Zhang TianHong, Xu LiHua, Tang XiaoChen, Wei YanYan, Hu YeGang, Cui HuiRu, Tang YingYing, Li ChunBo, Wang JiJun
Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai China.
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai China.
PCN Rep. 2023 Nov 14;2(4):e152. doi: 10.1002/pcn5.152. eCollection 2023 Dec.
Psychosis is recognized as one of the largest contributors to nonfatal health loss, and early identification can largely improve routine clinical activity by predicting the psychotic course and guiding treatment. Clinicians have used the clinical high-risk for psychosis (CHR) paradigm to better understand the risk factors that contribute to the onset of psychotic disorders. Clinical factors have been widely applied to calculate the individualized risks for conversion to psychosis 1-2 years later. However, there is still a dearth of valid biomarkers to predict psychosis. Biomarkers, in the context of this paper, refer to measurable biological indicators that can provide valuable information about the early identification of individuals at risk for psychosis. The aim of this paper is to critically review studies assessing CHR and suggest possible biomarkers for application of prediction. We summarized the studies on biomarkers derived from the findings of the ShangHai at Risk for Psychosis (SHARP) program, including those that are considered to have the most potential. This comprehensive review was conducted based on expert opinions within the SHARP research team, and the selection of studies and results presented in this paper reflects the collective expertise of the team in the field of early psychosis identification. The three dimensions with potential candidates include neuroimaging dimension of brain structure and function, electrophysiological dimension of event-related potentials (ERPs), and immune dimension of inflammatory cytokines and complement proteins, which proved to be useful in supporting the prediction of psychosis from the CHR state. We suggest that these three dimensions could be useful as risk biomarkers for treatment optimization. In the future, when available for the integration of multiple dimensions, clinicians may be able to obtain a comprehensive report with detailed information of psychosis risk and specific indications about preferred prevention.
精神病被认为是导致非致命性健康损失的最大因素之一,早期识别可以通过预测精神病病程和指导治疗在很大程度上改善常规临床活动。临床医生已采用精神病临床高危(CHR)范式来更好地理解导致精神病性障碍发病的风险因素。临床因素已被广泛应用于计算个体在1至2年后转化为精神病的风险。然而,仍然缺乏有效的预测精神病的生物标志物。在本文中,生物标志物是指可测量的生物学指标,其能够为早期识别有患精神病风险的个体提供有价值的信息。本文的目的是批判性地回顾评估CHR的研究,并提出可能用于预测的生物标志物。我们总结了来自上海精神病风险(SHARP)项目研究结果的生物标志物研究,包括那些被认为最具潜力的研究。这一全面综述是基于SHARP研究团队内部的专家意见进行的,本文所呈现的研究选择和结果反映了该团队在早期精神病识别领域的集体专业知识。具有潜在候选物的三个维度包括脑结构和功能的神经影像学维度、事件相关电位(ERP)的电生理学维度以及炎性细胞因子和补体蛋白的免疫维度,事实证明这些维度有助于支持从CHR状态预测精神病。我们认为这三个维度可作为优化治疗的风险生物标志物。未来,当可用于整合多个维度时,临床医生或许能够获得一份包含精神病风险详细信息和关于首选预防措施具体指征的综合报告。