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卵巢癌孟德尔随机化研究综述

Review of Mendelian Randomization Studies on Ovarian Cancer.

作者信息

Guo Jian-Zeng, Xiao Qian, Gao Song, Li Xiu-Qin, Wu Qi-Jun, Gong Ting-Ting

机构信息

Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.

College of Life and Health Sciences, Northeastern University, Shenyang, China.

出版信息

Front Oncol. 2021 Aug 11;11:681396. doi: 10.3389/fonc.2021.681396. eCollection 2021.

Abstract

Ovarian cancer (OC) is one of the deadliest gynecological cancers worldwide. Previous observational epidemiological studies have revealed associations between modifiable environmental risk factors and OC risk. However, these studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) analysis has been established as a reliable method to investigate the causal relationship between risk factors and diseases using genetic variants to proxy modifiable exposures. Over recent years, MR analysis in OC research has received extensive attention, providing valuable insights into the etiology of OC as well as holding promise for identifying potential therapeutic interventions. This review provides a comprehensive overview of the key principles and assumptions of MR analysis. Published MR studies focusing on the causality between different risk factors and OC risk are summarized, along with comprehensive analysis of the method and its future applications. The results of MR studies on OC showed that higher BMI and height, earlier age at menarche, endometriosis, schizophrenia, and higher circulating β-carotene and circulating zinc levels are associated with an increased risk of OC. In contrast, polycystic ovary syndrome; vitiligo; higher circulating vitamin D, magnesium, and testosterone levels; and HMG-CoA reductase inhibition are associated with a reduced risk of OC. MR analysis presents a2 valuable approach to understanding the causality between different risk factors and OC after full consideration of its inherent assumptions and limitations.

摘要

卵巢癌(OC)是全球最致命的妇科癌症之一。以往的观察性流行病学研究已经揭示了可改变的环境风险因素与OC风险之间的关联。然而,这些研究容易受到混杂因素、测量误差和反向因果关系的影响,从而削弱了可靠的因果推断。孟德尔随机化(MR)分析已被确立为一种可靠的方法,用于利用基因变异来替代可改变的暴露因素,研究风险因素与疾病之间的因果关系。近年来,OC研究中的MR分析受到了广泛关注,为OC的病因学提供了有价值的见解,并有望识别潜在的治疗干预措施。本综述全面概述了MR分析的关键原则和假设。总结了已发表的关注不同风险因素与OC风险之间因果关系的MR研究,并对该方法及其未来应用进行了综合分析。关于OC的MR研究结果表明,较高的体重指数和身高、初潮年龄较早、子宫内膜异位症、精神分裂症以及较高的循环β-胡萝卜素和循环锌水平与OC风险增加相关。相比之下,多囊卵巢综合征;白癜风;较高的循环维生素D、镁和睾酮水平;以及HMG-CoA还原酶抑制与OC风险降低相关。在充分考虑其固有假设和局限性后,MR分析为理解不同风险因素与OC之间的因果关系提供了一种有价值的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca96/8385140/617b15cb211c/fonc-11-681396-g001.jpg

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