Investigator:
Ignacio Mata, PhD
Name of Institution:
Cleveland Clinic Foundation
Project Title:
Machine-learning model for predicting levodopa-induced dyskinesias in a large cohort of Latinos with Parkinson’s disease
Investigator Bio:
Dr. Mata is Associate Staff at the Genomic Medicine Institute (GMI) at the Cleveland Clinic Foundation (CCF) and Assistant Professor of Molecular Medicine at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University (CCLCM-CWRU). He has worked in the field of Parkinson’s disease (PD) genetics for over 20 years.
Dr. Mata received his BS and PhD from the University of Oviedo, in Spain, under the mentorship of Dr. Victoria Alvarez and funded by the Asociacion Parkinson Asturias. During his PhD, he was a visiting scientist at the Mayo Clinic in Jacksonville in Dr. Farrer’s lab. Dr. Mata later moved to Seattle to pursue a post doctoral fellowship with Dr. Cyrus Zabetian at the University of Washington and the Veteran’s Affairs Puget Sound, where he became Research Assistant Professor. For the past six years he has been at the Cleveland Clinic.
A significant focus of Dr. Mata’s research has been performing genetic studies in non-European populations. Dr. Mata created and continues to coordinate the Latin American Research consortium on the Genetics of PD (LARGE-PD), a collaboration of more than 50 institutions in 13 countries in Latin America and the Caribbean, trying to identify the genetic risk factors associated with PD in these populations. His work has been internationally recognized and he leads the Underrepresented Populations Working group in the Aligning Science Across Parkinson’s Global Parkinson’s Genetics Program (ASAP-GP2), a world-wide initiative to further understand the genetic architecture of PD in all populations.
Objective:
To create a computer-based tool that can predict who might develop levodopa-induced dyskinesia (LID) among Latino individuals with PD.
Background:
PD is commonly treated with levodopa to improve movement problems. However, this medication can lead to a side effect called levodopa-induced dyskinesia (LID), non-purposeful body movements. LID can vary in its severity and can greatly affect the quality of life of people with PD. It also adds to the cost of treatment. Finding ways to predict who might develop LID is a big challenge in treating PD. Many studies have looked at different factors that might contribute to LID, like a person’s age, genetics, and other medical conditions. But most of these studies have only looked at one factor at a time and have mainly been done in people of European descent, which might not apply to other groups like Latinos. Therefore, right now, there is not a good tool for health providers to predict who might develop LID.
Methods/Design:
We will analyze genetic data on genes linked to LID and PD risk from more than 2,000 Latinos with PD from the Latin American Research consortium in the Genetics of Parkinson’s Disease (LARGE-PD). We will analyze this data along with information about their general health, lifestyle, and environmental exposures. Then, we will use several computer algorithms and test if we can predict who might develop LID. We will replicate the best-performing model in an independent PD cohort from the Cleveland Clinic.
Relevance to Diagnosis/Treatment of Parkinson’s Disease:
These findings have the potential to offer personalized care for people with PD at high risk of developing LID, while focusing on the Latino PD community, a historically underrepresented population group. Furthermore, the findings will offer valuable insights into the biological factors contributing to LID, thereby paving the way for potential solutions.