精神分裂症(面向对象编程)
细胞外小泡
队列
双相情感障碍
重性抑郁障碍
医学
胞外囊泡
抗精神病药
精神科
内科学
生物标志物
精神分裂症的诊断
精神病
肿瘤科
心理学
临床心理学
认知
生物
小RNA
生物化学
微泡
基因
细胞生物学
作者
Ting Xue,Wenxin Liu,Lijun Wang,Yuan Shi,Ying Hu,Jing Yang,Guiming Li,Hongna Huang,Zezhi Li
出处
期刊:Brain
[Oxford University Press]
日期:2023-10-10
标识
DOI:10.1093/brain/awad341
摘要
Schizophrenia, a complex neuropsychiatric disorder, frequently experiences a high rate of misdiagnosis due to subjective symptom assessment. Consequently, there is an urgent need for innovative and objective diagnostic tools. In this study, we utilized cutting-edge extracellular vesicles' (EVs) proteome profiling and XGBoost-based machine learning to develop new markers and personalized discrimination scores (PDS) for schizophrenia diagnosis and prediction of treatment response. We analyzed plasma and plasma-derived EVs from 343 participants, including 100 individuals with chronic schizophrenia, 34 first-episode and drug-naïve (FEDN) patients, 35 individuals with bipolar disorder (BD), 25 individuals with major depressive disorder (MDD), and 149 age- and sex-matched healthy controls. Our innovative approach uncovered EVs-based complement changes in patients, specific to their disease-type and status. The EV-based biomarkers outperformed their plasma counterparts, accurately distinguishing schizophrenia individuals from healthy controls with an area under curve (AUC) of 0.895, 83.5% accuracy, 85.3% sensitivity, and 82.0% specificity. Moreover, they effectively differentiated schizophrenia from BD and MDD, with AUCs of 0.966 and 0.893, respectively. The PDS provided a personalized diagnostic index for schizophrenia and exhibited a significant association with patients' antipsychotic treatment response in the follow-up cohort. Overall, our study represents a significant advancement in the field of neuropsychiatric disorders, demonstrating the potential of EV-based biomarkers in guiding personalized diagnosis and treatment of schizophrenia.
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