Mendelian randomisation for mediation analysis: current methods and challenges for implementation

TLDR

Mediation analysis seeks to explain how an exposure influences an outcome, yet conventional non‑instrumental methods are plagued by confounding, measurement error, and other biases. This study proposes using Mendelian randomisation to strengthen causal inference in mediation analysis. The authors detail two MR‑based mediation strategies—multivariable MR and two‑step MR—providing code, illustrating implementation with simulated and real data, and discussing challenges such as weak instruments, pleiotropy, interactions, and multiple mediators, applicable to both individual‑level and summary‑data settings. Simulations demonstrate that, when strong instruments and no horizontal pleiotropy are present, these MR approaches are robust to confounding and non‑differential measurement error, and overall improve causal inference compared to traditional methods.

Abstract

Abstract Mediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.