Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research

Anguita Ruiz, Augusto; Amine, Ines; Stratakis, Nikos; Maitre, Léa; Júlvez Calvo, Jordi; Urquiza, José M.; Luo, Chongliang; Nieuwenhuijsen, Mark J.; Thomsen, Cathrine; Gražulevičienė, Regina; Heude, Barbara; McEachan, Rosemary R.C.; Vafeiadi, Marina, 1983-; Chatzi, Leda; Wright, John; Yang, Tiffany C.; Slama, Rémy; Siroux, Valérie; Vrijheid, Martine; Basagaña Flores, Xavier

Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research

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dc.contributor.author Anguita Ruiz, Augusto
dc.contributor.author Amine, Ines
dc.contributor.author Stratakis, Nikos
dc.contributor.author Maitre, Léa
dc.contributor.author Júlvez Calvo, Jordi
dc.contributor.author Urquiza, José M.
dc.contributor.author Luo, Chongliang
dc.contributor.author Nieuwenhuijsen, Mark J.
dc.contributor.author Thomsen, Cathrine
dc.contributor.author Gražulevičienė, Regina
dc.contributor.author Heude, Barbara
dc.contributor.author McEachan, Rosemary R.C.
dc.contributor.author Vafeiadi, Marina, 1983-
dc.contributor.author Chatzi, Leda
dc.contributor.author Wright, John
dc.contributor.author Yang, Tiffany C.
dc.contributor.author Slama, Rémy
dc.contributor.author Siroux, Valérie
dc.contributor.author Vrijheid, Martine
dc.contributor.author Basagaña Flores, Xavier
dc.date.accessioned 2024-06-14T06:42:53Z
dc.date.available 2024-06-14T06:42:53Z
dc.date.issued 2023
dc.description.abstract Outcome-wide analysis can offer several benefits, including increased power to detect weak signals and the ability to identify exposures with multiple effects on health, which may be good targets for preventive measures. Recently, advanced statistical multivariate techniques for outcome-wide analysis have been developed, but they have been rarely applied to exposome analysis. In this work, we provide an overview of a selection of methods that are well-suited for outcome-wide exposome analysis and are implemented in the R statistical software. Our work brings together six different methods presenting innovative solutions for typical problems arising from outcome-wide approaches in the context of the exposome, including dependencies among outcomes, high dimensionality, mixed-type outcomes, missing data records, and confounding effects. The identified methods can be grouped into four main categories: regularized multivariate regression techniques, multi-task learning approaches, dimensionality reduction approaches, and bayesian extensions of the multivariate regression framework. Here, we compare each technique presenting its main rationale, strengths, and limitations, and provide codes and guidelines for their application to exposome data. Additionally, we apply all selected methods to a real exposome dataset from the Human Early-Life Exposome (HELIX) project, demonstrating their suitability for exposome research. Although the choice of the best method will always depend on the challenges to be faced in each application, for an exposome-like analysis we find dimensionality reduction and bayesian methods such as reduced rank regression (RRR) or multivariate bayesian shrinkage priors (MBSP) particularly useful, given their ability to deal with critical issues such as collinearity, high-dimensionality, missing data or quantification of uncertainty.
dc.description.sponsorship ATHLETE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 874583. This publication reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains. We acknowledge support from the grant CEX2018-000806-S funded by MCIN/AEI/ 10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program. We also thank the support from the grant FJC2021-046952-I funded by MCIN/AEI/ 10.13039/501100011033 and, by “European Union NextGenerationEU/PRTR” and acknowledge funding from the Ministry of Research and Universities of the Government of Catalonia (2021-SGR-01563).
dc.format.mimetype application/pdf
dc.identifier.citation Anguita-Ruiz A, Amine I, Stratakis N, Maitre L, Julvez J, Urquiza J, et al. Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research. Environ Int. 2023 Dec;182:108344. DOI: 10.1016/j.envint.2023.108344
dc.identifier.doi http://dx.doi.org/10.1016/j.envint.2023.108344
dc.identifier.issn 0160-4120
dc.identifier.uri http://hdl.handle.net/10230/60468
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Environ Int. 2023 Dec;182:108344
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/874583
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/CEX2018-000806-S
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.keyword Environmental epidemiology
dc.subject.keyword Exposome analysis
dc.subject.keyword Multi-outcome analysis
dc.subject.keyword Outcome-wide analysis
dc.title Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research
dc.type info:eu-repo/semantics/article
dc.type.version info:eu-repo/semantics/publishedVersion

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