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
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