Evidence map›Paper›PMID 41324773›Full record

SynthesisJournal of neurology2025

Cerebrospinal fluid biomarkers for diagnosis of Parkinson's disease: a systematic review and network meta-analysis.

Siming Li, Chen Yang, Jiayi Wu, Yuanchu Zheng, Zhenwei Yu, Genliang Liu, Yaqin Yang, Tao Feng

Abstract readSystematic ReviewNetwork Meta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Journal of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
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

8 authors.

Siming Li *Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Chen Yang *Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Jiayi Wu *Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Yuanchu ZhengCenter for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Zhenwei YuDepartment of Pathophysiology, Beijing Neurosurgical Institute, Beijing, China.
Genliang LiuCenter for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Yaqin YangCenter for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. yangyaqin1019@sina.com.
Tao FengCenter for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. bxbkyjs@sina.com.

Funding

Beijing Neurosurgical Institute 11000025T000003319495-3Natural Science Foundation 82401480
6 · The paper itself

Abstract

objectivesThere are multiple cerebrospinal fluid (CSF) biomarkers with potential for distinguishing Parkinson's disease (PD) from atypical parkinsonian syndromes (APSs) and controls; however, consensus on their diagnostic performance remains limited. This study aims to systematically evaluate and rank the diagnostic accuracy of CSF biomarkers in differentiating PD from APS and controls through network meta-analysis, determining the clinical diagnostic yield of these biomarkers and whether they should be considered as first-line diagnostic and differentiating tools.

methodsA comprehensive research was performed on PubMed, Web of Science, Embase and Cochrane library from inception until December 31st, 2024. Random effects models for sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and 95% CIs were used to calculate test accuracy. In addition, summary receiver operating characteristic (SROC) curves were used to summarize the overall diagnostic performance. The network meta-analysis based on the ANOVA model and surface under the cumulative ranking curve (SUCRA) scores were selected to rank the diagnostic performance of the included biomarkers by calculating the relative sensitivity, specificity, and diagnostic odds ratio (DOR).

resultsSeventy eligible studies containing 4925 PD patients, 698 multiple system atrophy (MSA), 177 progressive supranuclear palsy (PSP), 78 dementia with Lewy body (DLB) and 3072 healthy controls (HCs) or non-neurological controls (NNCs) were included in our meta-analysis. CSF Alpha-synuclein seed amplification assays (α-syn SAAs) showed high diagnostic accuracy with pooled sensitivity of 0.91 (95% CI 0.89-0.92) and specificity of 0.95 (95% CI 0.94-0.96) in distinguishing PD from HC or NNCs. The Area Under the Curve (AUC) of the SROC curve of CSF Neurofilament Light Chain (NfL) was 0.91, which effectively distinguished between PD and MSA. Both α-syn SAAs and NfL demonstrated similarly high diagnostic accuracy for differentiating PD from PSP. Only CSF Aβ

conclusionsCSF α-syn SAAs could serve as promising biomarkers for distinguishing PD from PSP and HCs or NNCs. Meanwhile, NfL is suitable for differentiating PD from MSA and PSP. In addition, the confirmation of these CSF biomarkers, as well as the discovery of new biomarkers, requires extensive study in large, independent cohorts. The main limitations of this analysis are the reliance on evidence derived from case-control studies, which are susceptible to bias, and the high level of heterogeneity observed for some biomarkers.

Indexed as

BiomarkersParkinson DiseaseHumansParkinsonian DisordersBiomarkersBiomarkerCerebrospinal fluidDiagnostic test accuracyMeta-analysisParkinson's disease

Identifiers

PMID41324773

What Socratic holds

Textmetadata
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 Socratic graph.