Genetic screening of EIF2B genes reveals mutation spectrum and predicted prevalence of vanishing white matter disease in Chinese population

IF 2.9 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY
Clinica Chimica Acta Pub Date : 2026-05-15 Epub Date: 2026-03-04 DOI:10.1016/j.cca.2026.120942
Xiaoli Liu , Ruolan Guo , Zhan Qi , Yaodong Zhang , Xiaotun Ren , Wei Li , Xuyun Hu , Chanjuan Hao
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Abstract

Background

Vanishing White Matter disease (VWM) is a rare autosomal recessive leukoencephalopathy caused by biallelic variants in any of the five subunits of eukaryotic initiation factor 2B (EIF2B1–5), with varied clinical manifestations, including progressive neurological deterioration, cerebellar ataxia, and white matter abnormalities on MRI. Early and accurate diagnosis is crucial for medical interventions and genetic counseling.

Methods

We aimed to characterize the prevalence and spectrum of pathogenic variants of VWM in the Chinese population through Genetic screening for VWM mutations in 36,820 Chinese newborns from 31 hospitals across 14 provinces using next-generation sequencing. Pathogenic and likely pathogenic variants were identified and classified according to ACMG guidelines. Prevalence rates and variant spectra were analyzed.

Results

Among screened newborns, 114 carriers with 36 distinct pathogenic and likely pathogenic variants were identified, including 18 novel variants. The overall carrier frequency was 1 in 323. EIF2B2 showed the highest carrier frequency (1 in 498), with c.254 T > A/p.Val85Glu being a hotspot variant (61/74 carriers, 82.4%). The estimated prevalence rate of VWM in China was 1.12/1,000,000.

Conclusions

This large-scale screening provides valuable insights into the genetic landscape of VWM in the Chinese population, contributing to improved genetic counseling, early diagnosis, and management strategies. These findings contribute to enhancing the understanding and management of VWM in China.
EIF2B基因的遗传筛选揭示了中国人群中白质消失病的突变谱和预测患病率。
背景:消失白质病(VWM)是一种罕见的常染色体隐性白质脑病,由真核起始因子2B (EIF2B1-5)的5个亚基中的任何一个双等位基因变异引起,临床表现多样,包括进行性神经功能恶化、小脑性共济失调和MRI上的白质异常。早期和准确的诊断对于医疗干预和遗传咨询至关重要。方法:我们旨在通过使用下一代测序技术对来自14个省份31家医院的36,820名中国新生儿的VWM突变进行遗传筛查,以表征中国人群中VWM致病变异的患病率和谱。根据ACMG指南确定致病和可能致病的变异并进行分类。分析了患病率和变异谱。结果:在筛查的新生儿中,114名携带者被鉴定出36种不同的致病和可能的致病变异,其中18种是新变异。总体载波频率为1 / 323。EIF2B2的载频最高(1 / 498),为c.254 T > A/p。Val85Glu是热点变型(61/74携带者,82.4%)。估计中国VWM患病率为1.12/ 100万。结论:这项大规模筛查为了解中国人群VWM的遗传格局提供了有价值的见解,有助于改进遗传咨询、早期诊断和管理策略。这些发现有助于加强对中国VWM的认识和管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Clinica Chimica Acta
Clinica Chimica Acta 医学-医学实验技术
CiteScore
10.10
自引率
2.00%
发文量
1268
审稿时长
23 days
期刊介绍: The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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