Mendelian Randomization is a widely used instrumental variable method for assessing causal effects of lifelong exposures on health outcomes. Many exposures, however, have causal effects that vary across the life course and often influence outcomes jointly with other exposures or indirectly through mediating pathways. Existing approaches to multivariable Mendelian Randomization assume constant effects over time and therefore fail to capture these dynamic relationships. We introduce Multivariable Functional Mendelian Randomization (MV-FMR), a new framework that extends functional Mendelian Randomization to simultaneously model multiple time-varying exposures. The method combines functional principal component analysis with a data-driven cross-validation strategy for basis selection and accounts for overlapping instruments and mediation effects. Through extensive simulations, we assessed MV-FMR's ability to recover time-varying causal effects under a range of data-generating scenarios and compared the performance of joint versus separate exposure effect estimation strategies. Across scenarios involving nonlinear effects, horizontal pleiotropy, mediation, and sparse data, MV-FMR consistently recovered the true causal functions and outperformed univariable approaches. To demonstrate its practical value, we applied MV-FMR to UK Biobank data to investigate the time-varying causal effects of systolic blood pressure and body mass index on coronary artery disease. MV-FMR provides a flexible and interpretable framework for disentangling complex time-dependent causal processes and offers new opportunities for identifying life-course critical periods and actionable drivers relevant to disease prevention.

翻译：孟德尔随机化是一种广泛使用的工具变量方法，用于评估终生暴露对健康结局的因果效应。然而，许多暴露的因果效应在生命历程中会发生变化，且常与其他暴露共同影响结局，或通过中介路径间接产生影响。现有的多变量孟德尔随机化方法假设效应随时间恒定，因此无法捕捉这些动态关系。本文提出多变量功能孟德尔随机化（MV-FMR）这一新框架，将功能孟德尔随机化扩展至同时建模多个时变暴露。该方法结合功能主成分分析与数据驱动的交叉验证策略进行基函数选择，并考虑了工具变量重叠和中介效应。通过大量模拟研究，我们评估了MV-FMR在多种数据生成情境下恢复时变因果效应的能力，并比较了联合估计与单独估计暴露效应的策略性能。在涉及非线性效应、水平多效性、中介效应和稀疏数据的情境中，MV-FMR始终能恢复真实的因果函数，且优于单变量方法。为展示其实用价值，我们将MV-FMR应用于英国生物银行数据，研究收缩压和体重指数对冠状动脉疾病的时变因果效应。MV-FMR为解析复杂的时间依赖性因果过程提供了一个灵活且可解释的框架，并为识别生命历程关键期和疾病预防相关的可干预驱动因素提供了新的机遇。