The socioeconomic burden of facioscapulohumeral muscular dystrophy

Blokhuis, Anna M.; Deenen, J.C.W.; Voermans, Nicol C.; Engelen, B.G.M. van; Kievit, Wietske; Groothuis, Jan T.

doi:10.1007/s00415-021-10591-w

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…Less visible FSHD symptoms include fatigue and chronic pain [ 3 ]. In addition to the physical burden, individuals with FSHD also experience emotional, social, and socioeconomic burdens [ 4 , 5 ]. As a result, patients report increased deterioration in quality of life as the disease progresses [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study

Zhuparris¹,

Maleki²,

Koopmans³

et al. 2023

JMIR Form Res

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Background 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. Trial Registration ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735

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Section: Introductionmentioning

confidence: 99%

Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study

Zhuparris¹,

Maleki²,

Koopmans³

et al. 2023

JMIR Form Res

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“…Facioscapulohumeral muscular dystrophy (FSHD) is one of the most prevalent myopathies that afflict males and females of all ages [ 1 , 2 , 3 , 4 ]. With the onset of clinical weakness typically appearing in the second or third decade of life and ~20% of patients ultimately using a wheelchair, the personal, social, and economic costs of this disease are enormous [ 5 , 6 ]. Although progress is being made, there are no cures or ameliorative treatments for FSHD, so an effective therapy is critically needed.…”

Section: Introductionmentioning

confidence: 99%

FSHD Therapeutic Strategies: What Will It Take to Get to Clinic?

Himeda

Jones

2022

JPM

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Facioscapulohumeral muscular dystrophy (FSHD) is arguably one of the most challenging genetic diseases to understand and treat. The disease is caused by epigenetic dysregulation of a macrosatellite repeat, either by contraction of the repeat or by mutations in silencing proteins. Both cases lead to chromatin relaxation and, in the context of a permissive allele, pathogenic misexpression of DUX4 in skeletal muscle. The complex nature of the locus and the fact that FSHD is a toxic, gain-of-function disease present unique challenges for the design of therapeutic strategies. There are three major DUX4-targeting avenues of therapy for FSHD: small molecules, oligonucleotide therapeutics, and CRISPR-based approaches. Here, we evaluate the preclinical progress of each avenue, and discuss efforts being made to overcome major hurdles to translation.

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