Evidence map›Paper›PMID 32518535›Full record

ReviewClinical proteomics2020

A comprehensive systematic review of CSF proteins and peptides that define Alzheimer's disease.

Cristina M Pedrero-Prieto, Sonia García-Carpintero, Javier Frontiñán-Rubio, Emilio Llanos-González, Cristina Aguilera García, Francisco J Alcaín, Iris Lindberg, Mario Durán-Prado, Juan R Peinado, Yoana Rabanal-Ruiz

Abstract readReview
In one paragraph

Review in Clinical proteomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 1 pooled it
–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

45 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. Analysis of the mechanism ofThe Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2024
    Article
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  14. Origins, Technological Advancement, and Applications of Peptidomics.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  15. Article
  16. Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023
    Review
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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.

Cristina M Pedrero-PrietoDepartment of Medical Sciences, Ciudad Real Medical School, Oxidative Stress and Neurodegeneration Group, Regional Center for Biomedical Research, University of Castilla-La Mancha, Ciudad Real, Spain.
Sonia García-CarpinteroDepartment of Medical Sciences, Ciudad Real Medical School, Oxidative Stress and Neurodegeneration Group, Regional Center for Biomedical Research, University of Castilla-La Mancha, Ciudad Real, Spain.
Javier Frontiñán-RubioDepartment of Medical Sciences, Ciudad Real Medical School, Oxidative Stress and Neurodegeneration Group, Regional Center for Biomedical Research, University of Castilla-La Mancha, Ciudad Real, Spain.
Emilio Llanos-GonzálezDepartment of Medical Sciences, Ciudad Real Medical School, Oxidative Stress and Neurodegeneration Group, Regional Center for Biomedical Research, University of Castilla-La Mancha, Ciudad Real, Spain.
Cristina Aguilera GarcíaDepartment of Medical Sciences, Ciudad Real Medical School, Oxidative Stress and Neurodegeneration Group, Regional Center for Biomedical Research, University of Castilla-La Mancha, Ciudad Real, Spain.
Francisco J AlcaínDepartment of Medical Sciences, Ciudad Real Medical School, Oxidative Stress and Neurodegeneration Group, Regional Center for Biomedical Research, University of Castilla-La Mancha, Ciudad Real, Spain.
Iris LindbergDepartment of Anatomy and Neurobiology, University of Maryland School of Medicine, University of Maryland, Baltimore, MD 21201 USA.
Mario Durán-PradoDepartment of Medical Sciences, Ciudad Real Medical School, Oxidative Stress and Neurodegeneration Group, Regional Center for Biomedical Research, University of Castilla-La Mancha, Ciudad Real, Spain.
Juan R Peinado *Department of Medical Sciences, Ciudad Real Medical School, Oxidative Stress and Neurodegeneration Group, Regional Center for Biomedical Research, University of Castilla-La Mancha, Ciudad Real, Spain.
Yoana Rabanal-Ruiz *Department of Medical Sciences, Ciudad Real Medical School, Oxidative Stress and Neurodegeneration Group, Regional Center for Biomedical Research, University of Castilla-La Mancha, Ciudad Real, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDuring the last two decades, over 100 proteomics studies have identified a variety of potential biomarkers in CSF of Alzheimer's (AD) patients. Although several reviews have proposed specific biomarkers, to date, the statistical relevance of these proteins has not been investigated and no peptidomic analyses have been generated on the basis of specific up- or down- regulation. Herein, we perform an analysis of all unbiased explorative proteomics studies of CSF biomarkers in AD to critically evaluate whether proteins and peptides identified in each study are consistent in distribution; direction change; and significance, which would strengthen their potential use in studies of AD pathology and progression.

methodsWe generated a database containing all CSF proteins whose levels are known to be significantly altered in human AD from 47 independent, validated, proteomics studies. Using this database, which contains 2022 AD and 2562 control human samples, we examined whether each protein is consistently present on the basis of reliable statistical studies; and if so, whether it is over- or under-represented in AD. Additionally, we performed a direct analysis of available mass spectrometric data of these proteins to generate an AD CSF peptide database with 3221 peptides for further analysis.

resultsOf the 162 proteins that were identified in 2 or more studies, we investigated their enrichment or depletion in AD CSF. This allowed us to identify 23 proteins which were increased and 50 proteins which were decreased in AD, some of which have never been revealed as consistent AD biomarkers (i.e. SPRC or MUC18). Regarding the analysis of the tryptic peptide database, we identified 87 peptides corresponding to 13 proteins as the most highly consistently altered peptides in AD. Analysis of tryptic peptide fingerprinting revealed specific peptides encoded by CH3L1, VGF, SCG2, PCSK1N, FBLN3 and APOC2 with the highest probability of detection in AD.

conclusionsOur study reveals a panel of 27 proteins and 21 peptides highly altered in AD with consistent statistical significance; this panel constitutes a potent tool for the classification and diagnosis of AD.

Indexed as

Alzheimer´s DiseaseBiomarkersPeptidomicsProteomic

Identifiers

PMID32518535
PMCPMC7273668

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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.