He Yudan, Chen Yao, Yao Lilin, Wang Junyi, Sha Xianzheng, Wang Yin
Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, China.
Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, Shenyang, China.
Front Genet. 2022 May 30;13:865827. doi: 10.3389/fgene.2022.865827. eCollection 2022.
Atherosclerosis, one of the main threats to human life and health, is driven by abnormal inflammation (i.e., chronic inflammation or oxidative stress) during accelerated aging. Many studies have shown that inflamm-aging exerts a significant impact on the occurrence of atherosclerosis, particularly by inducing an immune homeostasis imbalance. However, the potential mechanism by which inflamm-aging induces atherosclerosis needs to be studied more thoroughly, and there is currently a lack of powerful prediction models. First, an improved inflamm-aging prediction model was constructed by integrating aging, inflammation, and disease markers with the help of machine learning methods; then, inflamm-aging scores were calculated. In addition, the causal relationship between aging and disease was identified using Mendelian randomization. A series of risk factors were also identified by causal analysis, sensitivity analysis, and network analysis. Our results revealed an accelerated inflamm-aging pattern in atherosclerosis and suggested a causal relationship between inflamm-aging and atherosclerosis. Mechanisms involving inflammation, nutritional balance, vascular homeostasis, and oxidative stress were found to be driving factors of atherosclerosis in the context of inflamm-aging. In summary, we developed a model integrating crucial risk factors in inflamm-aging and atherosclerosis. Our computation pipeline could be used to explore potential mechanisms of related diseases.
动脉粥样硬化是对人类生命和健康的主要威胁之一，由加速衰老过程中的异常炎症（即慢性炎症或氧化应激）驱动。许多研究表明，炎症衰老对动脉粥样硬化的发生有重大影响，特别是通过诱导免疫稳态失衡。然而，炎症衰老诱导动脉粥样硬化的潜在机制需要更深入地研究，目前缺乏强大的预测模型。首先，借助机器学习方法，通过整合衰老、炎症和疾病标志物构建了一种改进的炎症衰老预测模型；然后，计算炎症衰老评分。此外，利用孟德尔随机化确定衰老与疾病之间的因果关系。还通过因果分析、敏感性分析和网络分析确定了一系列风险因素。我们的结果揭示了动脉粥样硬化中加速的炎症衰老模式，并表明炎症衰老与动脉粥样硬化之间存在因果关系。发现在炎症衰老背景下，涉及炎症、营养平衡、血管稳态和氧化应激的机制是动脉粥样硬化的驱动因素。总之，我们开发了一个整合炎症衰老和动脉粥样硬化关键风险因素的模型。我们的计算流程可用于探索相关疾病的潜在机制。
