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. 2012;7(2):e32383.
doi: 10.1371/journal.pone.0032383. Epub 2012 Feb 22.

Diagnosis of Parkinson's disease based on disease-specific autoantibody profiles in human sera

Affiliations

Diagnosis of Parkinson's disease based on disease-specific autoantibody profiles in human sera

Min Han et al. PLoS One. 2012.

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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Conflict of interest statement

Competing Interests: The authors have read the journal's policy and have the following conflicts: MH, NA and CD have no competing interests. EN is a paid consultant for Durin Technologies. RN is Founder of Durin Technologies and has stock interest. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Biomarker selection and training/testing analysis.
Before biomarker selection, our total sample pool was split into two randomized groups: the Training Set and Testing Set. Prospector and PAM statistical analyses were performed on the Training Set to identify the top 10 most significant autoantibody classifiers of PD and control. We then verified the diagnostic accuracy of these selected biomarkers by using Random Forest to predict sample classification in the Training Set, Testing Set, and then both sets combined.
Figure 2
Figure 2. Differential expression of identified PD-specific autoantibody biomarkers in PD and control sera.
Microarray fluorescence values reflecting individual serum autoantibody titers demonstrate the differences in the serum expression of the selected ten PD-specific autoantibody biomarkers in PD (n = 29) and control (n = 40) sera (A–I).

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