Potential two-step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study

Sid E. O'Bryant; Melissa Edwards; Fan Zhang; Leigh A. Johnson; James Hall; Yuliya Kuras; Clemens R. Scherzer

doi:10.1016/j.dadm.2019.03.001

Potential two-step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study

Sid E. O'Bryant, Melissa Edwards, Fan Zhang, Leigh A. Johnson, James Hall, Yuliya Kuras, Clemens R. Scherzer

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Introduction: We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases. Methods: Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms. Results: The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal. Discussion: These data provide evidence of the potential use of a multitiered blood-based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.

Original language	English
Pages (from-to)	374-382
Number of pages	9
Journal	Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume	11
DOIs	https://doi.org/10.1016/j.dadm.2019.03.001
State	Published - Dec 2019

Keywords

Blood biomarkers
Diagnostic accuracy
Parkinson's disease
Precision medicine
Proteomics

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title = "Potential two-step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study",

abstract = "Introduction: We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases. Methods: Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms. Results: The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal. Discussion: These data provide evidence of the potential use of a multitiered blood-based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.",

keywords = "Blood biomarkers, Diagnostic accuracy, Parkinson's disease, Precision medicine, Proteomics",

author = "O'Bryant, {Sid E.} and Melissa Edwards and Fan Zhang and Johnson, {Leigh A.} and James Hall and Yuliya Kuras and Scherzer, {Clemens R.}",

year = "2019",

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language = "English",

volume = "11",

pages = "374--382",

journal = "Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring",

issn = "2352-8729",

publisher = "Elsevier BV",

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Potential two-step proteomic signature for Parkinson's disease : Pilot analysis in the Harvard Biomarkers Study. / O'Bryant, Sid E.; Edwards, Melissa; Zhang, Fan ; Johnson, Leigh A.; Hall, James; Kuras, Yuliya; Scherzer, Clemens R.

In: Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, Vol. 11, 12.2019, p. 374-382.

Research output: Contribution to journal › Article › peer-review

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T2 - Pilot analysis in the Harvard Biomarkers Study

AU - O'Bryant, Sid E.

AU - Edwards, Melissa

AU - Zhang, Fan

AU - Johnson, Leigh A.

AU - Hall, James

AU - Kuras, Yuliya

AU - Scherzer, Clemens R.

PY - 2019/12

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N2 - Introduction: We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases. Methods: Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms. Results: The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal. Discussion: These data provide evidence of the potential use of a multitiered blood-based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.

AB - Introduction: We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases. Methods: Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms. Results: The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal. Discussion: These data provide evidence of the potential use of a multitiered blood-based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.

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