Combined accelerometer and genetic analysis to differentiate essential tremor from Parkinson’s disease

Author:

Molparia Bhuvan12,Schrader Brian N.1,Cohen Eli3,Wagner Jennifer L.1,Gupta Sandeep R.3,Gould Sherrie4,Hwynn Nelson4,Spencer Emily G.1ORCID,Torkamani Ali12ORCID

Affiliation:

1. The Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, USA

2. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA

3. Intel Corporation, Santa Clara, CA, USA

4. Scripps Clinic Torrey Pines, Scripps Health, La Jolla, CA, USA

Abstract

Essential tremor (ET) and Parkinson’s disease (PD) are among the most common adult-onset tremor disorders. Clinical and pathological studies suggest that misdiagnosis of PD for ET, and vice versa, occur in anywhere from 15% to 35% of cases. Complex diagnostic procedures, such as dopamine transporter imaging, can be powerful diagnostic aids but are lengthy and expensive procedures that are not widely available. Preliminary studies suggest that monitoring of tremor characteristics with consumer grade accelerometer devices could be a more accessible approach to the discrimination of PD from ET, but these studies have been performed in well-controlled clinical settings requiring multiple maneuvers and oversight from clinical or research staff, and thus may not be representative of at-home monitoring in the community setting. Therefore, we set out to determine whether discrimination of PD vs. ET diagnosis could be achieved by monitoring research subject movements at home using consumer grade devices, and whether discrimination could be improved with the addition of genetic profiling of the type that is readily available through direct-to-consumer genetic testing services. Forty subjects with PD and 27 patients with ET were genetically profiled and had their movements characterized three-times a day for two weeks through a simple procedure meant to induce rest tremors. We found that tremor characteristics could be used to predict diagnosis status (sensitivity = 76%, specificity = 65%, area under the curve (AUC) = 0.75), but that the addition of genetic risk information, via a PD polygenic risk score, did not improve discriminatory power (sensitivity = 80%, specificity = 65%, AUC = 0.73).

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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