Association between cholelithiasis, cholecystectomy, and risk of breast and gynecological cancers: Evidence from meta-analysis and Mendelian randomization study
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引用次数: 0
Abstract
Background
Observational studies have shown that cholelithiasis and cholecystectomy are associated with the risk of breast cancer (BC) and gynecological cancers, but whether these relationships are causal has not been established and remains controversial.
Methods
Our study began with a meta-analysis that synthesized data from prior observational studies to examine the association between cholelithiasis, cholecystectomy, and the risk of BC and gynecological cancers. Subsequently, a two-sample Mendelian randomization (MR) analysis was conducted utilizing genetic variant data to investigate the potential causal relationship between cholelithiasis, cholecystectomy, and the aforementioned cancers.
Results
The results of the meta-analysis demonstrated a significant association between cholecystectomy and the risk of BC (risk ratio [RR] = 1.04, 95% confidence interval [CI]: 1.01–1.06, p = 0.002) and endometrial cancer (EC) (RR = 1.26, 95% CI: 1.02–1.56, p = 0.031). Conversely, no significant association was observed between cholelithiasis and the risk of BC, EC, and ovarian cancer. The MR analysis revealed no discernible causal connection between cholelithiasis and overall BC (p = 0.053), as well as BC subtypes (including estrogen receptor-positive/negative). Similarly, there was no causal effect of cholecystectomy on BC risk (p = 0.399) and its subtypes. Furthermore, no causal associations were identified between cholelithiasis, cholecystectomy, and the risk of gynecological cancers (ovarian, endometrial, and cervical cancer [CC]) (all p > 0.05).
Conclusion
This study does not support a causal link between cholelithiasis and cholecystectomy and an increased risk of female cancers such as breast, endometrial, ovarian, and CC.
期刊介绍:
Annals of Human Genetics publishes material directly concerned with human genetics or the application of scientific principles and techniques to any aspect of human inheritance. Papers that describe work on other species that may be relevant to human genetics will also be considered. Mathematical models should include examples of application to data where possible.
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