Precision prevention in exercise: Integrating OMICS biomarkers and artificial intelligence for injury risk assessment

作者：Mohammad Nasb, Lamis Dayoub, Ning Chen · 发表于：Advanced Exercise and Health Science · 年份：2025 · DOI：10.1016/j.aehs.2025.09.001 · 被引用次数：7 · 研究领域：Genetics and Physical Performance、Cardiovascular Effects of Exercise、Injury Epidemiology and Prevention

Exercise injuries present a significant challenge in medicine and public health, impacting exercise performance, career longevity, and overall quality of life. While OMICS technologies such as genomics, transcriptomics, proteomics, and metabolomics offer unprecedented insights into individual susceptibility, recovery, and performance optimization, their full potential is realized through the integration of artificial intelligence (AI). This article explores the transformative role of OMICS biomarkers in understanding the molecular underpinnings of exercise injuries. Crucially, it emphasizes how AI and advanced data analytics are essential for processing and interpreting the vast, complex datasets generated by multi-OMICS strategies. By leveraging machine learning algorithms, AI can identify subtle patterns and predictive biomarkers, thereby significantly enhancing the accuracy of injury risk assessment, enabling personalized intervention strategies, and facilitating real-time monitoring of exercise, health and recovery. This integration moves beyond traditional biological insights, paving the way for truly predictive and preventive precision exercise medicine. Furthermore, this article addresses the ethical, legal, and logistical challenges associated with the application of OMICS and AI technologies, emphasizing the need for robust regulatory frameworks and equitable access to these advancements. The synergistic combination of OMICS and AI stands as a cornerstone of future p...