Diabetes Mellitus and Pancreatic Cancer: Investigation of Causal Pathways Through Mendelian Randomization Analysis

Abstract

Objective: This study was aimed at investigating the association between diabetes mellitus and susceptibility to pancreatic cancer by using Mendelian randomization (MR) methods and an extensive human genome-wide association study (GWAS) dataset.Methods: The publicly accessible MR Base database was used to obtain the complete genome, relevant research findings, and summary data pertaining to diabetes mellitus and pancreatic cancer. Genetic variables, specifically single-nucleotide polymorphisms closely associated with diabetes mellitus, were selected for analysis. Four methods—inverse variance weighted (IVW) analysis, weighted median analysis, weighted mode, and MR-Egger regression—were used. Statistical analysis was conducted to explore the potential association between diabetes mellitus and susceptibility to pancreatic cancer.Results: The results of the IVW analysis (OR = 11.56519319, 95% CI 1.275068624‐104.8992116, P = 0.0296) indicated a significant causal relationship between diabetes and elevated pancreatitis risk. Furthermore, the absence of horizontal pleiotropic effects (Egger intercept = 0.29, P = 0.384) and heterogeneity (P = 0.126) suggested that the observed association was not influenced by confounding factors. Sensitivity analysis and other statistical methods also supported the conclusion that genetic pleiotropy did not introduce bias to the findings.Conclusion: A causal relationship exists between diabetes mellitus and the occurrence of pancreatic cancer. People with diabetes mellitus are at high risk of pancreatic cancer and should receive early screening. The IGF signaling pathway may be a key mediator of the effects of diabetes on pancreatic cancer pathogenesis.

Sarcopenia-related Traits, Body Mass Index and Ovarian Cancer Risk: Investigation of Causal Relationships Through Multivariable Mendelian Randomization Analyses

“…Mendelian randomization (MR) is a widely accepted analytical method used to establish causality in observational studies, particularly in the presence of potential confounding and reverse causation [12,13]. The MR approach, using instrumental genetic variables, can be applied to elucidate the relationship between sarcopenia and ovarian cancer risk [14].…”