Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization

• Published in: PLoS genetics 2022-07, Vol.18 (7), p.e1010290
• Main Authors: Sanderson, Eleanor, Richardson, Tom G, Morris, Tim T, Tilling, Kate, Davey Smith, George
• Format: Article
• Language: English
• Subjects: Biology and Life Sciences; C-reactive protein; Causality; Children; Dose-response relationship (Biochemistry); Engineering and Technology; Epidemiology; Estimates; Ethics; Genetic aspects; Genetic diversity; Genetic variance; Genetic Variation; Health aspects; Health risk assessment; Liability; Mendel's law; Mendelian Randomization Analysis - methods; Multiple sclerosis; Observational studies; Physical Sciences; Social Sciences
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1010290

Mendelian Randomisation (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effect estimates obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of some exposures are thought to vary throughout an individual's lifetime with periods during which an exposure has a greater effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual's lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies. However, this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. Prior knowledge regarding the biological basis of exposure trajectories can help interpretation. We illustrate the method through estimation of the causal effects of childhood and adult BMI on C-Reactive protein and smoking behaviour.