Evidence map›Paper›PMID 25261568›Full record

ArticleDisease models & mechanisms2014

Predictive markers of clinical outcome in the GRMD dog model of Duchenne muscular dystrophy.

Inès Barthélémy, Fernanda Pinto-Mariz, Erica Yada, Loïc Desquilbet, Wilson Savino, Suse Dayse Silva-Barbosa, Anne-Marie Faussat, Vincent Mouly, Thomas Voit, Stéphane Blot and 1 more

Open access · goldAbstract read
In one paragraph

Article in Disease models & mechanisms, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

21 citing papers in PubMed, 37 citations in OpenAlex.

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  18. [Research progress on disease models and gene therapy of Duchenne muscular dystrophy].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2016
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 7 institutions in 2 countries.

Inès BarthélémyUniversité Paris-Est, Ecole Nationale Vétérinaire d'Alfort, UPR de Neurobiologie, 94704 Maisons-Alfort, France.
Fernanda Pinto-MarizUniversité Pierre et Marie Curie-Paris 06 UM76, INSERM U974, CNRS UMR 7215, Institut de Myologie, Paris, France. Institute of Pediatrics/Federal University of Rio de Janeiro (IPPMG/UFRJ), Rio de Janeiro 21941-912, Brazil. Laboratory of Thymus Research, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil.
Erica YadaUniversité Pierre et Marie Curie-Paris 06 UM76, INSERM U974, CNRS UMR 7215, Institut de Myologie, Paris, France.
Loïc DesquilbetUniversité Paris-Est, Ecole Nationale Vétérinaire d'Alfort, Unité D'Épidémiologie clinique et de Biostatistique, 94704 Maisons-Alfort, France. CNRS UMR 7179, MNHN, Brunoy 91800, France.
Wilson SavinoLaboratory of Thymus Research, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil.
Suse Dayse Silva-BarbosaDepartment of Clinical Research, National Cancer Institute (INCA), Rio de Janeiro 20230-130, Brazil.
Anne-Marie FaussatUniversité Pierre et Marie Curie-Paris 06 IFR 65 Saint-Antoine, Paris 75005, France.
Vincent MoulyUniversité Pierre et Marie Curie-Paris 06 UM76, INSERM U974, CNRS UMR 7215, Institut de Myologie, Paris, France.
Thomas VoitUniversité Pierre et Marie Curie-Paris 06 UM76, INSERM U974, CNRS UMR 7215, Institut de Myologie, Paris, France.
Stéphane BlotUniversité Paris-Est, Ecole Nationale Vétérinaire d'Alfort, UPR de Neurobiologie, 94704 Maisons-Alfort, France. sblot@vet-alfort.fr gillian.butler-browne@upmc.fr.
Gillian Butler-BrowneUniversité Pierre et Marie Curie-Paris 06 UM76, INSERM U974, CNRS UMR 7215, Institut de Myologie, Paris, France. sblot@vet-alfort.fr gillian.butler-browne@upmc.fr.
Sorbonne Université · FRBipar · FRÉcole Nationale Vétérinaire d'Alfort · FRFundação Oswaldo Cruz · BRInstitut de Myologie · FRInstituto Nacional do Câncer · BRPitié-Salpêtrière Hospital · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the translational process of developing innovative therapies for DMD (Duchenne muscular dystrophy), the last preclinical validation step is often carried out in the most relevant animal model of this human disease, namely the GRMD (Golden Retriever muscular dystrophy) dog. The disease in GRMD dogs mimics human DMD in many aspects, including the inter-individual heterogeneity. This last point can be seen as a drawback for an animal model but is inherently related to the disease in GRMD dogs closely resembling that of individuals with DMD. In order to improve the management of this inter-individual heterogeneity, we have screened a combination of biomarkers in sixty-one 2-month-old GRMD dogs at the onset of the disease and a posteriori we addressed their predictive value on the severity of the disease. Three non-invasive biomarkers obtained at early stages of the disease were found to be highly predictive for the loss of ambulation before 6 months of age. An elevation in the number of circulating CD4(+)CD49d(hi) T cells and a decreased stride frequency resulting in a reduced spontaneous speed were found to be strongly associated with the severe clinical form of the disease. These factors can be used as predictive tests to screen dogs to separate them into groups with slow or fast disease progression before their inclusion into a therapeutic preclinical trial, and therefore improve the reliability and translational value of the trials carried out on this invaluable large animal model. These same biomarkers have also been described to be predictive for the time to loss of ambulation in boys with DMD, strengthening the relevance of GRMD dogs as preclinical models of this devastating muscle disease.

Indexed as

Disease Models, AnimalAnimalsBiomarkersDogsImmunophenotypingMuscular Dystrophy, DuchenneBiomarkersAccelerometryCD49dDMDDogDystrophinGait analysisGRMDLymphocytePredictive biomarker

Identifiers

PMID25261568
PMCPMC4213729
OpenAlexW2113831939

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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.