five

Adult MTM1-related myopathy carriers: classification based on deep phenotyping

收藏
NIAID Data Ecosystem2026-03-11 收录
下载链接:
http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.116n37m
下载链接
链接失效反馈
官方服务:
资源简介:
Objective To better characterize adult MTM1-related myopathy carriers and recommend a phenotypic classification. Methods This cohort study was performed at the National Institutes of Health Clinical Center. Participants were required to carry a confirmed MTM1 mutation and were recruited via the Congenital Muscle Disease International Registry (n=8), a traveling local clinic of the Neuromuscular and Neurogenetic Disorders of Childhood Section, NINDS, NIH and Cure CMD (n=1) and direct physician referral (n=1). Neuromuscular examinations, muscle MRI, dynamic breathing MRI, cardiac MRI, pulmonary function tests (PFTs) and physical therapy assessments including the Motor Function Measure 32 (MFM-32) scale were performed. Results Phenotypic categories were proposed based on ambulatory status and muscle weakness. Carriers were categorized as severe (non-ambulatory; n=1); moderate (minimal independent ambulation/assisted ambulation; n=3); mild (independent ambulation but with evidence of muscle weakness; n=4) and non-manifesting (no evidence of muscle weakness; n=2). Weaker carriers exhibited a greater severity of respiratory insufficiency and abnormal signal on muscle imaging. Skeletal asymmetries were evident in both maninfesting and non-manifesting carriers. Skewed X chromosome inactivation did not explain phenotypic severity. Conclusion This work illustrates the phenotypic range of MTM1-related myopathy carriers in adulthood and recommends a phenotypic classification. This classification, defined by ambulatory status and muscle weakness, is supported by muscle MRI, PFT, and MFM-32 scale composite score findings, which may serve as markers of disease progression and outcome measures in future gene therapy or other clinical trials.
创建时间:
2019-09-28
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作