Unraveling time-varying causal effects of multiple exposures: integrating Functional Data Analysis with Multivariable Mendelian Randomization

2025-12-22

AI Summary

Existing Mendelian randomization (MR) methods assume constant causal effects of exposures on outcomes, limiting their ability to characterize dynamic, life-course–varying causal relationships among multiple time-varying exposures and to account for mediation. To address this, we propose multivariate functional MR (MV-FMR), the first method enabling joint modeling of causal effects for multiple time-varying exposures. MV-FMR integrates functional principal component analysis with data-driven basis function selection, explicitly handling instrument variable overlap and mediation. It supports identification of time-varying causal functions under nonlinearity, sparsity, and horizontal pleiotropy. Simulation studies demonstrate robust recovery of true dynamic effects and substantial gains over univariate MR approaches. Applied to UK Biobank data, MV-FMR precisely identifies critical life-course windows during which blood pressure and BMI exert strongest causal effects on coronary heart disease—providing novel, temporally resolved evidence for staged disease prevention.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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.

Problem

• Estimates time-varying causal effects of multiple exposures on health outcomes.
• Addresses limitations of constant effect assumptions in existing multivariable Mendelian Randomization.
• Models dynamic relationships, including mediation and joint exposure influences.

Innovation

• Integrates functional data analysis with multivariable Mendelian randomization
• Models multiple time-varying exposures using functional principal components
• Accounts for overlapping instruments and mediation effects