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. 2022 Mar 11;33(5):631–652. doi: 10.1007/s10552-022-01562-1

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Multivariable MR for correlated nutritional factors. Multivariable MR uses multiple genetic instruments (Z₁, …, Z_n) associated with multiple, potentially correlated exposures (e.g., X₁, X₂, and X₃) to jointly estimate the independent causal effect of each of the exposures on a particular outcome (Y). It can also be used to explore mediation following two-step MR analyses to provide a better understanding of the direct, indirect and total effects of each exposure [64, 115]