Fernández-Pérez, IsabelJiménez-Balado, JoanLazcano, UxueGiralt-Steinhauer, EvaRey Álvarez, LucíaCuadrado Godia, ElisaRodríguez-Campello, AnaMacias-Gómez, AdriàSuárez-Pérez, AntoniRevert-Barberá, AnnaEstragués-Gázquez, IsabelSoriano Tarraga, CarolinaRoquer, JaumeOis Santiago, Angel JavierJiménez Conde, Jordi2023-06-202023-06-202023Fernández-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/ijms240327591661-6596http://hdl.handle.net/10230/57253Age 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.application/pdfeng© 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/).info:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/ijms24032759AgingEpigenetic clockMachine learningVascular risk factorsStrokeinfo:eu-repo/semantics/openAccess