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从遗传学到表观遗传学,揭示青少年特发性脊柱侧凸的病因。

From genetics to epigenetics to unravel the etiology of adolescent idiopathic scoliosis.

机构信息

EpiDisease S.L., University of Valencia. Scientific Park. Paterna, Valencia, Spain.

Institute for Health Research La Fe, IISLaFe, Valencia, Spain.

出版信息

Bone. 2020 Nov;140:115563. doi: 10.1016/j.bone.2020.115563. Epub 2020 Aug 5.

DOI:10.1016/j.bone.2020.115563
PMID:32768685
Abstract

Scoliosis is defined as the three-dimensional (3D) structural deformity of the spine with a radiological lateral Cobb angle (a measure of spinal curvature) of ≥10° that can be caused by congenital, developmental or degenerative problems. However, those cases whose etiology is still unknown, and affect healthy children and adolescents during growth, are the commonest form of spinal deformity, known as adolescent idiopathic scoliosis (AIS). In AIS management, early diagnosis and the accurate prediction of curve progression are most important because they can decrease negative long-term effects of AIS treatment, such as unnecessary bracing, frequent exposure to radiation, as well as saving the high costs of AIS treatment. Despite efforts made to identify a method or technique capable of predicting AIS progression, this challenge still remains unresolved. Genetics and epigenetics, and the application of machine learning and artificial intelligence technologies, open up new avenues to not only clarify AIS etiology, but to also identify potential biomarkers that can substantially improve the clinical management of these patients. This review presents the most relevant biomarkers to help explain the etiopathogenesis of AIS and provide new potential biomarkers to be validated in large clinical trials so they can be finally implemented into clinical settings.

摘要

脊柱侧凸定义为脊柱的三维(3D)结构畸形,放射学侧方 Cobb 角(脊柱弯曲度的衡量指标)≥10°,可由先天性、发育性或退行性问题引起。然而,那些病因仍不清楚的病例,以及在生长过程中影响健康儿童和青少年的病例,是最常见的脊柱畸形形式,称为青少年特发性脊柱侧凸(AIS)。在 AIS 管理中,早期诊断和准确预测曲线进展最重要,因为这可以降低 AIS 治疗的负面长期影响,例如不必要的支具、频繁暴露于辐射,以及节省 AIS 治疗的高成本。尽管已经努力确定一种能够预测 AIS 进展的方法或技术,但这一挑战仍然没有得到解决。遗传学和表观遗传学,以及机器学习和人工智能技术的应用,开辟了新的途径,不仅可以阐明 AIS 的病因,还可以确定潜在的生物标志物,从而大大改善这些患者的临床管理。本综述介绍了最相关的生物标志物,以帮助解释 AIS 的病因发病机制,并提供新的潜在生物标志物,以在大型临床试验中进行验证,最终将其应用于临床实践。

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