Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research
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 analysi...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Environment International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412023006177 |
| _version_ | 1797401783029465088 |
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| author | Augusto Anguita-Ruiz Ines Amine Nikos Stratakis Lea Maitre Jordi Julvez Jose Urquiza Chongliang Luo Mark Nieuwenhuijsen Cathrine Thomsen Regina Grazuleviciene Barbara Heude Rosemary McEachan Marina Vafeiadi Leda Chatzi John Wright Tiffany C. Yang Rémy Slama Valérie Siroux Martine Vrijheid Xavier Basagaña |
| author_facet | Augusto Anguita-Ruiz Ines Amine Nikos Stratakis Lea Maitre Jordi Julvez Jose Urquiza Chongliang Luo Mark Nieuwenhuijsen Cathrine Thomsen Regina Grazuleviciene Barbara Heude Rosemary McEachan Marina Vafeiadi Leda Chatzi John Wright Tiffany C. Yang Rémy Slama Valérie Siroux Martine Vrijheid Xavier Basagaña |
| author_sort | Augusto Anguita-Ruiz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T02:16:15Z |
| format | Article |
| id | doaj.art-d4b03ca3feda46c3b7d3bede03f8137a |
| institution | Directory Open Access Journal |
| issn | 0160-4120 |
| language | English |
| last_indexed | 2024-03-09T02:16:15Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj.art-d4b03ca3feda46c3b7d3bede03f8137a2023-12-07T05:27:36ZengElsevierEnvironment International0160-41202023-12-01182108344Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome researchAugusto Anguita-Ruiz0Ines Amine1Nikos Stratakis2Lea Maitre3Jordi Julvez4Jose Urquiza5Chongliang Luo6Mark Nieuwenhuijsen7Cathrine Thomsen8Regina Grazuleviciene9Barbara Heude10Rosemary McEachan11Marina Vafeiadi12Leda Chatzi13John Wright14Tiffany C. Yang15Rémy Slama16Valérie Siroux17Martine Vrijheid18Xavier Basagaña19ISGlobal, 08003 Barcelona, Spain; CIBEROBN (CIBER Physiopathology of Obesity and Nutrition), Instituto de Salud Carlos III, 28029 Madrid, SpainUniversity Grenoble Alpes, Inserm U 1209, CNRS UMR 5309, Team of Environmental Epidemiology Applied to the Development and Respiratory Health, Institute for Advanced Biosciences, 38000 Grenoble, FranceISGlobal, 08003 Barcelona, SpainISGlobal, 08003 Barcelona, Spain; Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, SpainISGlobal, 08003 Barcelona, Spain; CIBEROBN (CIBER Physiopathology of Obesity and Nutrition), Instituto de Salud Carlos III, 28029 Madrid, Spain; Epidemiology and Environmental Health Joint Research Unit, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region, FISABIO-Public Health, FISABIO-Universitat Jaume I-Universitat de València, Av. Catalunya 21, 46020 Valencia, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Clinical and Epidemiological Neuroscience Group (NeuroÈpia), 43204 Reus (Tarragona), Catalonia, SpainISGlobal, 08003 Barcelona, SpainDivision of Public Health Sciences, Washington University School of Medicine in St. Louis, 600 S Taylor Ave, St. Louis, MO 63110, USAISGlobal, 08003 Barcelona, Spain; Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, SpainDepartment of Food Safety, Norwegian Institute of Public Health (NIPH), Oslo, NorwayDepartment of Environmental Science, Vytautas Magnus University, 44248 Kaunas, LithuaniaUniversité Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, FranceBradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UKDepartment of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, GreeceDepartment of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, GreeceBradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UKBradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UKUniversity Grenoble Alpes, Inserm U 1209, CNRS UMR 5309, Team of Environmental Epidemiology Applied to the Development and Respiratory Health, Institute for Advanced Biosciences, 38000 Grenoble, FranceUniversity Grenoble Alpes, Inserm U 1209, CNRS UMR 5309, Team of Environmental Epidemiology Applied to the Development and Respiratory Health, Institute for Advanced Biosciences, 38000 Grenoble, FranceISGlobal, 08003 Barcelona, Spain; Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, SpainISGlobal, 08003 Barcelona, Spain; Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; Corresponding author at: C/ del Dr. Aiguader, 88, 08003 Barcelona, Spain.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.http://www.sciencedirect.com/science/article/pii/S0160412023006177Outcome-wide analysisMulti-outcome analysisExposome analysisEnvironmental epidemiology |
| spellingShingle | Augusto Anguita-Ruiz Ines Amine Nikos Stratakis Lea Maitre Jordi Julvez Jose Urquiza Chongliang Luo Mark Nieuwenhuijsen Cathrine Thomsen Regina Grazuleviciene Barbara Heude Rosemary McEachan Marina Vafeiadi Leda Chatzi John Wright Tiffany C. Yang Rémy Slama Valérie Siroux Martine Vrijheid Xavier Basagaña Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research Environment International Outcome-wide analysis Multi-outcome analysis Exposome analysis Environmental epidemiology |
| title | Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research |
| title_full | Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research |
| title_fullStr | Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research |
| title_full_unstemmed | Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research |
| title_short | Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research |
| title_sort | beyond the single outcome approach a comparison of outcome wide analysis methods for exposome research |
| topic | Outcome-wide analysis Multi-outcome analysis Exposome analysis Environmental epidemiology |
| url | http://www.sciencedirect.com/science/article/pii/S0160412023006177 |
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