Evidence map›Paper›PMID 35250451›Full record

ArticleFrontiers in neuroscience2022

A Panel of Plasma Biomarkers for Differential Diagnosis of Parkinsonian Syndromes.

Qi Li, Zhen Li, Xiaoxuan Han, Xiao Shen, Fei Wang, Lipeng Bai, Zhuo Li, Rui Zhang, Yanlin Wang, Xiaodong Zhu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 3 pooled it
3.2field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 28 citations in OpenAlex.

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  7. Fluid biomarkers in atypical Parkinsonism: current state and future perspectives.Journal of neural transmission (Vienna, Austria : 1996) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Qi LiDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Zhen LiDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Xiaoxuan HanDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Xiao ShenDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Fei WangDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Lipeng BaiDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Zhuo LiDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Rui ZhangDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Yanlin WangDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Xiaodong ZhuDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
Tianjin Medical University General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe 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, β-amyloid 42 (Aβ42) and β-amyloid 40 (Aβ40), and investigated the diagnostic value of these biomarkers.

methodsA 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, Aβ42, and Aβ40 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.

resultsA combination of α-syn, Aβ42, Aβ40, Aβ42/40, 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 Aβ42 or Aβ40 or Aβ42/40, 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, Aβ42, 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, Aβ42, Aβ40, and Aβ42/40 was the best discriminating panel. Correlation analysis suggests that plasma Aβ42 levels were positively correlated to UPDRS part III scores in MSA. In terms of cognitive function, there was a relationship between plasma Aβ42/40 level and MMSE scores in patients with APD.

conclusionIn 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.

Indexed as

biomarkerneurofilament light chainpanelParkinsonian syndromesParkinson’s disease

Identifiers

PMID35250451
PMCPMC8891509
OpenAlexW4213076694

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.