Evidence map›Paper›PMID 33154531›Full record

ArticleScientific reports2020

Clustering of Alzheimer's and Parkinson's disease based on genetic burden of shared molecular mechanisms.

Mohammad Asif Emon, Ashley Heinson, Ping Wu, Daniel Domingo-Fernández, Meemansa Sood, Henri Vrooman, Jean-Christophe Corvol, Phil Scordis, Martin Hofmann-Apitius, Holger Fröhlich

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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.

Mohammad Asif EmonFraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany.
Ashley HeinsonUCB Pharma (UCB Celltech Ltd.), 208 Bath Road, Slough, SL1 3WE, Berkshire, UK.
Ping WuUCB Pharma (UCB Celltech Ltd.), 208 Bath Road, Slough, SL1 3WE, Berkshire, UK.
Daniel Domingo-FernándezFraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany.
Meemansa SoodFraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany.
Henri VroomanDepartment of Radiology and Nuclear Medicine, Department of Medical Informatics, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
Jean-Christophe CorvolICM - Hôpital Pitié Salpêtrière, 47, bd de l'hôpital, 75013, Paris, France.
Phil ScordisUCB Pharma (UCB Celltech Ltd.), 208 Bath Road, Slough, SL1 3WE, Berkshire, UK.
Martin Hofmann-ApitiusFraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany.
Holger FröhlichFraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany. holger.froehlich@scai.fraunhofer.de.

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer's (AD) and Parkinson's Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important step towards a molecular mechanism-based taxonomy of neurological disorders, which could help in developing better targeted therapies in the future by going beyond classical phenotype based disease definitions.

Indexed as

AgedAged, 80 and overAlzheimer DiseaseAmyloid beta-PeptidesBrainCluster AnalysisCohort StudiesDrug DevelopmentEpigenomeFemaleGenotypeHumansMaleMiddle AgedNeuroimagingOutcome Assessment, Health CareAmyloid beta-Peptides

Identifiers

PMID33154531
PMCPMC7645798

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Registered trials

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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.