# Trained immunity in atherosclerosis: plasticity, metabolic-vascular axis, and AI-driven precision remodeling

## TL;DR

This paper explores how trained immunity in atherosclerosis involves dynamic immune cell changes, metabolism, and AI-based approaches for personalized treatment.

## Contribution

The paper introduces four novel frameworks, including a model of trained immunity plasticity and AI-driven precision remodeling for atherosclerosis.

## Key findings

- Monocytes dynamically balance pro-inflammatory and anti-inflammatory states via mTOR/AMPK signaling and histone modifications.
- Metabolic disorders influence immune memory formation through vascular microenvironment changes and epigenetic communication.
- AI and CRISPR-based interventions offer new ways to reprogram immune memory for personalized atherosclerosis treatment.

## Abstract

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.