Related Experiment Video
Updated: Jun 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
No causal association between insomnia and bladder cancer: a bidirectional two-sample Mendelian randomization study
Lihuan Du1, Bohan Wang2, Jiaming Wen2
1Department of Urology, The Second Affiliated Hospital of Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, China. dlh1984@zju.edu.cn.
Background:
Previous observational studies have indicated a potential link between insomnia and bladder cancer, yet the underlying causal relationship remains uncertain. The current study employed a bidirectional two-sample Mendelian randomization (MR) analysis to investigate this association.
Methods:
A two-sample MR analysis was conducted utilizing publicly available summary data from genome-wide association studies (GWAS) on insomnia and bladder cancer. Various regression methods including the inverse variance weighted (IVW), weighted median, MR-Egger, weighted mode, and simple mode methods were employed for the MR analysis. The presence of pleiotropy and heterogeneity in the MR results was also assessed. Furthermore, additional sensitivity tests were performed to mitigate potential biases.
Results:
No significant causal relationship was detected between insomnia and bladder cancer using IVW method (OR = 0.761, 95% CI 0.996-1.005; P = 0.76). Similarly, the IVW model did not reveal any causal effect of bladder cancer on the risk of insomnia (OR = 1.47, 95% CI 0.772-2.799; P = 0.24). Consistent results were obtained from the other four methods employed. There was no evidence of horizontal pleiotropy or heterogeneity in our MR analysis (P > 0.05). The sensitivity analyses further supported the reliability of the estimated causal effects.
Conclusions:
This study presents no evidence for a causal relationship between insomnia and bladder cancer.
Related Concept Videos
Blind Procedures
Randomized Experiments
Simple randomization
Simple...
Blinding
Cause and Effect

