Jin Yi-Jing, Wu Xing-Yuan, An Zhuo-Yu
Peking University Health Science Center, 100191 Beijing, China.
Department of Cardiology, Peking University First Hospital, 100034 Beijing, China.
Rev Cardiovasc Med. 2024 Jul 11;25(7):262. doi: 10.31083/j.rcm2507262. eCollection 2024 Jul.
Cardiovascular disease (CVD), a leading cause of death and disability worldwide, and is associated with a wide range of risk factors, and genetically associated conditions. While many CVDs are preventable and early detection alongside treatment can significantly mitigate complication risks, current prediction models for CVDs need enhancements for better accuracy. Mendelian randomization (MR) offers a novel approach for estimating the causal relationship between exposure and outcome by using genetic variation in quasi-experimental data. This method minimizes the impact of confounding variables by leveraging the random allocation of genes during gamete formation, thereby facilitating the integration of new predictors into risk prediction models to refine the accuracy of prediction. In this review, we delve into the theory behind MR, as well as the strengths, applications, and limitations behind this emerging technology. A particular focus will be placed on MR application to CVD, and integration into CVD prediction frameworks. We conclude by discussing the inclusion of various populations and by offering insights into potential areas for future research and refinement.
心血管疾病（CVD）是全球死亡和残疾的主要原因，与多种风险因素及基因相关病症有关。虽然许多心血管疾病是可预防的，早期检测并配合治疗可显著降低并发症风险，但目前的心血管疾病预测模型仍需改进以提高准确性。孟德尔随机化（MR）提供了一种新方法，通过利用准实验数据中的基因变异来估计暴露与结果之间的因果关系。该方法通过利用配子形成过程中基因的随机分配，将混杂变量的影响降至最低，从而便于将新的预测因子整合到风险预测模型中，以提高预测的准确性。在这篇综述中，我们深入探讨了孟德尔随机化背后的理论，以及这项新兴技术的优势、应用和局限性。将特别关注孟德尔随机化在心血管疾病中的应用，以及将其整合到心血管疾病预测框架中。我们通过讨论纳入不同人群并对未来研究和改进的潜在领域提供见解来结束本文。
