patients with GBM are more likely to shift toward an evening chronotype (OR = 1.0094; 95% CI = 1.0034–1.0154; P = 0.002). No significant associations were observed between GBM and other sleep traits or sleep disorders.

Conclusions and Significance

This study systematically evaluated the causal links between sleep traits, sleep disorders, and GBM using a bidirectional MR approach. The findings indicate that shorter sleep duration may increase GBM risk, while GBM might promote a shift toward an evening chronotype. These results shed light on the complex interplay between sleep and GBM, providing new perspectives for future research into their correlations.

Highlights

  1. Key Discoveries: The study found a significant negative correlation between sleep duration and GBM risk, and GBM may influence circadian preference.
  2. Methodological Innovation: This study is the first to use a bidirectional MR method to systematically explore the causal relationships between sleep traits, sleep disorders, and GBM.
  3. Potential Applications: The results suggest that improving sleep quality and duration may help reduce GBM risk and provide new insights for managing sleep in GBM patients.

Conclusion

This study provides new evidence on the causal associations between sleep traits, sleep disorders, and GBM. Future research is needed to validate these findings and explore the underlying biological mechanisms. Through these studies, we hope to better understand GBM pathogenesis and improve patient quality of life by enhancing sleep management.