Performance of approaches relying on multidimensional intermediary data to decipher causal relationships between the exposome and health: A simulation study under various causal structures

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• dc.contributor.author Cadiou, Solène
• dc.contributor.author Basagaña Flores, Xavier
• dc.contributor.author González, Juan Ramón
• dc.contributor.author Lepeule, Johanna
• dc.contributor.author Vrijheid, Martine
• dc.contributor.author Siroux, Valérie
• dc.contributor.author Slama, Rémy
• dc.date.accessioned 2022-05-10T05:52:56Z
• dc.date.available 2022-05-10T05:52:56Z
• dc.date.issued 2021
• dc.description.abstract Challenges in the assessment of the health effects of the exposome, defined as encompassing all environmental exposures from the prenatal period onwards, include a possibly high rate of false positive signals. It might be overcome using data dimension reduction techniques. Data from the biological layers lying between the exposome and its possible health consequences, such as the methylome, may help reducing exposome dimension. We aimed to quantify the performances of approaches relying on the incorporation of an intermediary biological layer to relate the exposome and health, and compare them with agnostic approaches ignoring the intermediary layer. We performed a Monte-Carlo simulation, in which we generated realistic exposome and intermediary layer data by sampling with replacement real data from the Helix exposome project. We generated a Gaussian outcome assuming linear relationships between the three data layers, in 2381 scenarios under five different causal structures, including mediation and reverse causality. We tested 3 agnostic methods considering only the exposome and the health outcome: ExWAS (for Exposome-Wide Association study), DSA, LASSO; and 3 methods relying on an intermediary layer: two implementations of our new oriented Meet-in-the-Middle (oMITM) design, using ExWAS and DSA, and a mediation analysis using ExWAS. Methods' performances were assessed through their sensitivity and FDP (False-Discovery Proportion). The oMITM-based methods generally had lower FDP than the other approaches, possibly at a cost in terms of sensitivity; FDP was in particular lower under a structure of reverse causality and in some mediation scenarios. The oMITM-DSA implementation showed better performances than oMITM-ExWAS, especially in terms of FDP. Among the agnostic approaches, DSA showed the highest performance. Integrating information from intermediary biological layers can help lowering FDP in studies of the exposome health effects; in particular, oMITM seems less sensitive to reverse causality than agnostic exposome-health association studies.
• dc.description.sponsorship The study has received funding from the European Commission Program H2020-EU.3.1.2 under grant agreement no 874583 – ATHLETE (Advancing Tools for Human Early Lifecourse Exposome Research and Translation). We also received support from Région Auvergne-Rhône-Alpes for collaborations with Catalunya.
• dc.format.mimetype application/pdf
• dc.identifier.citation Cadiou S, Basagaña X, Gonzalez JR, Lepeule J, Vrijheid M, Siroux V, Slama R. Performance of approaches relying on multidimensional intermediary data to decipher causal relationships between the exposome and health: A simulation study under various causal structures. Environ Int. 2021 Aug;153:106509. DOI: 10.1016/j.envint.2021.106509
• dc.identifier.doi http://dx.doi.org/10.1016/j.envint.2021.106509
• dc.identifier.issn 0160-4120
• dc.identifier.uri http://hdl.handle.net/10230/53032
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Environ Int. 2021 Aug;153:106509
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/874583
• dc.rights © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Exposome
• dc.subject.keyword Multilayer
• dc.subject.keyword Omics
• dc.subject.keyword Reverse causality
• dc.subject.keyword Sensitivity
• dc.subject.keyword Specificity
• dc.subject.keyword Variable selection
• dc.title Performance of approaches relying on multidimensional intermediary data to decipher causal relationships between the exposome and health: A simulation study under various causal structures
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion