Characterization of Parkinson's disease using blood-based biomarkers: A multicohort proteomic analysis

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

Research output: Contribution to journalArticle

Abstract

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 FDRcorrected 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.

Original languageEnglish (US)
Article numbere1002931
JournalPLoS medicine
Volume16
Issue number10
DOIs
StatePublished - Jan 1 2019

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Proteomics
Parkinson Disease
Biomarkers
Proteins
Blood Proteins
Fluorescence
National Institute of Neurological Disorders and Stroke
Integrin-Binding Sialoprotein
Somatotropin Receptors
Amyotrophic Lateral Sclerosis
Levodopa
Neurodegenerative Diseases
Dementia
Disease Progression

All Science Journal Classification (ASJC) codes

  • Medicine(all)

Cite this

Posavi, M., Diaz-Ortiz, M., Liu, B., Swanson, C. R., Skrinak, R. T., Hernandez-Con, P., ... Chen-Plotkin, A. S. (2019). Characterization of Parkinson's disease using blood-based biomarkers: A multicohort proteomic analysis. PLoS medicine, 16(10), [e1002931]. https://doi.org/10.1371/journal.pmed.1002931
Posavi, Marijan ; Diaz-Ortiz, Maria ; Liu, Benjamine ; Swanson, Christine R. ; Skrinak, R. Tyler ; Hernandez-Con, Pilar ; Amado, Defne A. ; Fullard, Michelle ; Rick, Jacqueline ; Siderowf, Andrew ; Weintraub, Daniel ; McCluskey, Leo ; Trojanowski, John Q. ; Dewey, Richard B. ; Huang, Xuemei ; Chen-Plotkin, Alice S. / Characterization of Parkinson's disease using blood-based biomarkers : A multicohort proteomic analysis. In: PLoS medicine. 2019 ; Vol. 16, No. 10.
@article{a3d962b3dee04f76bad56362df546970,
title = "Characterization of Parkinson's disease using blood-based biomarkers: A multicohort proteomic analysis",
abstract = "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 FDRcorrected 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.",
author = "Marijan Posavi and Maria Diaz-Ortiz and Benjamine Liu and Swanson, {Christine R.} and Skrinak, {R. Tyler} and Pilar Hernandez-Con and Amado, {Defne A.} and Michelle Fullard and Jacqueline Rick and Andrew Siderowf and Daniel Weintraub and Leo McCluskey and Trojanowski, {John Q.} and Dewey, {Richard B.} and Xuemei Huang and Chen-Plotkin, {Alice S.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1371/journal.pmed.1002931",
language = "English (US)",
volume = "16",
journal = "PLoS Medicine",
issn = "1549-1277",
publisher = "Public Library of Science",
number = "10",

}

Posavi, M, Diaz-Ortiz, M, Liu, B, Swanson, CR, Skrinak, RT, Hernandez-Con, P, Amado, DA, Fullard, M, Rick, J, Siderowf, A, Weintraub, D, McCluskey, L, Trojanowski, JQ, Dewey, RB, Huang, X & Chen-Plotkin, AS 2019, 'Characterization of Parkinson's disease using blood-based biomarkers: A multicohort proteomic analysis', PLoS medicine, vol. 16, no. 10, e1002931. https://doi.org/10.1371/journal.pmed.1002931

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

In: PLoS medicine, Vol. 16, No. 10, e1002931, 01.01.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Characterization of Parkinson's disease using blood-based biomarkers

T2 - A multicohort proteomic analysis

AU - Posavi, Marijan

AU - Diaz-Ortiz, Maria

AU - Liu, Benjamine

AU - Swanson, Christine R.

AU - Skrinak, R. Tyler

AU - Hernandez-Con, Pilar

AU - Amado, Defne A.

AU - Fullard, Michelle

AU - Rick, Jacqueline

AU - Siderowf, Andrew

AU - Weintraub, Daniel

AU - McCluskey, Leo

AU - Trojanowski, John Q.

AU - Dewey, Richard B.

AU - Huang, Xuemei

AU - Chen-Plotkin, Alice S.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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 FDRcorrected 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.

AB - 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 FDRcorrected 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.

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Posavi M, Diaz-Ortiz M, Liu B, Swanson CR, Skrinak RT, Hernandez-Con P et al. Characterization of Parkinson's disease using blood-based biomarkers: A multicohort proteomic analysis. PLoS medicine. 2019 Jan 1;16(10). e1002931. https://doi.org/10.1371/journal.pmed.1002931