Journal of neurology · 2026
Identification of prognostic biomarkers in a large cohort of patients with LGMD R2.
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Bolano-Diaz Carla F, Verdu-Diaz Jose, Hao Dan, James Meredith K, Rufibach Laura, Blamire Andrew, Reyngoudt Harmen, Carlier Pierre G, Gordish-Dressman Heather, Hilsden Heather, Spuler Simone, Day John, Jones Kristi J, Bharucha-Goebel Diana, Pestronk Alan, Walter Maggie C, Paradas Carmen, Stojkovic Tanya, Mori-Yoshimura Madoka, Bravver Elena, Pegoraro Elena, Mendell Jerry, Jain COS Consortium, Straub Volker, Diaz-Manera Jordi
Аннотация
BACKGROUND: Limb-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.
METHODS: Data 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.
RESULTS: Two 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.
CONCLUSIONS: Progression 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.
METHODS: Data 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.
RESULTS: Two 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.
CONCLUSIONS: Progression 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.