Fernández-Pérez I, Jiménez-Balado J, Lazcano U, Giralt-Steinhauer E, et al. Int J Mol Sci. 2023 Feb 1;24(3):2759. doi: 10.3390/ijms24032759. PMID: 36769083; PMCID: PMC9917369
https://pubmed.ncbi.nlm.nih.gov/36769083/
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.
Funding: This work was supported by grants from the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III with the grants “Registro BASICMAR” Funding for Research in Health (PI051737), Fondos de Investigación Sanitaria ISC III (PI12/01238), (PI15/00451), (PI18/00022), (PI21/00593); Sara Borrell program, funded by Instituto de Salud Carlos III (CD22/00001, J.J.-B.); and Fondos FEDER/EDRF Spanish stroke research network INVICTUS+ (RD16/0019/0002) and Grant “RICORS-ICTUS” (RD21/0006/0021) funded by Instituto de Salud Carlos III (ISCIII), and by Int. J. Mol. Sci. 2023, 24, 2759 13 of 15 the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia (MRR). Additional support was provided by Recercaixa’13 (JJ086116).