Spinal cord injury (SCI) is a profoundly debilitating neurological condition that leads to persistent sensory, motor, and autonomic dysfunction. Although the initial mechanical insult is unavoidable, long-term outcomes are largely shaped by secondary injury processes, including ischemia, oxidative stress, neuroinflammation, mitochondrial dysfunction, and maladaptive glial activation. Despite substantial advances in experimental research, no pharmacological therapy has yet succeeded in preventing or reversing secondary injury in clinical settings, underscoring a major translational gap between animal models and human SCI biology.
This project aims to address this gap by exploiting the UK Biobank’s extensive genetic, biochemical, imaging, and longitudinal health data to define human-specific molecular and metabolic determinants of SCI and its ischemic and inflammatory complications. Using artificial intelligence, machine learning, and systems biology approaches, the study will integrate multi-omics datasets to identify regulatory networks and pharmacologically actionable targets associated with injury severity and progression. Temporal stratification will enable the analysis of molecular changes across acute and chronic phases of SCI. Comparative evaluation with rat SCI datasets will distinguish conserved from human-specific mechanisms, thereby strengthening translational relevance. Ultimately, this research will generate predictive models to prioritise therapeutic targets and establish a mechanistically informed framework for future drug discovery aimed at mitigating secondary injury and promoting neural protection in SCI.
