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A Panel of Plasma Biomarkers for Differential Diagnosis of Parkinsonian Syndromes.
Li, Qi; Li, Zhen; Han, Xiaoxuan; Shen, Xiao; Wang, Fei; Bai, Lipeng; Li, Zhuo; Zhang, Rui; Wang, Yanlin; Zhu, Xiaodong.
  • Li Q; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
  • Li Z; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
  • Han X; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
  • Shen X; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
  • Wang F; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
  • Bai L; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
  • Li Z; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
  • Zhang R; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
  • Wang Y; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
  • Zhu X; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Front Neurosci ; 16: 805953, 2022.
Article en En | MEDLINE | ID: mdl-35250451
ABSTRACT

OBJECTIVE:

The aim of our study is to explore the most reliable panel of plasma biomarkers for differential diagnosis of parkinsonian syndromes (PDSs). We selected five kinds of neurodegenerative proteins in plasma neurofilament light chain (NfL), α-synuclein (α-syn), total tau, ß-amyloid 42 (Aß42) and ß-amyloid 40 (Aß40), and investigated the diagnostic value of these biomarkers.

METHODS:

A total of 99 plasma samples from patients with Parkinson's disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy, and age-matched healthy controls (HCs) were enrolled in our study. Plasma NfL, α-syn, total tau, Aß42, and Aß40 levels were quantified by ultrasensitive single molecule array immunoassay. We used logistic regression analyses to examine diagnostic accuracy of these plasma biomarkers. Disease severity was assessed by the modified Hoehn and Yahr staging scale, Unified Parkinson's Disease Rating Scale part III (UPDRS III), and the Mini-Mental State Examination (MMSE), and subsequently, correlation analysis was performed.

RESULTS:

A combination of α-syn, Aß42, Aß40, Aß42/40, and NfL could achieve a best diagnostic value in differentiating PDSs from HC and PD from HC, with an AUC of 0.983 and 0.977, respectively. By adding NfL to measurements of α-syn or Aß42 or Aß40 or Aß42/40, the best discriminating panel was formed in differentiating atypical parkinsonian disorder (APD) and HC, and the discriminatory potential could reach a sensitivity of 100% and specificity of 100% (AUC = 1.000). For further distinguishing PD from APD, we found a combination of NfL, Aß42, and total tau was the most reliable panel with equally high diagnostic accuracy. With respect to differentiating the subtypes of APD from one another, our results revealed that measurement of NfL, total tau, Aß42, Aß40, and Aß42/40 was the best discriminating panel. Correlation analysis suggests that plasma Aß42 levels were positively correlated to UPDRS part III scores in MSA. In terms of cognitive function, there was a relationship between plasma Aß42/40 level and MMSE scores in patients with APD.

CONCLUSION:

In our study, various combinations of plasma biomarkers have great potentialities in identifying PDSs, with important clinical utility in improving diagnostic accuracy. Plasma NfL may have added value to a blood-based biomarker panel for differentiating PDSs.
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Article