Biomarkers of Cervical Spondylotic Myelopathy

Investigators believe that patients with cervical spondylotic myelopathy (CSM) have measurable changes in certain molecules in their blood compared with people without CSM. Investigators expect that these changes may be related to how severe a patient's condition is based on imaging and clinical exams. Investigators will also study how these molecules change before and after surgery to see whether they are related to how well patients recover. By combining these blood markers with clinical and imaging information, investigators hope to develop a tool that may help predict patient outcomes.

Cervical spondylotic myelopathy (CSM) represents the most common cause of non-traumatic spinal cord injury in adults (New et al., 2014) with an estimated prevalence of 605 per million in North America (Nouri et al., 2015). This degenerative condition leads to spinal cord compression which can result in severely debilitating symptoms such as weakness, loss of fine motor control, bowel and bladder dysfunction, and pain. Surgical decompression is often needed in patients with symptomatic neural element stenosis, and surgical decision-making is primarily based on clinical examination findings and imaging evidence of cervical spinal cord compression. However, the degree of spinal cord compression observed on imaging poorly correlates with the severity of neurologic dysfunction in cervical myelopathy patients (Nagata et al., 2012; Rhee et al., 2009). Additionally, there are no reliable objective measures or prognostic factors to aid in assessing patients best-suited for surgical decompression and recovery following surgery. This study aims to identify serum biomarkers of CSM injury severity and long-term outcomes. This proposal focuses on transcriptomic changes as well as radiographic and clinical data variables in CSM patients to characterize RNA biomarkers of injury and recovery, which has been studied to date. Identification of prognostic biomarkers of CSM may uncover new mechanisms underlying spinal cord injury and develop predictive algorithms for CSM treatment planning and prediction of long-term outcomes.