Duchenne muscular dystrophy is a severe neuromuscular disorder that is caused by mutations in the DMD gene, resulting in a disruption of dystrophin production. Next to dystrophin expression in the... Show moreDuchenne muscular dystrophy is a severe neuromuscular disorder that is caused by mutations in the DMD gene, resulting in a disruption of dystrophin production. Next to dystrophin expression in the muscle, different isoforms of the protein are also expressed in the brain and lack of these isoforms leads to cognitive and behavioral deficits in patients. It remains unclear how the loss of the shorter dystrophin isoform Dp140 affects these processes. Using a variety of behavioral tests, we found that mdx and mdx4cv mice (which lack Dp427 or Dp427 + Dp140, respectively) exhibit similar deficits in working memory, movement patterns and blood–brain barrier integrity. Neither model showed deficits in spatial learning and memory, learning flexibility, anxiety or spontaneous behavior, nor did we observe differences in aquaporin 4 and glial fibrillary acidic protein. These results indicate that in contrast to Dp427, Dp140 does not play a crucial role in processes of learning, memory and spontaneous behavior. Show less
Background: Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on... Show moreBackground: Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on validated biomarkers that can quantify disease progression and response to drug interventions.Objective: We aimed to build a tool that estimates FSHD clinical severity based on behavioral features captured using smartphone and remote sensor data. The adoption of remote monitoring tools, such as smartphones and wearables, would provide a novel opportunity for continuous, passive, and objective monitoring of FSHD symptom severity outside the clinic.Methods: In total, 38 genetically confirmed patients with FSHD were enrolled. The FSHD Clinical Score and the Timed Up and Go (TUG) test were used to assess FSHD symptom severity at days 0 and 42. Remote sensor data were collected using an Android smartphone, Withings Steel HR+, Body+, and BPM Connect+ for 6 continuous weeks. We created 2 single-task regression models that estimated the FSHD Clinical Score and TUG separately. Further, we built 1 multitask regression model that estimated the 2 clinical assessments simultaneously. Further, we assessed how an increasingly incremental time window affected the model performance. To do so, we trained the models on an incrementally increasing time window (from day 1 until day 14) and evaluated the predictions of the clinical severity on the remaining 4 weeks of data.Results: The single-task regression models achieved an R2 of 0.57 and 0.59 and a root-mean-square error (RMSE) of 2.09 and 1.66 when estimating FSHD Clinical Score and TUG, respectively. Time spent at a health-related location (such as a gym or hospital) and call duration were features that were predictive of both clinical assessments. The multitask model achieved an R2 of 0.66 and 0.81 and an RMSE of 1.97 and 1.61 for the FSHD Clinical Score and TUG, respectively, and therefore outperformed the single-task models in estimating clinical severity. The 3 most important features selected by the multitask model were light sleep duration, total steps per day, and mean steps per minute. Using an increasing time window (starting from day 1 to day 14) for the FSHD Clinical Score, TUG, and multitask estimation yielded an average R2 of 0.65, 0.79, and 0.76 and an average RMSE of 3.37, 2.05, and 4.37, respectively. Conclusions: We demonstrated that smartphone and remote sensor data could be used to estimate FSHD clinical severity and therefore complement the assessment of FSHD outside the clinic. In addition, our results illustrated that training the models on the first week of data allows for consistent and stable prediction of FSHD symptom severity. Longitudinal follow-up studies should be conducted to further validate the reliability and validity of the multitask model as a tool to monitor disease progression over a longer period. Show less
Introduction Electrical impedance myography (EIM) has been proposed as a noninvasive biomarker of muscle composition in facioscapulohumeral muscular dystrophy (FSHD). Here we determine the... Show moreIntroduction Electrical impedance myography (EIM) has been proposed as a noninvasive biomarker of muscle composition in facioscapulohumeral muscular dystrophy (FSHD). Here we determine the associations of EIM variables with muscle structure measured by MRI. Methods We evaluated 20 patients with FSHD at two centers, comparing EIM measurements (resistance, reactance, and phase at 50, 100, and 211 kHZ) recorded from bilateral vastus lateralis, tibialis anterior, and medial gastrocnemius muscles to MRI skin and subcutaneous fat thickness, MRI T1-based muscle severity score (T1 muscle score), and MRI quantitative intramuscular Dixon fat fraction (FF). Results While reactance and phase both correlated with FF and T1 muscle score, 50 kHz reactance was most sensitive to muscle structure alterations measured by both T1 score (rho = -0.71, P < .001) and FF (rho = -0.74, P < .001). Discussion This study establishes the correlation of EIM with structural MRI features in FSHD and supports further evaluation of EIM as a potential biomarker in FSHD clinical trials. Show less
