Neurovascular Research Group, Department of Neurology, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), 08003 Barcelona, Spain.
Unidad de Investigación AP-OSIs Guipúzcoa, 20014 Donostia, Spain.
Int J Mol Sci. 2023 Feb 1;24(3):2759. doi: 10.3390/ijms24032759.
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 (R 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.
年龄加速（Age-A）是一种有用的工具，能够预测广泛的健康结果。需要确定 DNA 甲基化水平来估计它，并且已知 Age-A 受环境、生活方式和血管风险因素（VRF）的影响。本研究的目的是使用不同的机器学习（ML）近似值来估计这些易于测量的因素对脑血管疾病（CVD）患者 Age-A 的贡献，并尝试找到一个更易于访问的模型来预测 Age-A。我们研究了一个包含 952 名患者信息的 CVD 队列，包括 VRF、生活方式习惯和靶器官损伤。我们使用 Hannum 的表观遗传时钟来估计 Age-A，并训练了六个不同的模型来预测 Age-A：一个传统的线性回归模型、四个 ML 模型（弹性网回归（EN）、K-最近邻、随机森林和支持向量机模型）和一个深度学习近似模型（多层感知机（MLP）模型）。表现最好的模型是 EN 和 MLP；尽管如此，预测能力还是中等（分别为 0.358 和 0.378）。总之，我们的结果支持这些因素对 Age-A 的影响；尽管如此，它们还不足以解释其大部分可变性。
