Cardiac unloading models for myocardial reverse remodeling.

Authors

Xinjie Xu, Yukun Luo, Tao Lei, Zichen Wu, Jiaying Cao, Jiansong Huang, Henan Zhang, Xiaoyan Li, Xiaying Deng, Dongjing Zhou, Yibo Zhang, Antoni Bayes-Genis, Liang Chen

Published in

Basic research in cardiology. Jul 08, 2026. Epub Jul 08, 2026.

Abstract

Myocardial reverse remodeling (RR) represents the structural, functional, cellular, and molecular recovery of the failing heart, leading to long-term prognostic improvements. This process can be mediated by various clinical modalities, including pharmacological treatment and interventional/surgical procedures. Among these approaches, left ventricular assist devices (LVADs) exhibit the most potent capacity for mechanical unloading and inducing RR, which has profoundly transformed the treatment paradigm and prognosis of end-stage heart failure. Furthermore, the implantation and explantation of LVAD devices facilitate the collection of paired patient samples before and after treatment, providing a unique real-world model for investigating this phenomenon. However, the scarcity of human myocardial samples is insufficient to meet the demands of elucidating the mechanisms of reverse cardiac remodeling and identifying novel therapeutic targets. Findings derived from human specimens require validation through experimental unloading models, which inherently offer novel perspectives on cardiac pathophysiology. Thus, reproducible models of myocardial unloading have become valuable complementary resources. This review systematically summarizes progress in understanding the relationship between mechanical load and myocardial phenotypes within various unloading models. We further discuss how these reproducible unloading models provide a solid foundation for elucidating the mechanisms of RR and ultimately developing novel therapies to improve patient outcomes.

PMID:
42420652
Bibliographic data and abstract were imported from PubMed on 09 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.