Biomarkers are objectively measurable characteristics that can be used to diagnose a disease or to track its progression. In this study, we will explore circular RNAs -- a recently discovered type of RNA, a molecule that plays a role in protein production -- as a possible biomarker of Parkinson's disease (PD). We aim to find a specific type of circular RNA useful in diagnosing PD early and in identifying people with Parkinson's who will develop dementia, a disease of memory, thinking and/or social abilities that is severe enough to interfere with everyday activities. This, in turn, will allow timely treatment.
We hypothesize that specific circular RNAs are present in biosamples from people with Parkinson's and not in biosamples from healthy people. These circular RNAs could be used to identify people most likely to develop PD.
We will compare the levels of circular RNAs in biosamples donated by people with Parkinson's and with those of healthy people. We will utilize blood, serum and brain tissue samples collected in the course of the ICICLE-PD study for the purpose of studying the development and progression of Parkinson's disease. The levels of circular RNAs will be assessed using cutting-edge technology. All data will be analyzed using computer algorithms developed at Newcastle University specifically to detect and measure circular RNAs.
Impact on Diagnosis/Treatment of Parkinson's Disease:
Early diagnosis and treatment have a potential to greatly improve the quality of life of people with Parkinson's disease. In addition, identifying and studying new biomarkers of Parkinson's is likely to open new avenues for the development of therapeutics.
Next Steps for Development:
If our hypothesis is correct and specific circular RNAs are indeed associated with PD and predict dementia, then our next step will be to confirm our findings by testing another set of samples, such as samples and data from the Parkinson's Progression Markers Initiative (PPMI), The Michael J. Fox Foundation's landmark clinical study to find biomarkers. Should this confirm our findings, we would use this data to improve our understanding of disease mechanisms while seeking collaborative partnerships to develop diagnostic tests.