Evidence map›Paper›PMID 34993845›Full record

ArticleMolecular neurobiology2022

Strong Predictive Algorithm of Pathogenesis-Based Biomarkers Improves Parkinson's Disease Diagnosis.

Daniel Kam Yin Chan, Nady Braidy, Ren Fen Chen, Ying Hua Xu, Steven Bentley, Michal Lubomski, Ryan L Davis, Jack Chen, Carolyn M Sue, George D Mellick

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Molecular neurobiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 29% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  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

10 authors at 5 institutions in 1 country.

Daniel Kam Yin ChanUniversity of New South Wales, Kensington, Australia. d.chan@unsw.edu.au.ORCID http://orcid.org/0000-0003-0481-3933
Nady BraidyUniversity of New South Wales, Kensington, Australia.
Ren Fen ChenCentral Sydney Immunology Laboratory, NSW Health Pathology at Royal Prince Alfred Hospital, Camperdown, NSW, Australia.
Ying Hua XuUniversity of New South Wales, Kensington, Australia.
Steven BentleyGriffith University, Brisbane, Queensland, Australia.
Michal LubomskiKolling Institute of Medical Research, University of Sydney, Sydney, Australia.
Ryan L DavisKolling Institute of Medical Research, University of Sydney, Sydney, Australia.
Jack ChenUniversity of New South Wales, Kensington, Australia.
Carolyn M SueKolling Institute of Medical Research, University of Sydney, Sydney, Australia.
George D MellickGriffith University, Brisbane, Queensland, Australia.
The University of Sydney · AUBankstown Lidcombe Hospital · AUGriffith University · AUUNSW Sydney · AURoyal Prince Alfred Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Easily accessible and accurate biomarkers can aid Parkinson's disease diagnosis. We investigated whether combining plasma levels of α-synuclein, anti-α-synuclein, and/or their ratios to amyloid beta-40 correlated with clinical diagnosis. The inclusion of amyloid beta-40 (Aβ40) is novel. Plasma levels of biomarkers were quantified with ELISA. Using receiver operating characteristic (ROC) curve analysis, levels of α-synuclein, anti-α-synuclein, and their ratios with Aβ40 were analyzed in an initial training set of cases and controls. Promising biomarkers were then used to build a diagnostic algorithm. Verification of the results of biomarkers and the algorithm was performed in an independent set. The training set consisted of 50 cases (age 65.2±9.3, range 44-83, female:male=21:29) with 50 age- and gender-matched controls (67.1±10.0, range 45-96 years; female:male=21:29). ROC curve analysis yielded the following area under the curve results: anti-α-synuclein=0.835, α-synuclein=0.738, anti-α-synuclein/Aβ40=0.737, and α-synuclein/Aβ40=0.663. A 2-step diagnostic algorithm was built: either α-synuclein or anti-α-synuclein was ≥2 times the means of controls (step-1), resulting in 74% sensitivity; and adding α-synuclein/Aβ40 or anti-α-synuclein/Aβ40 (step-2) yielded better sensitivity (82%) while using step-2 alone yielded good specificity in controls (98%). The results were verified in an independent sample of 46 cases and 126 controls, with sensitivity reaching 91.3% and specificity 90.5%. The algorithm was equally sensitive in Parkinson's disease of ≤5-year duration with 92.6% correctly identified in the training set and 90% in the verification set. With two independent samples totaling 272 subjects, our study showed that combination of biomarkers of α-synuclein, anti-α-synuclein, and their ratios to Aβ40 showed promising sensitivity and specificity.

Indexed as

Parkinson DiseaseAdultAgedAged, 80 and overAlgorithmsalpha-SynucleinAmyloid beta-PeptidesBiomarkersFemaleHumansMaleMiddle AgedROC Curvealpha-SynucleinAmyloid beta-PeptidesBiomarkersAlpha-synucleinAmyloid-beta40BiomarkersDiagnosisParkinson’s disease

Identifiers

PMID34993845
OpenAlexW4206028090

What OpenQuestion 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 OpenQuestion graph.