Stochastic Epigenetic Mutations Influence Parkinson's Disease Risk, Progression, and Mortality

Gary K. Chen, Qi Yan, Kimberly C. Paul, Cynthia D.J. Kusters, Aline Duarte Folle, Melissa Furlong, Adrienne Keener, Jeff Bronstein, Steve Horvath, Beate Ritz

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Stochastic epigenetic mutations (SEM) reflect a deviation from normal site-specific methylation patterns. Epigenetic mutation load (EML) captures the accumulation of SEMs across an individual's genome and may reflect dysfunction of the epigenetic maintenance system in response to epigenetic challenges. Objective: We investigate whether EML is associated with PD risk and time to events (i.e., death and motor symptom decline). Methods: We employed logistic regression and Cox proportional hazards regression to assess the association between EML and several outcomes. Our analyses are based on 568 PD patients and 238 controls from the Parkinson's disease, Environment and Genes (PEG) study, for whom blood-based methylation data was available. Results: We found an association for PD onset and EML in all genes (OR = 1.90; 95%CI 1.52-2.37) and PD-related genes (OR = 1.87; 95%CI 1.50-2.32). EML was also associated with time to a minimum score of 35 points on the motor UPDRS exam (OR = 1.28; 95%CI 1.06-1.56) and time to death (OR = 1.29, 95%CI 1.11-1.49). An analysis of PD related genes only revealed five intragenic hotspots of high SEM density associated with PD risk. Conclusion: Our findings suggest an enrichment of methylation dysregulation in PD patients in general and specifically in five PD related genes. EML may also be associated with time to death and motor symptom progression in PD patients.

Original languageEnglish (US)
Pages (from-to)545-556
Number of pages12
JournalJournal of Parkinson's Disease
Volume12
Issue number2
DOIs
StatePublished - 2022

Keywords

  • epigenetics
  • Parkinson's disease

ASJC Scopus subject areas

  • Clinical Neurology
  • Cellular and Molecular Neuroscience

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