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Diagnosis of Parkinson's Disease Based on Disease-Specific Autoantibody Profiles in Human Sera

Overview of attention for article published in PLOS ONE, February 2012
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Title
Diagnosis of Parkinson's Disease Based on Disease-Specific Autoantibody Profiles in Human Sera
Published in
PLOS ONE, February 2012
DOI 10.1371/journal.pone.0032383
Pubmed ID
Authors

Min Han, Eric Nagele, Cassandra DeMarshall, Nimish Acharya, Robert Nagele

Abstract

Parkinson's disease (PD), hallmarked by a variety of motor disorders and neurological decline, is the second most common neurodegenerative disease worldwide. Currently, no diagnostic test exists to identify sufferers, and physicians must rely on a combination of subjective physical and neurological assessments to make a diagnosis. The discovery of definitive blood-borne biomarkers would be a major step towards early and reliable diagnosis. Despite attention devoted to this search, such biomarkers have remained elusive. In the present study, we used human protein microarrays to reveal serum autoantibodies that are differentially expressed among PD and control subjects. The diagnostic significance of each of these autoantibodies was evaluated, resulting in the selection of 10 autoantibody biomarkers that can effectively differentiate PD sera from control sera with a sensitivity of 93.1% and specificity of 100%. PD sera were also distinguishable from sera obtained from Alzheimer's disease, breast cancer, and multiple sclerosis patients with accuracies of 86.0%, 96.6%, and 100%, respectively. Results demonstrate that serum autoantibodies can be used as highly specific and accurate biomarkers for PD diagnosis throughout the course of the disease.

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Geographical breakdown

Country Count As %
South Africa 1 <1%
Unknown 121 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 18%
Researcher 22 18%
Student > Master 17 14%
Student > Bachelor 12 10%
Student > Postgraduate 8 7%
Other 20 16%
Unknown 21 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 23%
Medicine and Dentistry 22 18%
Biochemistry, Genetics and Molecular Biology 15 12%
Neuroscience 12 10%
Immunology and Microbiology 6 5%
Other 15 12%
Unknown 24 20%