Unraveling the biological link between diabetes mellitus and prostate cancer: Insights and implications

Table 1 The relationship between diabetes mellitus and prostate cancer

DM and PCa	Main point	Ref.
DM is a protective factor of PCa	This publication discusses the potential protective effects of diabetes against PCa, focusing on the role of diabetes-related microvascular complications	Zhang and Hu[18], 2010
	This article reviews the epidemiological evidence regarding the reduced risk of PCa in diabetic individuals, offering insights into potential mechanisms	Pierce[19], 2012
	This population-based case-control study examines the association between T2DM, antidiabetic medication, and the risk of PCa	Lin et al[20], 2020
DM is a high-risk factor for PCa	This approach improved the accuracy in estimating the relationship between diabetes and the incidence of PCa	Yuan et al[5], 2023
	The study employed the Cox model and competing risks methods to estimate hazard ratios for various subtypes of PCa	Piffoux et al[21], 2021
	The study discusses the common risk factors for diabetes and how aspects frequently seen in diabetic patients, such as hyperglycemia and hyperinsulinemia, impact the risk of developing PCa	Sousa et al[22], 2022

T2DM: Type 2 diabetes mellitus; PCa: Prostate cancer.

Table 2 The commonly used mendelian randomization methods in the analysis of the relationship between diabetes mellitus and prostate cancer

Analytical method	Method name	Description	Application
Association analysis	Univariable MR	Using a single genetic variant as an instrumental variable to estimate the causal relationship between exposure and outcome	Utilizing specific SNPs associated with the development of DM to assess the risk of PCa in diabetic patients
	Multivariable MR	Simultaneously using multiple genetic variations as instrumental variables to consider the potential relationships among multiple exposures	Uncovering potential common genetic paths between DM and PCa
	Two-sample MR	Allowing data on exposure and outcome to come from different study populations can increase the sample size, improve statistical power, and reduce the impact of sample selection bias	It can be used to evaluate whether DM increases the risk of PCa
Statistical efficiency analysis	Reliability analysis	Examine the consistency of estimates and stability of different genetic instrumental variables	MR-Egger regression, the weighted median approach, and the leave-one-out cross-validation
	Sensitivity analysis	Assess the sensitivity of the results to potential confounding factors or violations of instrumental variable assumptions

DM: Diabetes mellitus; MR: Mendelian randomization; PCa: Prostate cancer; SNP: Single nucleotide polymorphism.