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
Published online Jun 15, 2024. doi: 10.4239/wjd.v15.i6.1367
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 |
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 |
- Citation: Li J, Li ZP, Xu SS, Wang W. Unraveling the biological link between diabetes mellitus and prostate cancer: Insights and implications. World J Diabetes 2024; 15(6): 1367-1373
- URL: https://www.wjgnet.com/1948-9358/full/v15/i6/1367.htm
- DOI: https://dx.doi.org/10.4239/wjd.v15.i6.1367