REVIEW article
Front. Immunol.
Sec. Immunological Memory
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1669796
Trained Immunity in Atherosclerosis: Plasticity, Metabolic-Vascular Axis, and AI-Driven Precision Remodeling
Provisionally accepted- 1Zhejiang Chinese Medical University, Hangzhou, China
- 2Zhejiang University School of Medicine, Hangzhou, China
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Chronic inflammation linked to atherosclerosis is closely related to a trained immunoregulatory network. Traditional studies primarily focus on the pro-inflammatory memory of monocytes, they frequently neglect important aspects such as the cell's plasticity, interactions between different organs, and the dynamic regulation of the metabolism-vascular axis. This review presents four novel frameworks, including the trained immunity plasticity spectrum model. It demonstrates how monocytes maintain a dynamic balance between pro-inflammatory, tolerogenic, and anti-inflammatory phenotypes, regulated by mTOR/AMPK signaling and competitive histone modifications. The trained immunity–metabolism– vascular axis shows that metabolic disorders can change the way immune memory is formed. They achieve this by modifying the vascular microenvironment through epigenetic changes, exosomes, and products of mitochondrial stress. The cross-organ trained immunity framework reveals how remote epigenetic communication between the bone marrow, gut, and liver influences the development of monocytes. Finally, dynamic immune reprogramming integrates CRISPR-based epigenetic editing, metabolism-focused interventions, and AI-driven multi-omics predictions. This approach signifies a major transition from simply alleviating symptoms to accurately reshaping immune memory. This review reinterprets the immunometabolic mechanisms of atherosclerosis. It also lays the foundation for personalized therapies enhanced by AI and explores new interdisciplinary research avenues.
Keywords: trained immunity, Atherosclerosis, Metabolic-vascular axis, immune plasticity, AI-driven precisionmedicine
Received: 20 Jul 2025; Accepted: 25 Sep 2025.
Copyright: © 2025 Wan, Zhao, Zhou, Wan and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Haitong Wan, whtoog@126.com
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