Age-Dependent Myocardial Adaptation to Endurance Training: A Multimodal Assessment in a Rodent Model

Ramaz Khetsuriani, Manana Arabuli, Davit Topuria, Marina Pailodze, Nikoloz Vachadze, Elene Shvangiradze, Irakli Amiranashvili, Tamar Sultanishvili, Anzor Gogiberidze, Sopiko Kandelaki, Nino Pruidze

Abstract


Aging significantly impacts myocardial function and its adaptability to endurance training, yet the extent and mechanisms of these changes remain incompletely understood. This study investigates the myocardial adaptations to an eight-week progressive endurance training program in a controlled rodent model, stratified into four age groups: young (2–3 months), early-middle age (6–8 months), late-middle age (12–14 months), and elderly (>18 months). Echocardiographic assessments, heart rate variability (HRV) analysis, and metabolic biomarkers (serum lactate and troponin-I) were measured at baseline, mid-experiment, and post-experiment to evaluate cardiovascular function and stress responses. A sedentary control group was included to distinguish between exercise-induced and age-related myocardial changes. The results revealed significant improvements in left ventricular ejection fraction (LVEF) and stroke volume in younger and early-middle-aged rats, while older groups exhibited attenuated responses. HRV progressively declined with age, indicating reduced autonomic regulation, and the elderly group demonstrated elevated post-training serum lactate and troponin-I levels, suggesting increased myocardial stress. Computational modeling provided predictive insights into myocardial functional trajectories based on age and training intensity. These findings underscore the age-dependent nature of cardiovascular adaptation to endurance training, highlighting the need for personalized exercise prescriptions to optimize heart health while mitigating risks in aging populations.

Keywords


Aging, Myocardial Adaptation, Endurance Training, Heart Rate Variability, Echocardiography, Cardiovascular Health, Exercise Physiology, Machine Learning

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DOI: http://dx.doi.org/10.52155/ijpsat.v50.2.7227

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