ArticleOrphanet journal of rare diseases2025
Prevalence and predictors of uncommon features in FSHD1 patients: insights from the French FSHD registry.
Article in Orphanet journal of rare diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04694456 (Pro-inflammatory Cytokines as Potential Therapeutic Target in Type 1 Facioscapulohumeral Muscular Dystrophy), which is not on this map. Not yet cited in PubMed.
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.
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.
Pro-inflammatory Cytokines as Potential Therapeutic Target in Type 1 Facioscapulohumeral Muscular Dystrophy: Pilot Study
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
34 authors.
Funding
Abstract
backgroundFacioscapulohumeral muscular dystrophy (FSHD) is characterized by a typical pattern of muscle involvement, yet it encompasses a wide spectrum of phenotypes, including less common features that remain incompletely defined in the literature. While previous studies have highlighted this clinical variability, no consensus has been reached on how to classify uncommon manifestations, nor have specific predictors been identified. This study aims to describe these uncommon features and explore potential predictors, utilizing data from the French FSHD registry. To this end, we analysed data from 306 FSHD1 patients across nine French neuromuscular referral centres. Descriptive statistics, univariate analyses, and multiple logistic regression models were employed to examine uncommon characteristics and their predictors.
resultsUncommon features were observed in 19.6% of cases. The most common was a discrepancy between disease severity and D4Z4 repeat unit (RU) count (41.7%), followed by predominant impairment at proximal lower limb or distal upper limb muscles (21.7%). Three unanticipated features emerged: isolated or predominant axial impairment, anosmia and atopic dermatitis. Univariate analysis revealed that uncommon features were associated with higher RU count (6.5 ± 2.1 vs. 5.8 ± 1.8 in typical patients) and older age of onset (32.0 ± 18.8 years vs. 25.0 ± 15.4 years). Such features were more prevalent in the borderline 8-10 RU range, an association confirmed by multivariate analysis (OR = 2.43, 95% CI 1.21 to 4.87). Later age of onset consistently emerged as a factor across multiple multivariate models.
conclusionsThis study documents uncommon FSHD features, revealing their association with the 8-10 RU range and later age of onset. These findings further support a complex interplay among genetic and epigenetic modifiers and ageing in shaping the clinical phenotype of FSHD, especially in patients carrying borderline D4Z4 arrays. Differential phenotypes, particularly in relation to RU range and age of onset, points to the importance of harmonized, comprehensive clinical and genetic assessments. Recognizing uncommon features may improve diagnostic accuracy and guide individualized management strategies, highlighting the need for tailored approaches to patient care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.