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FKRP 肢带型肌营养不良症的纵向功能和影像学结果测量。

Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy.

机构信息

Center for Genetic Muscle Disorders, Hugo W. Moser Research Institute at Kennedy Krieger Institute, 716 North Broadway, Room 411, Baltimore, MD, 21205, USA.

Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

出版信息

BMC Neurol. 2020 May 19;20(1):196. doi: 10.1186/s12883-020-01774-5.

Abstract

BACKGROUND

Pathogenic variants in the FKRP gene cause impaired glycosylation of α-dystroglycan in muscle, producing a limb-girdle muscular dystrophy with cardiomyopathy. Despite advances in understanding the pathophysiology of FKRP-associated myopathies, clinical research in the limb-girdle muscular dystrophies has been limited by the lack of normative biomarker data to gauge disease progression.

METHODS

Participants in a phase 2 clinical trial were evaluated over a 4-month, untreated lead-in period to evaluate repeatability and to obtain normative data for timed function tests, strength tests, pulmonary function, and body composition using DEXA and whole-body MRI. Novel deep learning algorithms were used to analyze MRI scans and quantify muscle, fat, and intramuscular fat infiltration in the thighs. T-tests and signed rank tests were used to assess changes in these outcome measures.

RESULTS

Nineteen participants were observed during the lead-in period for this trial. No significant changes were noted in the strength, pulmonary function, or body composition outcome measures over the 4-month observation period. One timed function measure, the 4-stair climb, showed a statistically significant difference over the observation period. Quantitative estimates of muscle, fat, and intramuscular fat infiltration from whole-body MRI corresponded significantly with DEXA estimates of body composition, strength, and timed function measures.

CONCLUSIONS

We describe normative data and repeatability performance for multiple physical function measures in an adult FKRP muscular dystrophy population. Our analysis indicates that deep learning algorithms can be used to quantify healthy and dystrophic muscle seen on whole-body imaging.

TRIAL REGISTRATION

This study was retrospectively registered in clinicaltrials.gov (NCT02841267) on July 22, 2016 and data supporting this study has been submitted to this registry.

摘要

背景

FKRP 基因中的致病变异会导致肌肉中 α- dystroglycan 的糖基化受损,从而产生伴有心肌病的肢带型肌营养不良症。尽管人们对 FKRP 相关肌病的病理生理学有了更多的了解,但肢带型肌营养不良症的临床研究一直受到缺乏规范生物标志物数据来评估疾病进展的限制。

方法

参加 2 期临床试验的参与者在未经治疗的 4 个月导入期内接受评估,以评估重复性并获得定时功能测试、力量测试、肺功能和使用 DEXA 和全身 MRI 进行身体成分的正常数据。使用新的深度学习算法分析 MRI 扫描并定量大腿的肌肉、脂肪和肌肉内脂肪浸润。使用 t 检验和符号秩检验评估这些结果测量的变化。

结果

在该试验的导入期内观察了 19 名参与者。在 4 个月的观察期内,力量、肺功能或身体成分测量结果没有明显变化。一项定时功能测量,即 4 级楼梯攀登,在观察期间显示出统计学上的显著差异。全身 MRI 的肌肉、脂肪和肌肉内脂肪浸润的定量估计与 DEXA 估计的身体成分、力量和定时功能测量结果密切相关。

结论

我们描述了成年 FKRP 肌营养不良症人群中多种身体功能测量的正常数据和重复性表现。我们的分析表明,深度学习算法可用于量化全身成像中健康和营养不良的肌肉。

试验注册

这项研究于 2016 年 7 月 22 日在 clinicaltrials.gov(NCT02841267)中进行了回顾性注册,支持这项研究的数据已提交给该注册处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d69e/7236878/bfae23a59024/12883_2020_1774_Fig1_HTML.jpg

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