Machine learning-based health environmental-clinical risk scores in European children

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• dc.contributor.author Guimbaud, Jean-Baptiste
• dc.contributor.author Sabidó Aguadé, Eduard, 1981-
• dc.contributor.author Borràs, Eva
• dc.contributor.author Júlvez Calvo, Jordi
• dc.contributor.author Urquiza, José M.
• dc.contributor.author Casas Sanahuja, Maribel
• dc.contributor.author Bustamante Pineda, Mariona
• dc.contributor.author Nieuwenhuijsen, Mark J.
• dc.contributor.author Vrijheid, Martine
• dc.contributor.author López Vicente, Mònica, 1988-
• dc.contributor.author de Castro, Montserrat
• dc.contributor.author Basagaña Flores, Xavier
• dc.contributor.author Maitre, Léa
• dc.date.accessioned 2024-11-22T11:36:55Z
• dc.date.available 2024-11-22T11:36:55Z
• dc.date.issued 2024
• dc.description.abstract Background: Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data. Methods: In this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 mother-child clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest and XGBoost. XGBoost ECRS were selected to extract local feature contributions using Shapley values and derive feature importance and interactions. Results: ECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods.The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health. Conclusions: Besides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms.
• dc.description.sponsorship The authors would like to thank all the participating children, parents, practitioners, and researchers in the six countries who took part in this study. The Norwegian Mother, Father and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We also acknowledge the support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Programme / Generalitat de Catalunya. The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech) and it is supported by “Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya” (2021SGR01225 and 2021SGR01563). This project was funded by the H2020-EU.3.1.2.—Preventing Disease Programme under grant agreement no 874583 (ATHLETE project). JB Guimbaud was supported by a CIFRE PhD fellowship (#2020/1297). Léa Maitre 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. Jose Urquiza is supported by Catalan program PERIS (Ref.: SLT017/20/000119), granted by Departament de Salut de la Generalitat de Catalunya (Spain). Oliver Robinson was funded by the UK Research and Innovation Future Leaders Fellowship (MR/S03532X/1). Silvia Alemany holds a Miquel Servet-I contract (CP22/00026) awarded by the Instituto de Salud Carlos III co-funded by the European Union Found: Fondo Social Europeo Plus, FSE + . Mónica López-Vicente is funded by a Juan de la Cierva-Incorporación fellowship (project IJC2020-045355-I, funded by MCIN/AEI/10.13039/501100011033 and for the European Union NextGenerationEU/PRTR). The data of the cohorts (BiB, EDEN, INMA, KANC, MoBa and RHEA) provided to this research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007‐2013) under grant agreement no 308333—the HELIX project. Fig. 2 was created with BioRender.com.
• dc.format.mimetype application/pdf
• dc.identifier.citation Guimbaud JB, Siskos AP, Sakhi AK, Heude B, Sabidó E, Borràs E, et al. Machine learning-based health environmental-clinical risk scores in European children. Commun Med (Lond). 2024 May 23;4(1):98. DOI: 10.1038/s43856-024-00513-y
• dc.identifier.doi http://dx.doi.org/10.1038/s43856-024-00513-y
• dc.identifier.issn 2730-664X
• dc.identifier.uri http://hdl.handle.net/10230/68783
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Commun Med (Lond). 2024 May 23;4(1):98
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/874583
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/308333
• dc.rights © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Epidemiology
• dc.subject.keyword Paediatric research
• dc.title Machine learning-based health environmental-clinical risk scores in European children
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion