Evidence map›Paper›PMID 41203629›Full record

ReviewCell death & disease2025

Are we ready for a biological diagnosis of Parkinson's Disease?

Angelo Tiziano Cimmino, Giulia Di Lazzaro, Antonio Pisani, Anna Rita Bentivoglio, Paolo Calabresi

Abstract readReview
In one paragraph

Review in Cell death & disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. 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

5 authors.

Angelo Tiziano CimminoDepartment of Neurosciences, Università Cattolica del Sacro Cuore, Rome, Italy.ORCID http://orcid.org/0000-0001-9298-565X
Giulia Di LazzaroDepartment of Neurosciences, Università Cattolica del Sacro Cuore, Rome, Italy.ORCID http://orcid.org/0000-0003-2413-1788
Antonio PisaniDepartment of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.ORCID http://orcid.org/0000-0002-8432-594X
Anna Rita BentivoglioDepartment of Neurosciences, Università Cattolica del Sacro Cuore, Rome, Italy.
Paolo CalabresiDepartment of Neurosciences, Università Cattolica del Sacro Cuore, Rome, Italy. paolo.calabresi@policlinicogemelli.it.ORCID http://orcid.org/0000-0003-0326-5509

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The diagnosis of Parkinson's disease (PD) is currently based on clinical criteria, centered on the characteristic motor syndrome. However, motor manifestations become evident only after a significant proportion of nigro-striatal dopaminergic neurons have already undergone neurodegeneration. The recent "NSD-ISS" and "SynNeurGe" research frameworks have proposed new biological diagnostic criteria focusing on α-synucleinopathy, neurodegeneration, and genetic biomarkers, independent of clinical manifestations. These proposals intend to detect the disease at a "biologically early phase" to foster advances in research and development of disease-modifying treatments. While the shift to a biological approach is mandatory to better understand PD, challenges to the new frameworks remain, including inherent criticisms, limitations in explaining PD clinical-biological complexity, restricted clinical applicability and related ethical concerns. In this paper, we describe the historical path toward the two biological proposals, explore critical issues and knowledge gaps emerging from them, and discuss the risk of their premature application in the clinical setting.

Indexed as

Parkinson Diseasealpha-SynucleinBiomarkersHumansalpha-SynucleinBiomarkers

Identifiers

PMID41203629
PMCPMC12594944

What OpenQuestion holds

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LicenceCC BY
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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.