Aging, Regeneration, and Peripheral Nerve Repair: Integrated Multi-Omics Biomarkers and AI-Enabled Therapeutic...

Peripheral nerve injury (PNI) remains a major cause of long-term sensory and motor dysfunction, imposing a substantial clinical and socioeconomic burden worldwide. Although peripheral nerves possess intrinsic regenerative capacity, functional recovery is often incomplete because of the complex interplay among axonal degeneration, Schwann cell plasticity, neuroimmune responses, extracellular matrix remodeling, metabolic reprogramming, remyelination, and target reinnervation. Recent advances in transcriptomics, proteomics, epigenomics, metabolomics, and single-cell technologies have expanded molecular characterization of nerve degeneration and regeneration, revealing numerous candidate biomarkers and therapeutic targets. However, biological heterogeneity, fragmented datasets, and the lack of robust predictive frameworks still limit translation into clinical practice. In this review, we provide a comprehensive overview of the cellular and molecular mechanisms governing peripheral nerve repair and examine emerging biomarker classes associated with regenerative competence, neuroinflammation, remyelination, and functional recovery. We also discuss therapeutic strategies targeting Schwann cell reprogramming, axonal growth, immune modulation, metabolic adaptation, and remodeling of the regenerative microenvironment. We further examine how artificial intelligence (AI) and machine learning (ML) may support multi-omics integration, biomarker prioritization, outcome prediction, and therapeutic target discovery, while emphasizing that PNI-specific clinical validation remains limited. We propose a systems-level framework that links molecular signatures with clinical outcomes and highlights the potential of AI-enabled multimodal analytics to support translational research. Age-related molecular variation should be incorporated as an additional analytical dimension, allowing comparison of regenerative and non-regenerative signatures across younger and older tissues. Together, these approaches provide a framework for testing age-aware molecular markers and computational models in future translational studies.