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

[Display omitted] •Outcome-wide analysis is a promising approach for the study of the exposome and its multiple effects on human health.•Advanced statistical multivariate techniques, well-suited for outcome-wide exposome analysis, have recently been developed.•An overview of methods for outcome-wide...

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Published in:Environment international Vol. 182; no. 3; p. 108344
Main Authors: Anguita-Ruiz, Augusto, Amine, Ines, Stratakis, Nikos, Maitre, Lea, Julvez, Jordi, Urquiza, Jose, Luo, Chongliang, Nieuwenhuijsen, Mark, Thomsen, Cathrine, Grazuleviciene, Regina, Heude, Barbara, McEachan, Rosemary, Vafeiadi, Marina, Chatzi, Leda, Wright, John, Yang, Tiffany C., Slama, Rémy, Siroux, Valérie, Vrijheid, Martine, Basagaña, Xavier
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.12.2023
Elsevier
Subjects:
Bayesian theory
computer software
data collection
environment
Environmental epidemiology
exposome
Exposome analysis
humans
Life Sciences
Multi-outcome analysis
Outcome-wide analysis
Santé publique et épidémiologie
Statistics
uncertainty
Environmental epidemiology
Exposome analysis
Multi-outcome analysis
Outcome-wide analysis
ISSN:0160-4120, 1873-6750, 1873-6750
Online Access:Get full text
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Summary:[Display omitted] •Outcome-wide analysis is a promising approach for the study of the exposome and its multiple effects on human health.•Advanced statistical multivariate techniques, well-suited for outcome-wide exposome analysis, have recently been developed.•An overview of methods for outcome-wide exposome analysis is provided along with guidelines and codes for their implementation. 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.
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ISSN:0160-4120
1873-6750
1873-6750
DOI:10.1016/j.envint.2023.108344