Abela Mark, D'Silva Andrew
Cardiology Clinical Academic Group, St George's, University of London, Cranmer Terrace, London, SW17 0RE, UK.
MSc Sports Cardiology, Cardiology Clinical Academic Group, St George's, University of London, London, UK.
Curr Treat Options Cardiovasc Med. 2018 Oct 26;20(12):100. doi: 10.1007/s11936-018-0698-8.
Excessive trabeculation attracting a diagnosis of left ventricular noncompaction cardiomyopathy (LVNC) has been reported in ostensibly healthy athletes. This review aims to explain why this occurs and whether this represents a spectrum of athletic physiological remodelling or unmasking of occult cardiomyopathy.
Genetic studies have yet to identify a dominant mutation associated with the LVNC phenotype and reported gene mutations overlap with many distinct cardiomyopathies and ion channel disorders, implying that the phenotype is shared across different genetic conditions. Large contemporary cohort studies indicate that current LVNC imaging criteria are oversensitive and not predictive of adverse clinical outcomes. The majority of excessive LV trabeculation, as assessed by current quantification methods, is not due to cardiomyopathy but forms part of the normal continuum in health with potential contributions from cardiac remodelling processes. The study of rare, severe LVNC phenotypes may yield insights into an underlying molecular pathogenesis but in the absence of a universally accepted definition, contamination with aetiologically distinct conditions expressing a similar phenotype will remain an issue. Automated, objective quantification of trabeculation will help to define the normal distribution using big data without the constraint of wide interobserver variation.
据报道，在表面健康的运动员中存在过度小梁化现象，这吸引了对左心室致密化不全心肌病（LVNC）的诊断。本综述旨在解释其发生原因，以及这是否代表了运动性生理重塑的一个范围，还是隐匿性心肌病的暴露。
基因研究尚未确定与LVNC表型相关的显性突变，并且报道的基因突变与许多不同的心肌病和离子通道疾病重叠，这意味着该表型在不同的遗传条件下是共享的。大型当代队列研究表明，当前的LVNC成像标准过于敏感，不能预测不良临床结局。通过当前量化方法评估，大多数左心室过度小梁化并非由心肌病引起，而是健康正常连续过程的一部分，心脏重塑过程可能对此有贡献。对罕见、严重LVNC表型的研究可能会深入了解潜在的分子发病机制，但在缺乏普遍接受的定义的情况下，与表达相似表型的病因不同的疾病的混杂仍然是一个问题。小梁化的自动化、客观量化将有助于利用大数据定义正常分布，而不受观察者间差异大的限制。
