PDF(Pubmed)
Abstract：
X-linked myotubular myopathy (XLMTM) is a rare congenital myopathy resulting from dysfunction of the protein myotubularin encoded by the MTM1 gene. XLMTM has a high neonatal and infantile mortality rate due to a severe myopathic phenotype and respiratory failure. However, in a minority of XLMTM cases, patients present with milder phenotypes and achieve ambulation and adulthood. Notable facial dysmorphia is also present.
We investigated the genotype-phenotype correlations in newly diagnosed XLMTM patients in a patients\' cohort (previously published data plus three novel variants, n = 414). Based on the facial gestalt difference between XLMTM patients and unaffected controls, we investigated the use of the Face2Gene application.
Significant associations between severe phenotype and truncating variants (p < 0.001), frameshift variants (p < 0.001), nonsense variants (p = 0.006), and in/del variants (p = 0.036) were present. Missense variants were significantly associated with the mild and moderate phenotype (p < 0.001). The Face2Gene application showed a significant difference between XLMTM patients and unaffected controls (p = 0.001).
Using genotype-phenotype correlations could predict the disease course in most XLMTM patients, but still with limitations. The Face2Gene application seems to be a practical, non-invasive diagnostic approach in XLMTM using the correct algorithm.
We investigated the genotype-phenotype correlations in newly diagnosed XLMTM patients in a patients\' cohort (previously published data plus three novel variants, n = 414). Based on the facial gestalt difference between XLMTM patients and unaffected controls, we investigated the use of the Face2Gene application.
Significant associations between severe phenotype and truncating variants (p < 0.001), frameshift variants (p < 0.001), nonsense variants (p = 0.006), and in/del variants (p = 0.036) were present. Missense variants were significantly associated with the mild and moderate phenotype (p < 0.001). The Face2Gene application showed a significant difference between XLMTM patients and unaffected controls (p = 0.001).
Using genotype-phenotype correlations could predict the disease course in most XLMTM patients, but still with limitations. The Face2Gene application seems to be a practical, non-invasive diagnostic approach in XLMTM using the correct algorithm.
摘要：
背景：X连锁肌管肌病（XLMTM）是一种罕见的先天性肌病，由MTM1基因编码的肌管蛋白的功能障碍引起。XLMTM由于严重的肌病表型和呼吸衰竭而具有较高的新生儿和婴儿死亡率。然而,在少数XLMTM病例中，患者表现为较温和的表型，并实现下床活动和成年期。还存在明显的面部畸形。
方法：我们调查了患者队列中新诊断的XLMTM患者的基因型-表型相关性（以前发表的数据加上三个新变体，n=414）。基于XLMTM患者和未受影响的对照组之间的面部完形差异，我们调查了Face2Gene应用程序的使用。
结果：严重表型与截短变异之间存在显著关联（p<0.001），移码变体（p<0.001），无义变体（p=0.006），和in/del变体（p=0.036）存在。错义变异与轻度和中度表型显著相关(p<0.001)。Face2Gene应用显示XLMTM患者和未受影响的对照组之间存在显着差异（p=0.001）。
结论：使用基因型-表型相关性可以预测大多数XLMTM患者的病程，但仍有局限性。Face2Gene应用程序似乎是一个实用的，使用正确算法的XLMTM无创诊断方法。
方法：我们调查了患者队列中新诊断的XLMTM患者的基因型-表型相关性（以前发表的数据加上三个新变体，n=414）。基于XLMTM患者和未受影响的对照组之间的面部完形差异，我们调查了Face2Gene应用程序的使用。
结果：严重表型与截短变异之间存在显著关联（p<0.001），移码变体（p<0.001），无义变体（p=0.006），和in/del变体（p=0.036）存在。错义变异与轻度和中度表型显著相关(p<0.001)。Face2Gene应用显示XLMTM患者和未受影响的对照组之间存在显着差异（p=0.001）。
结论：使用基因型-表型相关性可以预测大多数XLMTM患者的病程，但仍有局限性。Face2Gene应用程序似乎是一个实用的，使用正确算法的XLMTM无创诊断方法。
