Evidence map›Paper›PMID 42189263›Full record

ArticleJournal of neurology2026

Identification of prognostic biomarkers in a large cohort of patients with LGMD R2.

Carla F Bolano-Diaz, Jose Verdu-Diaz, Dan Hao, Meredith K James, Laura Rufibach, Andrew Blamire, Harmen Reyngoudt, Pierre G Carlier, Heather Gordish-Dressman, Heather Hilsden and 15 more

Abstract read
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In one paragraph

Article in Journal of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

25 authors.

Carla F Bolano-Diaz *The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Central Parkway, Newcastle Upon Tyne, UK. carla.bolano-diaz@newcastle.ac.uk.ORCID http://orcid.org/0009-0006-3764-1073
Jose Verdu-Diaz *The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Central Parkway, Newcastle Upon Tyne, UK.
Dan HaoThe John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Central Parkway, Newcastle Upon Tyne, UK.
Meredith K JamesThe John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Central Parkway, Newcastle Upon Tyne, UK.
Laura RufibachThe Jain Foundation, Seattle, WA, USA.
Andrew BlamireNewcastle Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK.
Harmen ReyngoudtNMR Laboratory, Neuromuscular Investigation Centre, Institute of Myology, Paris, France.
Pierre G CarlierNMR Laboratory, Neuromuscular Investigation Centre, Institute of Myology, Paris, France.
Heather Gordish-DressmanCenter for Health Outcomes Research and Delivery Science, Division of Biostatistics and Study Methodology, Children's National Health System, Washington, DC, USA.
Heather HilsdenThe John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Central Parkway, Newcastle Upon Tyne, UK.
Simone SpulerCharite Muscle Research Unit, Experimental and Clinical Research Center, A Joint Cooperation of the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine, Berlin, Germany.
John DayDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Kristi J JonesThe Sydney Children's Hospital Network, and The University of Sydney, Child and Adolescent Health, Sydney, Australia.
Diana Bharucha-GoebelCenter for Gene Therapy, Nationwide Children's Hospital, Columbus, OH, USA.
Alan PestronkDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
Maggie C WalterFriedrich Baur Institute at the Department of Neurology, University Hospital, LMU Munich, Munich, Germany.
Carmen ParadasNeuromuscular Unit, Department of Neurology, Hospital U. Virgen del Rocío/Instituto de Biomedicina de Sevilla, Seville, Spain.
Tanya StojkovicAPHP, Reference Center for Neuromuscular Diseases, Pitié-Salpêtrière Hospital, Institute of Myology, Paris, France.
Madoka Mori-YoshimuraDepartment of Neurology, National Center Hospital, National Center of Neurology and Psychiatry Tokyo, Tokyo, Japan.
Elena BravverNeuroscience Institute, Carolinas Neuromuscular/ALS-MDA Center, Carolinas HealthCare System, Charlotte, NC, USA.
Elena PegoraroDepartment of Neuroscience, University of Padova, Padua, Italy.
Jerry MendellThe Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
Jain COS Consortium
Volker Straub *The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Central Parkway, Newcastle Upon Tyne, UK.
Jordi Diaz-Manera *The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Central Parkway, Newcastle Upon Tyne, UK.

Funding

Jain Foundation Jain COSJain Foundation Jain COS AI
6 · The paper itself

Abstract

backgroundLimb-girdle muscular dystrophy R2-dysferlin related (LGMD-R2) is a progressive muscle condition with marked variability in disease course, making prognosis challenging. Quantitative MRI (qMRI) has emerged as a complementary tool that may detect progression earlier and more precisely. Integrating different data modalities is challenging with conventional approaches, and artificial intelligence (AI) can help overcome this. Our aim is to develop robust models capable of predicting clinical progression in LGMD-R2 by incorporating AI-based techniques into the analysis pipeline.

methodsData from 188 COS 1 participants were analysed. Disease progression was assessed using the North Star Assessment for Limb Girdle type Muscular Dystrophies (NSAD). Ambulatory individuals with a maximum NSAD ≥ 20 were included, and progression trajectories were identified through hierarchical clustering. Feature selection was performed using a machine learning pipeline, and top predictors were entered into stepwise logistic regression to build clinical-only and combined clinical-MRI models.

resultsTwo stages of progression were identified, a fast one with a mean three-year loss of 14.4 NSAD points, and a moderate one, with a mean loss of 3.8 NSAD points. The combined model achieved better balanced accuracy than the clinical-only one (83.7% vs 78.7%). Key predictors in the combined model were disease duration and fat content measures in the anterior thigh and gracilis muscle, while the clinical model included disease duration, creatine phosphokinase (CK), and 10 m walk/run test velocity.

conclusionsProgression in LGMD-R2 can be grouped into distinct clinical trajectories. Individuals at a faster stage of progression were younger, had shorter disease duration, higher CK, greater weakness, and relatively preserved vastus intermedius and gracilis muscles. AI enabled efficient integration of heterogeneous data, and qMRI biomarkers provided complementary information that improved predictive accuracy.

Indexed as

Muscular Dystrophies, Limb-GirdleAdolescentAdultBiomarkersCohort StudiesDisease ProgressionDysferlinFemaleHumansMachine LearningMagnetic Resonance ImagingMalePredictive Learning ModelsPrognosisYoung AdultBiomarkersDysferlinBiomarkersDisease progressionDysferlinLimb-girdle muscular dystrophiesMagnetic resonance imagingPrognosis

Identifiers

PMID42189263
PMCPMC13212672

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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.