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Approved Research

Genetic and non-genetic factors associated with cervical myelopathy and radiculopathy

Principal Investigator: Dr Elizabeth Yanik
Approved Research ID: 55067
Approval date: August 20th 2020

Lay summary

Spinal disorders in the neck (cervical spine) can lead to significant pain and disability. Cervical radiculopathy (nerve root compression) and myelopathy (spinal compression leading to spinal injury) are most often caused by degenerative changes in the cervical spine. While everyone's spines degenerate with aging, not everyone develops myelopathy or radiculopathy. This leads one to suspect that there may be genetic or environmental factors that predispose some people to development of myelopathy or radiculopathy.

To answer our questions regarding what factors may contribute to cervical myelopathy or radiculopathy we development 3 aims. Our first aim is to determine whether certain genetic markers are associated with cervical spinal radiculopathy and myelopathy. Our second aim is to determine how much occupational demands play into the development of spinal radiculopathy and myelopathy risk. Our third aim is to develop a predictive model for spinal radiculopathy and myelopathy risk that includes the genetic and non-genetic risk factors that we identify.

Our department has experience in using the UK Biobank which should facilitate project design, implementation and timely completion. We expect to finish the project in 2.5 years or sooner. We believe that our project will have a far reaching public health impact. A more comprehensive understanding of the relationship between genetics and cervical spinal disease can allow a more focused look at specific genetic susceptibility measures in smaller cohorts with more detailed clinical information, including investigations of how genetic factors may influence cervical spinal disease progression and outcomes after surgical treatment. Identification of occupations associated with high risk of cervical spinal disease can inform studies targeted toward specific occupational groups, in which evaluation of associations between particular workplace tasks and disease could lead to opportunities for prevention through modification of these tasks. Furthermore, future studies of interactions between genetic and non-genetic risk factors could lead to a better understanding of a multifactorial disease process and aid in targeting prevention measures. Finally, the prediction model developed can be replicated in the US as other large cohorts with genetic information are developed.