医学
孟德尔随机化
体质指数
因果推理
观察研究
全基因组关联研究
人口学
内科学
单核苷酸多态性
病理
遗传学
生物
基因
基因型
社会学
遗传变异
作者
Qinggang Liu,Linna Wang,Limin Liu,Huiling Cong,Yi Gao
摘要
Abstract Purpose To explore the potential causal links between obesity, type 2 diabetes (T2D), and lifestyle choices (such as smoking, alcohol and coffee consumption, and vigorous physical activity) on stress urinary incontinence (SUI), this study employs a Mendelian Randomization approach. This research aims to clarify these associations, which have been suggested but not conclusively established in prior observational studies. Methods Genetic instruments associated with the exposures at the genome‐wide significance ( p < 5 × 10 −8 ) were selected from corresponding genome‐wide association studies. Summary‐level data for SUI, was obtained from the UK Biobank. A two‐sample MR analysis was employed to estimate causal effects, utilizing the inverse‐variance weighted (IVW) method as the primary analytical approach. Complementary sensitivity analyses including MR‐PRESSO, MR‐Egger, and weighted median methods were performed. The horizontal pleiotropy was detected by using MR‐Egger intercept and MR‐PRESSO methods, and the heterogeneity was assessed using Cochran's Q statistics. Results Our findings demonstrate a significant causal relationship between higher body mass index (BMI) and the risk of SUI, with increased abdominal adiposity (WHRadjBMI) similarly linked to SUI. Smoking initiation is also causally associated with an elevated risk. However, our analysis did not find definitive causal connections for other factors, including T2D, alcohol consumption, coffee intake, and vigorous physical activity. Conclusions These findings provide valuable insights for clinical strategies targeting SUI, suggesting a need for heightened awareness and potential intervention in individuals with higher BMI, WHR, and smoking habits. Further research is warranted to explore the complex interplay between genetic predisposition and lifestyle choices in the pathogenesis of SUI.
科研通智能强力驱动
Strongly Powered by AbleSci AI