Man Alice, Knüsel Leona, Graf Josef, Lali Ricky, Le Ann, Di Scipio Matteo, Mohammadi-Shemirani Pedrum, Chong Michael, Pigeyre Marie, Kutalik Zoltán, Paré Guillaume
Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada.
Department of Medicine, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
Eur J Epidemiol. 2025 Mar 12. doi: 10.1007/s10654-025-01213-0.
Mendelian randomization (MR) is a technique which uses genetic data to uncover causal relationships between variables. With the growing availability of large-scale biobank data, there is increasing interest in elucidating nuances in these relationships using MR. Stratified MR techniques such as doubly-ranked MR (DRMR) and residual stratification MR have been developed to identify nonlinearity in causal relationships. These methods calculate causal estimates within strata of the exposure adjusted to mitigate the impact of collider bias. However, their application to scenarios using a stratifying variable other than the exposure to identify the presence of effect modifiers has been limited. The reliable identification of effect modifiers is key to identifying subgroups of patients differentially affected by risk and protective factors. In this study, we present a stratified MR algorithm capable of identifying effect modifiers of causal relationships using adapted forms of DRMR and residual stratification MR. Through simulations, the algorithm was found to be robust at handling nonlinear relationships and forms of collider bias, accommodating both binary and continuous outcomes. Application of the stratified MR algorithm to 1,715 exposure-stratifying variable-outcome combinations identified two Bonferroni significant effect modifiers of causal relationships in the UK Biobank. The causal effect of body mass index on type 2 diabetes mellitus was attenuated with age, while the effect of LDL cholesterol on coronary artery disease was exacerbated with increased serum urate. Overall, we introduce a tool for detecting effect modifiers of causal relationships, and present two cases with clinical implications for personalized risk assessment of cardiometabolic diseases.
孟德尔随机化（MR）是一种利用遗传数据揭示变量之间因果关系的技术。随着大规模生物样本库数据的日益可得，人们越来越有兴趣使用MR来阐明这些关系中的细微差别。已经开发了分层MR技术，如双排序MR（DRMR）和残差分层MR，以识别因果关系中的非线性。这些方法在经调整的暴露分层内计算因果估计值，以减轻对撞机偏差的影响。然而，它们在使用除暴露之外的分层变量来识别效应修饰因子存在情况的场景中的应用一直有限。可靠地识别效应修饰因子是识别受风险和保护因素不同影响的患者亚组的关键。在本研究中，我们提出了一种分层MR算法，该算法能够使用DRMR和残差分层MR的适配形式来识别因果关系的效应修饰因子。通过模拟发现，该算法在处理非线性关系和对撞机偏差形式方面具有稳健性，适用于二元和连续结局。将分层MR算法应用于1715个暴露 - 分层变量 - 结局组合，在英国生物样本库中确定了两个因果关系的Bonferroni显著效应修饰因子。体重指数对2型糖尿病的因果效应随年龄减弱，而低密度脂蛋白胆固醇对冠状动脉疾病的效应随血清尿酸升高而加剧。总体而言，我们介绍了一种检测因果关系效应修饰因子的工具，并展示了两个对心血管代谢疾病个性化风险评估具有临床意义的案例。
