BIO5 Institute, University of Arizona, Tucson, Arizona, USA.
Department of Mathematics, University of Arizona, Tucson, Arizona, USA.
Epilepsia. 2024 Nov;65(11):3324-3334. doi: 10.1111/epi.18118. Epub 2024 Sep 18.
Phenotypic heterogeneity presents challenges in providing clinical care to patients with pathogenic SCN8A variants, which underly a wide disease spectrum ranging from neurodevelopmental delays without seizures to a continuum of mild to severe developmental and epileptic encephalopathies (DEEs). An important unanswered question is whether there are clinically important subgroups within this wide spectrum. Using both supervised and unsupervised machine learning (ML) approaches, we previously found statistical support for two and three subgroups associated with loss- and gain- of- function vari-ants, respectively. Here, we test the hypothesis that the unsupervised subgroups (U1-U3) are distinguished by differential contributions of developmental and epileptic components.
We predicted that patients in the U1 and U2 subgroups would differ in timing of developmental delay and seizure onset, with earlier and concurrent onset of both features for the U3 subgroup. Standard statistical procedures were used to test these predictions, as well as to investigate clinically relevant associations among all five subgroups.
Two-population proportion and Kruskal-Wallis tests supported the hypothesis of a reversed order of developmental delay and seizure onset for patients in U1 and U2, and nearly synchronous developmental delay/seizure onset for the U3 (termed DEE) subgroup. Association testing identified subgroup variation in treatment response, frequency of initial seizure type, and comorbidities, as well as different median ages of developmental delay onset for all five subgroups.
Unsupervised ML approaches discern differential developmental and epileptic components among patients with SCN8A-related epilepsy. Patients in U1 (termed developmental encephalopathy) typically gain seizure control yet rarely experience improvements in development, whereas those in U2 (termed epileptic encephalopathy) have fewer if any developmental impairments despite difficulty in achieving seizure control. This understanding improves prognosis and clinical management and provides a framework to discover mechanisms underlying variability in clinical outcome of patients with SCN8A-related disorders.
致病性 SCN8A 变异导致表型异质性，给患者的临床治疗带来挑战，其疾病谱广泛，从无癫痫发作的神经发育迟缓到轻度至重度发育性和癫痫性脑病（DEE）连续谱。一个重要的未解决问题是在这个广泛的谱内是否存在临床重要的亚组。我们之前使用监督和无监督机器学习（ML）方法发现了与功能丧失和功能获得变异相关的两个和三个亚组的统计学支持。在这里，我们检验了无监督亚组（U1-U3）是否因发育和癫痫成分的差异贡献而不同的假设。
我们预测 U1 和 U2 亚组的患者在发育迟缓的时间和癫痫发作的起始方面会有所不同，U3 亚组的两者都具有更早和同时的发作。标准统计程序用于检验这些预测，以及调查所有五个亚组之间的临床相关关联。
两群体比例和 Kruskal-Wallis 检验支持 U1 和 U2 患者发育迟缓与癫痫发作起始顺序逆转的假设，而 U3（称为 DEE）亚组的发育延迟/癫痫发作几乎同步发生。关联检验确定了治疗反应、初始发作类型的频率和合并症的亚组变异，以及所有五个亚组发育迟缓起始的中位数年龄的差异。
无监督 ML 方法在 SCN8A 相关癫痫患者中辨别出不同的发育和癫痫成分。U1 组（称为发育性脑病）的患者通常能控制癫痫发作，但很少能改善发育，而 U2 组（称为癫痫性脑病）的患者尽管癫痫控制困难，但发育受损的情况较少或没有。这种理解改善了预后和临床管理，并为发现 SCN8A 相关疾病患者临床结局变异性的机制提供了框架。
