Characterization of Parkinson’s disease using blood-based biomarkers: A multicohort proteomic analysis
Authors: Marijan Posavi, Maria Diaz-Ortiz, Benjamine Liu, Christine R. Swanson, R. Tyler Skrinak, Pilar Hernandez-Con, Defne A. Amado, Michelle Fullard, Jacqueline Rick, Andrew Siderowf, Daniel Weintraub, Leo McCluskey, John Q. Trojanowski, Richard B. Dewey, Xuemei Huang, Alice S. Chen-Plotkin
Journal: PLoS Medicine (2019)

Background Parkinson's disease (PD) is a progressive neurodegenerative disease affecting about 5 million people worldwide with no disease-modifying therapies. We sought blood-based biomarkers in order to provide molecular characterization of individuals with PD for diagnostic confirmation and prediction of progression. Methods and findings In 141 plasma samples (96 PD, 45 neurologically normal control [NC] individuals; 45.4% female, mean age 70.0 years) from a longitudinally followed Discovery Cohort based at the University of Pennsylvania (UPenn), we measured levels of 1,129 proteins using an aptamer-based platform. We modeled protein plasma concentration (log10 of relative fluorescence units [RFUs]) as the effect of treatment group (PD versus NC), age at plasma collection, sex, and the levodopa equivalent daily dose (LEDD), deriving first-pass candidate protein biomarkers based on p-value for PD versus NC. These candidate proteins were then ranked by Stability Selection. We confirmed findings from our Discovery Cohort in a Replication Cohort of 317 individuals (215 PD, 102 NC; 47.9% female, mean age 66.7 years) from the multisite, longitudinally followed National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) Cohort. Analytical approach in the Replication Cohort mirrored the approach in the Discovery Cohort: each protein plasma concentration (log10 of RFU) was modeled as the effect of group (PD versus NC), age at plasma collection, sex, clinical site, and batch. Of the top 10 proteins from the Discovery Cohort ranked by Stability Selection, four associations were replicated in the Replication Cohort. These blood-based biomarkers were bone sialoprotein (BSP, Discovery false discovery rate [FDR]-corrected p = 2.82 × 10−2, Replication FDR-corrected p = 1.03 × 10−4), osteomodulin (OMD, Discovery FDR-corrected p = 2.14 × 10−2, Replication FDR-corrected p = 9.14 × 10−5), aminoacylase-1 (ACY1, Discovery FDR-corrected p = 1.86 × 10−3, Replication FDR-corrected p = 2.18 × 10−2), and growth hormone receptor (GHR, Discovery FDR-corrected p = 3.49 × 10−4, Replication FDR-corrected p = 2.97 × 10−3). Measures of these proteins were not significantly affected by differences in sample handling, and they did not change comparing plasma samples from 10 PD participants sampled both on versus off dopaminergic medication. Plasma measures of OMD, ACY1, and GHR differed in PD versus NC but did not differ between individuals with amyotrophic lateral sclerosis (ALS, n = 59) versus NC. In the Discovery Cohort, individuals with baseline levels of GHR and ACY1 in the lowest tertile were more likely to progress to mild cognitive impairment (MCI) or dementia in Cox proportional hazards analyses adjusting for age, sex, and disease duration (hazard ratio [HR] 2.27 [95% CI 1.04–5.0, p = 0.04] for GHR, and HR 3.0 [95% CI 1.24–7.0, p = 0.014] for ACY1). GHR's association with cognitive decline was confirmed in the Replication Cohort (HR 3.6 [95% CI 1.20–11.1, p = 0.02]). The main limitations of this study were its reliance on the aptamer-based platform for protein measurement and limited follow-up time available for some cohorts. Conclusions In this study, we found that the blood-based biomarkers BSP, OMD, ACY1, and GHR robustly associated with PD across multiple clinical sites. Our findings suggest that biomarkers based on a peripheral blood sample may be developed for both disease characterization and prediction of future disease progression in PD.