BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization.
BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization.
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BMI是2型糖尿病的可修改风险因素：使用门德利随机化的炼油和理解因果估计。
DOI：
10.2337/db16-0418
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发表时间：
2016-10
期刊：
影响因子：
7.7
通讯作者：
Timpson NJ
中科院分区：
文献类型：
--
作者：
Corbin LJ;Richmond RC;Wade KH;Burgess S;Bowden J;Smith GD;Timpson NJ
本研究旨在探讨体重指数（BMI）与2型糖尿病的关系。与BMI相关的多种变异的可用性为解决BMI对T2D的真正因果影响提供了新的机会，但是这些关联的性质及其作为遗传工具的有效性需要与进行孟德尔随机化的既定和新方法一起考虑。我们探讨了多效性遗传变异产生偏倚的可能性，修订了现有的估计值，并说明了新分析方法的价值。使用三种不同的分析方法，其中两种（MR-Egger和加权中位数）已专门开发，以解决问题的无效工具变量的两个样本孟德尔随机化（MR）的方法与96个遗传变异。我们估计BMI（kg/m2）每增加一个单位，患2型糖尿病的比值比在1.19 - 1.38之间，使用所有工具和加权中位数方法得到的估计值最稳定（1.26 95%CI（1.17，1.34））。TCF7L2（rs7903146）被确定为复杂效应或多效性工具，删除该变体导致不同因果分析方法的因果效应估计值收敛。这表明多效性可能影响估计值和替代分析方法的性能差异。在一个真实的2型糖尿病为重点的例子中，这项研究证明了无效的工具对因果效应估计的潜在影响，以及新方法减轻所造成偏倚的潜力。
This study focused on resolving the relationship between body mass index (BMI) and type 2 diabetes. The availability of multiple variants associated with BMI offers a new chance to resolve the true causal effect of BMI on T2D, however the properties of these associations and their validity as genetic instruments need to be considered alongside established and new methods for undertaking Mendelian randomisation. We explore the potential for pleiotropic genetic variants to generate bias, revise existing estimates and illustrate value in new analysis methods. A two-sample Mendelian randomisation (MR) approach with 96 genetic variants was employed using three different analysis methods, two of which (MR-Egger and the weighted median) have been developed specifically to address problems of invalid instrumental variables. We estimate an odds ratio for type 2 diabetes per unit increase in BMI (kg/m2) of between 1.19 and 1.38, with the most stable estimate using all instruments and a weighted median approach (1.26 95%CI (1.17, 1.34)). TCF7L2(rs7903146) was identified as a complex effect or pleiotropic instrument and removal of this variant resulted in convergence of causal effect estimates from different causal analysis methods. This indicated the potential for pleiotropy to affect estimates and differences in performance of alternative analytical methods. In a real type 2 diabetes focused example, this study demonstrates the potential impact of invalid instruments on causal effect estimates and the potential for new approaches to mitigate the bias caused.