Letter to the Editor
Copyright ©The Author(s) 2024.
World J Diabetes. Jun 15, 2024; 15(6): 1367-1373
Published online Jun 15, 2024. doi: 10.4239/wjd.v15.i6.1367
Table 1 The relationship between diabetes mellitus and prostate cancer
DM and PCa
Main point
Ref.
DM is a protective factor of PCaThis publication discusses the potential protective effects of diabetes against PCa, focusing on the role of diabetes-related microvascular complicationsZhang and Hu[18], 2010
This article reviews the epidemiological evidence regarding the reduced risk of PCa in diabetic individuals, offering insights into potential mechanismsPierce[19], 2012
This population-based case-control study examines the association between T2DM, antidiabetic medication, and the risk of PCaLin et al[20], 2020
DM is a high-risk factor for PCaThis approach improved the accuracy in estimating the relationship between diabetes and the incidence of PCaYuan et al[5], 2023
The study employed the Cox model and competing risks methods to estimate hazard ratios for various subtypes of PCaPiffoux 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 PCaSousa et al[22], 2022
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 analysisUnivariable MRUsing a single genetic variant as an instrumental variable to estimate the causal relationship between exposure and outcomeUtilizing specific SNPs associated with the development of DM to assess the risk of PCa in diabetic patients
Multivariable MRSimultaneously using multiple genetic variations as instrumental variables to consider the potential relationships among multiple exposuresUncovering potential common genetic paths between DM and PCa
Two-sample MRAllowing 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 biasIt can be used to evaluate whether DM increases the risk of PCa
Statistical efficiency analysisReliability analysisExamine the consistency of estimates and stability of different genetic instrumental variablesMR-Egger regression, the weighted median approach, and the leave-one-out cross-validation
Sensitivity analysisAssess the sensitivity of the results to potential confounding factors or violations of instrumental variable assumptions