State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

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• dc.contributor.author Maitre, Léa
• dc.contributor.author Guimbaud, Jean-Baptiste
• dc.contributor.author Warembourg, Charline
• dc.contributor.author Güil Oumrait, Núria
• dc.contributor.author Petrone, Paula M.
• dc.contributor.author Chadeau-Hyam, Marc
• dc.contributor.author Vrijheid, Martine
• dc.contributor.author Basagaña Flores, Xavier
• dc.contributor.author González, Juan Ramón
• dc.contributor.author Exposome Data Challenge Participant Consortium
• dc.date.accessioned 2022-11-24T07:01:25Z
• dc.date.available 2022-11-24T07:01:25Z
• dc.date.issued 2022
• dc.description.abstract The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother-child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field.
• dc.description.sponsorship The data for the challenge were issued from a study from the European Community’s Seventh Framework Programme (FP7/2007-206) under grant agreement no 308333 (HELIX project) and the H2020-EU.3.1.2. - Preventing Disease Programme under grant agreement no 874583 (ATHLETE project). LMaitre is funded by a Juan de la Cierva-Incorporación fellowship (IJC2018-035394-I) awarded by the Spanish Ministerio de Economía, Industria y Competitividad. ISGlobal and the Exposome hub acknowledges support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. JBGuimbaud was supported by a CIFRE PhD fellowship (#2020/1297) from Meersens. MYu was supported by the grant P30ES023515 (National Institute of Environmental Health Sciences, US).
• dc.format.mimetype application/pdf
• dc.identifier.citation Maitre L, Guimbaud JB, Warembourg C, Güil-Oumrait N, Petrone PM, Chadeau-Hyam M, Vrijheid M, Basagaña X, Gonzalez JR; Exposome Data Challenge Participant Consortium. State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event. Environ Int. 2022 Aug 27;168:107422. DOI: 10.1016/j.envint.2022.107422
• dc.identifier.doi http://dx.doi.org/10.1016/j.envint.2022.107422
• dc.identifier.issn 0160-4120
• dc.identifier.uri http://hdl.handle.net/10230/54992
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Environ Int. 2022 Aug 27;168:107422
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/308333
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/874583
• dc.rights © 2022 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 Environmental exposures
• dc.subject.keyword Exposome
• dc.subject.keyword Multi-omics
• dc.subject.keyword Multiple exposures
• dc.subject.keyword Statistical models
• dc.title State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion