Machine learning approximations to predict epigenetic age acceleration in stroke patients

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• dc.contributor.author Fernández-Pérez, Isabel
• dc.contributor.author Jiménez-Balado, Joan
• dc.contributor.author Lazcano, Uxue
• dc.contributor.author Giralt-Steinhauer, Eva
• dc.contributor.author Rey Álvarez, Lucía
• dc.contributor.author Cuadrado Godia, Elisa
• dc.contributor.author Rodríguez-Campello, Ana
• dc.contributor.author Macias-Gómez, Adrià
• dc.contributor.author Suárez-Pérez, Antoni
• dc.contributor.author Revert-Barberá, Anna
• dc.contributor.author Estragués-Gázquez, Isabel
• dc.contributor.author Soriano Tarraga, Carolina
• dc.contributor.author Roquer, Jaume
• dc.contributor.author Ois Santiago, Angel Javier
• dc.contributor.author Jiménez Conde, Jordi
• dc.date.accessioned 2023-06-20T06:22:44Z
• dc.date.available 2023-06-20T06:22:44Z
• dc.date.issued 2023
• dc.description.abstract Age acceleration (Age-A) is a useful tool that is able to predict a broad range of health outcomes. It is necessary to determine DNA methylation levels to estimate it, and it is known that Age-A is influenced by environmental, lifestyle, and vascular risk factors (VRF). The aim of this study is to estimate the contribution of these easily measurable factors to Age-A in patients with cerebrovascular disease (CVD), using different machine learning (ML) approximations, and try to find a more accessible model able to predict Age-A. We studied a CVD cohort of 952 patients with information about VRF, lifestyle habits, and target organ damage. We estimated Age-A using Hannum’s epigenetic clock, and trained six different models to predict Age-A: a conventional linear regression model, four ML models (elastic net regression (EN), K-Nearest neighbors, random forest, and support vector machine models), and one deep learning approximation (multilayer perceptron (MLP) model). The best-performing models were EN and MLP; although, the predictive capability was modest (R2 0.358 and 0.378, respectively). In conclusion, our results support the influence of these factors on Age-A; although, they were not enough to explain most of its variability.
• dc.format.mimetype application/pdf
• dc.identifier.citation Fernández-Pérez I, Jiménez-Balado J, Lazcano U, Giralt-Steinhauer E, Rey L, Cuadrado-Godia E, et al. Machine learning approximations to predict epigenetic age acceleration in stroke patients. IJMS. 2023 Feb 1;24(3):2759. DOI: 10.3390/ijms24032759
• dc.identifier.doi http://dx.doi.org/10.3390/ijms24032759
• dc.identifier.issn 1661-6596
• dc.identifier.uri http://hdl.handle.net/10230/57253
• dc.language.iso eng
• dc.publisher MDPI
• dc.relation.ispartof International Journal of Molecular Sciences. 2023 Feb 1;24(3):2759
• dc.rights © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://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 Aging
• dc.subject.keyword Epigenetic clock
• dc.subject.keyword Machine learning
• dc.subject.keyword Vascular risk factors
• dc.subject.keyword Stroke
• dc.title Machine learning approximations to predict epigenetic age acceleration in stroke patients
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion