GADGET-PD Genetic and Digital Diagnosis of Essential Tremor and Parkinson’s Disease Trial – Full Text View

Destiny Viator

The proposed study will capitalize on the early predictive information stored in an individual’s genetic risk for Parkinson Disease (PD) in combination with the subtle features of tremors that can be extracted from movement data gathered by modern compact accelerometers in order to determine if accurate discrimination of essential tremor […]

The proposed study will capitalize on the early predictive information stored in an individual’s genetic risk for Parkinson Disease (PD) in combination with the subtle features of tremors that can be extracted from movement data gathered by modern compact accelerometers in order to determine if accurate discrimination of essential tremor (ET) from PD can be achieved. Both of these technologies have a proven but somewhat limited ability to inform diagnosis of PD or differentiation of PD from ET – especially at early stages of the disease. The investigators hypothesize that a combination of prior genetic risk and current disease symptomology can synergize for accurate and early discrimination of PD from ET and ultimately inform a cost effective approach to movement disorder diagnosis.

In this study, the investigators will collect blood from individuals with confirmed late-onset diagnosis of PD and ET. Gold standard diagnosis status will be determined via the Unified Parkinson’s Disease Rating Scale (UPDRS) – the accepted clinical gold standard for Parkinson’s Disease diagnosis. DNA will be extracted from blood samples to characterize the genetic risk of individuals for PD via proven genetic risk models. In addition, participants will wear a wristwatch-like accelerometer device that will track their movements (tremors) at high temporal resolution and transmit movement data via a smartphone. Cognitive distraction tasks will be administered via mobile phones while simultaneously collecting movement data. Predictive tremor features will be extracted from movement data via signal processing approaches – e.g. discrete wavelet transformation. A final predictive model combining movement tracking information and genetic information will be designed in attempt to distinguish PD from ET individuals.

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