Modeling of Dysferlinopathy (LGMDR2) Progression

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Background and Objectives: Limb-girdle muscular dystrophy R2 (LGMDR2) is characterized by progressive muscle weakness usually leading to severe disability. The rate of progression and disease severity is variable among patients, although factors influencing this variability are not completely understood. The Dysferlinopathy Clinical Outcome Study is a natural history study that followed patients with LGMDR2 for 3 consecutive years using functional outcome measures and skeletal muscle MRI.The aim of our study was to develop statistical models able to describe fat fraction (FF) progression of the lower limbs in patients with LGMDR2 using clinical and radiologic variables to better understand which factors influence disease progression and improve the design of future clinical trials. Methods: We used linear-mixed modeling to analyze changes in FF over time according to patients' age. We calculated the average FF trajectory for each muscle of the lower limbs. We built 2 multivariate models for each segment adding other clinical factors and using likelihood ratio test and residuals' analysis to determine whether they better fitted observed FF values. Results: Muscles that participated in the same joint movement progressed similarly over time. FF was expected to be higher the older patients were and the earlier the age at symptom onset. Women had absolute FF values 8.8% higher than men in the lower leg. No differences in FF trajectory were seen based on ethnic groups (White, Asian, Black, or Hispanic), genetic variants, or residual dysferlin expression. Although multivariate models showed a better global fit to the data, there was no improvement in representing individual patient variability. Discussion: In conclusion, this study provides a better understanding of skeletal muscle fat replacement progression in the lower limb muscles of patients with LGMDR2, highlighting the influence of age at symptom onset, sex, and baseline motor function, which should be considered in the design and analysis of clinical trials. Although complex models improved the overall data fit, they did not improve the accuracy in identifying changes at a patient level, underlying the need for further research and validation and the fact that other variables we have not measured are probably influencing progression.

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