Jiang Nan, Zhang Liyuan, Zheng Zeyan, Du Hanze, Chen Shi, Pan Hui
4+4 Medical Doctor Program, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100730, Beijing, China.
Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100730, Beijing, China.
Eur J Hum Genet. 2024 Dec 9. doi: 10.1038/s41431-024-01754-0.
Xia-Gibbs syndrome (XGS) is a rare neurodevelopmental disorder with considerable clinical heterogeneity. To further characterize the syndrome's heterogeneity, we applied latent class analysis (LCA) on reported cases to identify phenotypic subtypes. By searching PubMed, Embase, China National Knowledge Infrastructure and Wanfang databases from inception to February 2024, we enrolled 97 cases with nonsense, frameshift or missense variants in the AHDC1 gene. LCA was based on the following 6 phenotypes with moderate occurrence and low missingness: ataxia, seizure, autism, sleep apnea, short stature and scoliosis. After excluding cases with missing data on all LCA variables or with unmatched phenotype-genotype information, a total of 85 cases were selected for LCA. Models with 1-5 classes were compared based on Akaike Information Criterion, Bayesian Information Criterion, Sample-Size Adjusted BIC and entropy. We used multinomial logistic regression (MLR) analyses to investigate the phenotype-genotype association and potential predictors for class membership. LCA revealed 3 distinct classes labeled as Ataxia subtype (n = 11 [12.9%]), Sleep apnea & short stature subtype (n = 23 [27.1%]) and Neuropsychological subtype (n = 51 [60.0%]). The commonest Neuropsychological subtype was characterized by high estimated probabilities of seizure, ataxia and autism. By adjusting for sex, age and variant type, MLR showed no significant association between phenotypic subtype and variant position. Age and variant type were identified as predictors of class membership. The findings of this review offer novel insights for different presentations of XGS. It is possible to deliver targeted monitoring and treatment for each subtype in the early stage.
夏-吉布斯综合征（XGS）是一种罕见的神经发育障碍，具有显著的临床异质性。为了进一步刻画该综合征的异质性，我们对已报道的病例应用潜在类别分析（LCA）来识别表型亚型。通过检索PubMed、Embase、中国知网和万方数据库，纳入从建库至2024年2月的97例AHDC1基因存在无义、移码或错义变异的病例。LCA基于以下6种发生率中等且缺失率低的表型：共济失调、癫痫发作、自闭症、睡眠呼吸暂停、身材矮小和脊柱侧弯。在排除所有LCA变量数据缺失或表型-基因型信息不匹配的病例后，共选择85例进行LCA。基于赤池信息准则、贝叶斯信息准则、样本量调整后的BIC和熵，比较了1-5类模型。我们使用多项逻辑回归（MLR）分析来研究表型-基因型关联以及类别归属的潜在预测因素。LCA揭示了3个不同的类别，分别标记为共济失调亚型（n = 11 [12.9%]）、睡眠呼吸暂停和身材矮小亚型（n = 23 [27.1%]）以及神经心理亚型（n = 51 [60.0%]）。最常见的神经心理亚型的特征是癫痫发作、共济失调和自闭症的估计概率较高。通过对性别、年龄和变异类型进行校正，MLR显示表型亚型与变异位置之间无显著关联。年龄和变异类型被确定为类别归属的预测因素。本综述的结果为XGS的不同表现提供了新的见解。有可能在早期对每个亚型进行针对性的监测和治疗。
