Evidence map›Paper›PMID 33144621›Full record

ArticleScientific reports2020

Genealogy of the neurodegenerative diseases based on a meta-analysis of age-stratified incidence data.

Daniela Gerovska, Haritz Irizar, David Otaegi, Isidre Ferrer, Adolfo López de Munain, Marcos J Araúzo-Bravo

Open access · goldAbstract 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 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.1field-weighted citation impact, top 23% 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

11 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
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  5. Article
  6. Long Non-Coding RNAs: New Insights in Neurodegenerative Diseases.International journal of molecular sciences · 2024
    Review
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  8. Article
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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

6 authors at 3 institutions in 4 countries.

Daniela GerovskaComputational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, Calle Doctor Beguiristain S/N, 20014, San Sebastián, Spain.ORCID 0000-0003-0671-4277
Haritz IrizarComputational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, Calle Doctor Beguiristain S/N, 20014, San Sebastián, Spain.
David OtaegiInstituto Biodonostia-Hospital Universitario Donostia, San Sebastián, Gipuzkoa, Spain.
Isidre FerrerDepartamento de Patología y Terapéutica Experimental, Universidad de Barcelona, CIBERNED, Hospitalet de LLobregat, Barcelona, Spain.
Adolfo López de MunainInstituto Biodonostia-Hospital Universitario Donostia, San Sebastián, Gipuzkoa, Spain. adolfo.lopezdemunainarregui@osakidetza.eus.
Marcos J Araúzo-BravoComputational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, Calle Doctor Beguiristain S/N, 20014, San Sebastián, Spain. mararabra@yahoo.co.uk.ORCID 0000-0002-3264-464X
Biogipuzkoa Health Research Institute · ESBiomedical Research Networking Center on Neurodegenerative Diseases · ESIkerbasque · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While the central common feature of the neurodegenerative diseases (NDs) is the accumulation of misfolded proteins, they share other pathogenic mechanisms. However, we miss an explanation for the onset of the NDs. The mechanisms through which genetic mutations, present from conception are expressed only after several decades of life are unknown. We aim to find clues on the complexity of the disease onset trigger of the different NDs expressed in the number of steps of factors related to a disease. We collected brain autopsies on diseased patients with NDs, and found a dynamic increase of the ND multimorbidity with the advance of age. Together with the observation that the NDs accumulate multiple misfolded proteins, and the same misfolded proteins are involved in more than one ND, motivated us to propose a model for a genealogical tree of the NDs. To collect the dynamic data needed to build the tree, we used a Big-data approach that searched automatically epidemiological datasets for age-stratified incidence of NDs. Based on meta-analysis of over 400 datasets, we developed an algorithm that checks whether a ND follows a multistep model, finds the number of steps necessary for the onset of each ND, finds the number of common steps with other NDs and the number of specific steps of each ND, and builds with these findings a parsimony tree of the genealogy of the NDs. The tree discloses three types of NDs: the stem NDs with less than 3 steps; the trunk NDs with 5 to 6 steps; and the crown NDs with more than 7 steps. The tree provides a comprehensive understanding of the relationship across the different NDs, as well as a mathematical framework for dynamic adjustment of the genealogical tree of the NDs with the appearance of new epidemiological studies and the addition of new NDs to the model, thus setting the basis for the search for the identity and order of these steps. Understanding the complexity, or number of steps, of factors related to disease onset trigger is important prior deciding to study single factors for a multiple steps disease.

Indexed as

MutationAdolescentAdultAgedAged, 80 and overAge FactorsAlgorithmsAutopsyBig DataChildChild, PreschoolFemaleGenetic Predisposition to DiseaseHumansIncidenceInfant

Identifiers

PMID33144621
PMCPMC7609593
OpenAlexW3096827638

What OpenQuestion holds

Textmetadata
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.