Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients

Autor: Isabel Fernández-Pérez, Joan Jiménez-Balado, Uxue Lazcano, Eva Giralt-Steinhauer, Lucía Rey Álvarez, Elisa Cuadrado-Godia, Ana Rodríguez-Campello, Adrià Macias-Gómez, Antoni Suárez-Pérez, Anna Revert-Barberá, Isabel Estragués-Gázquez, Carolina Soriano-Tarraga, Jaume Roquer, Angel Ois, Jordi Jiménez-Conde
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: International Journal of Molecular Sciences, Vol 24, Iss 3, p 2759 (2023)
Druh dokumentu: article
ISSN: 1422-0067
1661-6596
DOI: 10.3390/ijms24032759
Popis: 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.
Databáze: Directory of Open Access Journals
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