Exploration of Epigenetic Mechanisms and Biomarkers Among Patients with Very-Late-Onset Schizophrenia-Like Psychosis

Yansha Gan,1,* Weihua Yue,2,* JiaoJiao Sun,1 DanTing Yang,1 ChunXia Fang,1 Zhenhe Zhou,1 JiaJun Yin,1 Hongliang Zhou3 1The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, 214151, People’s Republic of China; 2National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, 100191, People’s Republic of China; 3Department of Psychology, The Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, 214100, People’s Republic of China*These authors contributed equally to this workCorrespondence: JiaJun Yin, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, 214151, People’s Republic of China, Email [email protected] Hongliang Zhou, Department of Psychology, The Affiliated Hospital of Jiangnan University, No. 200, Huihe Road, Binhu District, Wuxi City, Jiangsu Province, People’s Republic of China, Email [email protected]: 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).Methods: 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.Results: Analysis revealed distinct DNA methylation signatures in VLOSLP patients compared to controls. The GNB5 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.Conclusion: 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.Keywords: very late-onset schizophrenia-like psychosis, epigenetics schizophrenia, Alzheimer’s disease, biomarkers