Abstract

Introduction: The aim of this study was to examine the potential application of a targeted proteomic predictive biomarker comprised predominantly of inflammatory proteins in distinguishing those who responded to a previously conducted clinical trial for Parkinson's disease (PD). Methods: Plasma samples obtained from a biorepository were assayed from a total of n = 520 DATATOP (Deprenyl And Tocopherol Antioxidative Therapy Of Parkinsonism) clinical trial participants across treatment arms. Support vector machine analyses were conducted to distinguish responder status on primary (need for Levodopa) and secondary trial endpoints (UPDRS Motor and Total Scores). Results: For the α-tocopherol and deprenyl placebo treatment arm (TOC), the targeted proteomic biomarker was able to distinguish responder status with an accuracy (area under the curve [AUC]) of 91% for the primary endpoint while it was 100% across secondary endpoints. For the deprenyl and α-tocopherol placebo treatment arm (DEP), the AUC was 93% for the primary endpoint and 99–100% for the secondary endpoints. For the combined treatment arm, AUC was 87% for the primary and 94–96% for the secondary endpoints. Discussion: The targeted proteomic predictive biomarker was highly accurate in distinguishing responder status across treatment arms thereby supporting the application of a precision medicine approach to treating PD.

Original languageEnglish
Pages (from-to)15-21
Number of pages7
JournalParkinsonism and Related Disorders
Volume94
DOIs
StatePublished - Jan 2022

Keywords

  • Parkinson's disease
  • Plasma
  • Precision medicine
  • Predicative biomarker
  • Support vector machine

Fingerprint

Dive into the research topics of 'Analysis of a precision medicine approach to treating Parkinson's disease: Analysis of the DATATOP study'. Together they form a unique fingerprint.

Cite this