Literature DB >> 31190374

The early diagnosis of Parkinson's disease through combined biomarkers.

Xin-Qiao Chen1, Jian-Ping Niu1, Rui-Qiang Peng1, Ye-Hua Song1, Na Xu1, Yi-Wen Zhang1.   

Abstract

OBJECTIVE: This study primarily aims to explore the value of combining the measurement of plasma α-synuclein oligomer levels with enhanced T2 star-weighted angiography (ESWAN) in the early diagnosis of Parkinson's disease.
METHODS: Sixty patients with early Parkinson's disease and 30 normal adults, with similar ages and genders, were enrolled in the study. Their levels of plasma α-synuclein oligomers were measured, and ESWAN was performed. The amplitudes, phases and R2* values of the head, body and tail of the ipsilateral and contralateral substantia nigra pars compacta (SNc) were measured, at the side of the limb with severe symptoms or early symptoms. The receiver operating characteristic (ROC) curve was used to explore the value of these indexes in the early diagnosis of Parkinson's disease.
RESULTS: The plasma level of α-synuclein oligomer was significantly higher in the experimental group than in the control group (P < 0.05). The amplitude values of the head and tail of contralateral SNcs were significantly lower in the experimental group than in the control group (P < 0.05). In the single-index assessment, the serum α-synuclein oligomer had the highest specificity (70%), while the sensitivity of the amplitude of the head and tail of the contralateral SNc was 75% and 80%, respectively. The area under the curve, for the combination of these three indicators, was 0.827, diagnostic efficiency was particularly high, and sensitivity and specificity both reached 80%.
CONCLUSION: The combined detection of plasma α-synuclein oligomer and amplitude of the head and tail of the SNc has high diagnostic specificity and sensitivity.
© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  Parkinson's disease; early diagnosis; iron deposition; α-synuclein

Mesh:

Substances:

Year:  2019        PMID: 31190374     DOI: 10.1111/ane.13140

Source DB:  PubMed          Journal:  Acta Neurol Scand        ISSN: 0001-6314            Impact factor:   3.209


  4 in total

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4.  An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters.

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Journal:  PLoS One       Date:  2021-02-19       Impact factor: 3.240

  4 in total

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