Artificial intelligence in decoding muscle–organ crosstalk: Unveiling myokine networks and therapeutic frontiers

Abstract

Skeletal muscle functions as an endocrine organ, secreting myokines that mediate crosstalk with organs like the brain, liver, adipose tissue, and vascular system, influencing metabolism, inflammation, and disease progression. Advances in artificial intelligence (AI) are revolutionizing our ability to decode these complex interactions by predicting novel myokines, modeling signaling networks, and identifying therapeutic targets. Exercise training plays a pivotal role in modulating myokine expression, with both aerobic and resistance exercise inducing small to large increases in circulating myokines immediately to 60 minutes post-exercise, though levels typically return to baseline within hours. Different exercise modalities (resistance, aerobic, concurrent, high intensity interval training) stimulate distinct myokine profiles. These exercise-induced myokines contribute to improved metabolic regulation, muscle regeneration, and systemic health benefits, underscoring the therapeutic potential of tailored exercise interventions mediated through myokine signaling networks. This review explores how machine learning and network analysis tools bridge gaps in understanding myokine dynamics, particularly in exercise-induced contexts and pathologies such as obesity, cancer, and neurodegeneration. By integrating multi-omics data, AI-driven approaches offer unprecedented insights into myokine-mediated organ communication and their potential as biomarkers or treatments.

Keywords

• myokine
• artificial intelligence
• muscle
• organ crosstalk