Gan Yansha, Yue Weihua, Sun JiaoJiao, Yang DanTing, Fang ChunXia, Zhou Zhenhe, Yin JiaJun, Zhou Hongliang
The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, 214151, People's Republic of China.
National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, 100191, People's Republic of China.
Neuropsychiatr Dis Treat. 2025 Apr 22;21:927-942. doi: 10.2147/NDT.S513992. eCollection 2025.
This study aimed to identify DNA methylation patterns associated with Very Late-Onset Schizophrenia-like Psychosis (VLOSLP) and to develop methylation-based biomarkers that differentiate VLOSLP from Schizophrenia (SCZ) and Alzheimer's Disease (AD).
We analyzed methylation microarray datasets (n = 1218) from SCZ and AD patients obtained from the GEO database. We then collected blood samples from VLOSLP patients and age-matched healthy controls (n = 80) at the Wuxi Mental Health Center for methylation microarray profiling and bisulfite sequencing validation. Differential methylation analysis and Gene Ontology (GO) enrichment analysis identified candidate loci. We prioritized key methylation sites through integrated analysis of methylation quantitative trait loci (meQTL), linkage disequilibrium (LD) patterns, and blood-brain methylation correlations. Machine learning algorithms generated diagnostic models, with classification performance evaluated using Area Under the Curve (AUC) metrics.
Analysis revealed distinct DNA methylation signatures in VLOSLP patients compared to controls. The gene exhibited shared epigenetic modifications across SCZ, AD, and VLOSLP, suggesting a common pathogenic mechanism. The diagnostic model discriminating AD from VLOSLP demonstrated high accuracy, achieving an AUC of 1.0 in the training set and 0.958 in the test set (95% CI: 0.875-1.000). The AD versus SCZ classification model showed similar robustness, with AUCs of 0.995 and 0.955 in training and test sets, respectively (95% CI: 0.926-0.983). The SCZ versus VLOSLP model achieved perfect discrimination (AUC = 1.0) in both training and test sets, with substantial clinical utility. Additional analyses suggested distinct molecular subtypes within VLOSLP.
Specific DNA methylation alterations in VLOSLP are identified as potential diagnostic biomarkers. These findings may contribute to the development of molecular diagnostic tools, though further validation in larger, independent cohorts is warranted.
本研究旨在识别与极晚发性精神分裂症样精神病（VLOSLP）相关的DNA甲基化模式，并开发基于甲基化的生物标志物，以区分VLOSLP与精神分裂症（SCZ）和阿尔茨海默病（AD）。
我们分析了从基因表达综合数据库（GEO数据库）获得的SCZ和AD患者的甲基化微阵列数据集（n = 1218）。然后，我们在无锡市精神卫生中心采集了VLOSLP患者和年龄匹配的健康对照（n = 80）的血样，用于甲基化微阵列分析和亚硫酸氢盐测序验证。差异甲基化分析和基因本体（GO）富集分析确定了候选基因座。我们通过甲基化数量性状基因座（meQTL）、连锁不平衡（LD）模式和血脑甲基化相关性的综合分析，对关键甲基化位点进行了优先级排序。机器学习算法生成了诊断模型，并使用曲线下面积（AUC）指标评估分类性能。
分析显示，与对照组相比，VLOSLP患者具有独特的DNA甲基化特征。该基因在SCZ、AD和VLOSLP中表现出共同的表观遗传修饰，提示存在共同的致病机制。区分AD与VLOSLP的诊断模型显示出高准确性，在训练集中AUC为1.0，在测试集中为0.958（95%CI：0.875 - 1.000）。AD与SCZ的分类模型显示出类似的稳健性，训练集和测试集的AUC分别为0.995和0.955（95%CI：0.926 - 0.983）。SCZ与VLOSLP模型在训练集和测试集中均实现了完美区分（AUC = 1.0），具有重要的临床应用价值。进一步分析提示VLOSLP存在不同的分子亚型。
VLOSLP中特定的DNA甲基化改变被确定为潜在的诊断生物标志物。这些发现可能有助于分子诊断工具的开发，不过需要在更大的独立队列中进行进一步验证。
