Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Sanderson, Eleanor, Richardson, Tom G., Morris, Tim T., Tilling, Kate, Davey Smith, George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9348730/
https://www.ncbi.nlm.nih.gov/pubmed/35849575
http://dx.doi.org/10.1371/journal.pgen.1010290
_version_ 1784761978744471552
author Sanderson, Eleanor
Richardson, Tom G.
Morris, Tim T.
Tilling, Kate
Davey Smith, George
author_facet Sanderson, Eleanor
Richardson, Tom G.
Morris, Tim T.
Tilling, Kate
Davey Smith, George
author_sort Sanderson, Eleanor
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9348730
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-93487302022-08-04 Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization Sanderson, Eleanor Richardson, Tom G. Morris, Tim T. Tilling, Kate Davey Smith, George PLoS Genet Research Article 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. Public Library of Science 2022-07-18 /pmc/articles/PMC9348730/ /pubmed/35849575 http://dx.doi.org/10.1371/journal.pgen.1010290 Text en © 2022 Sanderson et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sanderson, Eleanor
Richardson, Tom G.
Morris, Tim T.
Tilling, Kate
Davey Smith, George
Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization
title Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization
title_full Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization
title_fullStr Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization
title_full_unstemmed Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization
title_short Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization
title_sort estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9348730/
https://www.ncbi.nlm.nih.gov/pubmed/35849575
http://dx.doi.org/10.1371/journal.pgen.1010290
work_keys_str_mv AT sandersoneleanor estimationofcausaleffectsofatimevaryingexposureatmultipletimepointsthroughmultivariablemendelianrandomization
AT richardsontomg estimationofcausaleffectsofatimevaryingexposureatmultipletimepointsthroughmultivariablemendelianrandomization
AT morristimt estimationofcausaleffectsofatimevaryingexposureatmultipletimepointsthroughmultivariablemendelianrandomization
AT tillingkate estimationofcausaleffectsofatimevaryingexposureatmultipletimepointsthroughmultivariablemendelianrandomization
AT daveysmithgeorge estimationofcausaleffectsofatimevaryingexposureatmultipletimepointsthroughmultivariablemendelianrandomization