Deutsches Herzzentrum München and Technische Universität München, Munich, Germany DZHK (German Research Centre for Cardiovascular Research), Munich Heart Alliance, Munich, Germany.
Department of Cardiovascular Sciences, University of Leicester, Leicester, UK National Institute for Health Research Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK.
Eur Heart J. 2014 Aug 1;35(29):1917-24. doi: 10.1093/eurheartj/ehu208. Epub 2014 Jun 10.
Epidemiological research over the last 50 years has discovered a plethora of biomarkers (including molecules, traits or other diseases) that associate with coronary artery disease (CAD) risk. Even the strongest association detected in such observational research precludes drawing conclusions about the causality underlying the relationship between biomarker and disease. Mendelian randomization (MR) studies can shed light on the causality of associations, i.e whether, on the one hand, the biomarker contributes to the development of disease or, on the other hand, the observed association is confounded by unrecognized exogenous factors or due to reverse causation, i.e. due to the fact that prevalent disease affects the level of the biomarker. However, conclusions from a MR study are based on a number of important assumptions. A prerequisite for such studies is that the genetic variant employed affects significantly the biomarker under investigation but has no effect on other phenotypes that might confound the association between the biomarker and disease. If this biomarker is a true causal risk factor for CAD, genotypes of the variant should associate with CAD risk in the direction predicted by the association of the biomarker with CAD. Given a random distribution of exogenous factors in individuals carrying respective genotypes, groups represented by the genotypes are highly similar except for the biomarker of interest. Thus, the genetic variant converts into an unconfounded surrogate of the respective biomarker. This scenario is nicely exemplified for LDL cholesterol. Almost every genotype found to increase LDL cholesterol level by a sufficient amount has also been found to increase CAD risk. Pending a number of conditions that needed to be fulfilled by the genetic variant under investigation (e.g. no pleiotropic effects) and the experimental set-up of the study, LDL cholesterol can be assumed to act as the functional component that links genotypes and CAD risk and, more importantly, it can be assumed that any modulation of LDL cholesterol-by whatever mechanism-would have similar effects on disease risk. Therefore, MR analysis has tremendous potential for identifying therapeutic targets that are likely to be causal for CAD. This review article discusses the opportunities and challenges of MR studies for CAD, highlighting several examples that involved multiple biomarkers, including various lipid and inflammation traits as well as hypertension, diabetes mellitus, and obesity.
在过去的 50 年中，流行病学研究发现了大量与冠心病（CAD）风险相关的生物标志物（包括分子、特征或其他疾病）。即使在这种观察性研究中检测到的最强关联，也不能得出关于生物标志物与疾病之间关系的因果关系的结论。孟德尔随机化（MR）研究可以揭示关联的因果关系，即一方面，生物标志物是否有助于疾病的发展，或者另一方面，观察到的关联是否受到未被识别的外源性因素的干扰，或者是由于反向因果关系，即由于现患疾病影响了生物标志物的水平。然而，MR 研究的结论基于许多重要的假设。此类研究的前提是，所使用的遗传变异显著影响所研究的生物标志物，但对可能干扰生物标志物与疾病之间关联的其他表型没有影响。如果该生物标志物是 CAD 的真正因果风险因素，那么该变异的基因型应该按照生物标志物与 CAD 关联的方向与 CAD 风险相关联。鉴于个体携带相应基因型的外源性因素的随机分布，除了感兴趣的生物标志物外，代表各基因型的组非常相似。因此，遗传变异转化为相应生物标志物的无混杂替代物。这种情况在 LDL 胆固醇中得到了很好的例证。几乎所有被发现足以增加 LDL 胆固醇水平的基因型也被发现会增加 CAD 风险。在研究中的遗传变异必须满足一些条件（例如没有多效性效应）和研究的实验设计下，可以假设 LDL 胆固醇作为连接基因型和 CAD 风险的功能性成分，更重要的是，可以假设任何通过任何机制调节 LDL 胆固醇都会对疾病风险产生类似的影响。因此，MR 分析对于确定可能对 CAD 具有因果关系的治疗靶点具有巨大的潜力。本文讨论了 MR 研究在 CAD 中的机遇和挑战，强调了涉及多个生物标志物的几个例子，包括各种脂质和炎症特征以及高血压、糖尿病和肥胖症。
