Causal Relationship between Cerebrospinal Fluid Metabolites and Pancreatic Cancer: A Mendelian Randomization Study
Year: 2025
Type: article
Study design: Observational Study
Abstract: Introduction: Pancreatic cancer (PC), a highly malignant neoplasm characterized by low survival rates and increasing incidence, necessitates further exploration of its etiology and early biomarkers. The specific connection between the nervous system and PC remains undefined. This study employs Mendelian randomization (MR) using genome-wide association study (GWAS) data to clarify the causal relationship between cerebrospinal fluid (CSF) metabolites and PC risk. Methods: A two-sample MR analysis was conducted using GWAS summary data from the IEU database to assess causal relationships between CSF metabolites and PC, employing methods such as Inverse Variance Weighting (IVW), MR Egger, and the weighted median approach. Heterogeneity and horizontal pleiotropy were evaluated with Cochran's Q test and leave-one-out analysis. The validity of the instrumental variables was assessed through MR-PRESSO and Steiger filtering, while the unidirectional nature of the causality was investigated via reverse MR analysis. Results: Initially, twelve CSF metabolites were associated with PC, with seven potentially promoting and five reducing its risk. After Bonferroni correction, only succinimide levels remained significant (p = 5.058e−5, 95% CI OR = 2.1272 (1.4767−3.0644), FDR = 0.017), suggesting a potential increase in the risk of developing PC. Sensitivity analyses and reverse MR results confirmed the study's adherence to MR assumptions, with no evidence of reverse causality. Conclusion: There is a causal relationship between CSF metabolite succinimide levels and the development of PC. Elevated levels of succinimide may increase the risk of developing this type of cancer.
Source: Journal of Pancreatology
Authors: Cao Ruiqi, Feng Zhengyuan, Zhang Shengzhan, Wu Jiaoxing, Li Jie, Liu Haonan, Wang Fangzhou, Wang Zheng, Zhou Cancan, Zheng Wu
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410616171