估计双向因果效应的方法,使用孟德尔随机化,并应用于体重指数和禁食葡萄糖
Jinhao Zou1, Rajesh Talluri1,2, Sanjay Shete1,3
1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
PloS one
|March 8, 2024
概括
新的门德尔随机化 (MR) 方法,BiRatio和BiLIML,准确估计表型之间的双向因果效应. 推采用BiLIML方法,特别是使用软弱的遗传仪器,并发现肥胖和糖尿病之间存在双向联系.
科学领域:
- 流行病学 流行病学
- 遗传流行病学遗传流行病学
- 生物统计学 生物统计学
背景情况:
- 门德尔随机化 (MR) 使用遗传变异作为因果推理的仪器变量 (IV).
- 单向MR (UMR) 方法很常见,但由于反循环,在估计双向关系时可能会产生偏差.
- 现有的UMR方法在两个方向上天真地应用,引入估计偏差.
研究的目的:
- 为准确的双向因果效应估计开发和评估新型MR方法.
- 通过模拟来比较新方法与原始UMR应用程序的性能.
- 使用拟议的方法,研究肥胖和2型糖尿病之间的双向关系.
主要方法:
- 提出了两种新的双向MR方法:BiRatio和BiLIML,扩展标准比率和有限信息最大概率 (LIML) 估计器.
- 进行了广泛的模拟,使用了不同数量的强弱遗传仪器 (IV).
- 应用BiLIML对多民族动脉样硬化研究 (MESA) 的数据,使用体重指数 (BMI) 和禁食葡萄糖 (FG).
主要成果:
- BiRatio和BiLIML提供了准确的双向因果效应估计,特别是多次强烈的IVs.
- 与BiRatio相比,BiLIML方法在使用微弱的IV时表现出更高的准确性.
- 在MESA队列中,在种族群体中发现了BMI和FG之间的显著双向因果关系.
结论:
- 在MR研究中,推使用BiLIML方法进行可靠的双向因果效应估计.
- 肥胖 (BMI) 和2型糖尿病 (FG) 呈现出双向的因果关系.
- 量化效应大小:BMI增加1kg/m2将FG提高0.70 mg/dL,FG增加1 mg/dL将BMI提高0.10 kg/m2.
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