Chen Luan, Huai Cong, Song Chuanfu, Wu Shaochang, Xu Yong, Yi Zhenghui, Tang Jinsong, Fan Lingzi, Wu Xuming, Ge Zhenhua, Liu Chuanxin, Jiang Deguo, Weng Saizheng, Wang Guoqiang, Zhang Xinfeng, Zhao Xudong, Shen Lu, Zhang Na, Wu Hao, Wang Yongzhi, Guo Zhenglin, Zhang Suli, Jiang Bixuan, Zhou Wei, Ma Jingsong, Li Mo, Chu Yunpeng, Zhou Chenxi, Lv Qinyu, Xu Qingqing, Zhu Wenli, Zhang Yan, Lian Weibin, Liu Sha, Li Xinrong, Gao Songyin, Liu Aihong, He Lei, Yang Zhenzhen, Dai Bojian, Ye Jiaen, Lin Ruiqian, Lu Yana, Yan Qi, Hu Yalan, Xing Qinghe, Huang Hailiang, Qin Shengying
Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China.
The Fourth People's Hospital of Wuhu, Wuhu, China.
Mol Psychiatry. 2025 Jun;30(6):2362-2371. doi: 10.1038/s41380-024-02841-w. Epub 2024 Nov 19.
Schizophrenia (SCZ) is a severe mental disorder affecting around 1% of individuals worldwide. The variability in response to antipsychotic drugs (APDs) among SCZ patients presents a significant challenge for clinicians in determining the most effective medication. In this study, we investigated the biological markers and established a predictive model for APD response based on a large-scale genome-wide association study using 3269 Chinese schizophrenia patients. Each participant underwent an 8-week treatment regimen with one of five mono-APDs: olanzapine, risperidone, aripiprazole, quetiapine, or amisulpride. By dividing the response into ordinal groups of "high", "medium", and "low", we mitigated the bias of unclear treatment outcome and identified three novel significantly associated genetic loci in or near CDH12, WDR11, and ELAVL2. Additionally, we developed predictive models of response to each specific APDs, with accuracies ranging from 79.5% to 98.0%. In sum, we established an effective method to predict schizophrenia patients' response to APDs across three categories, integrating novel biomarkers to guide personalized medicine strategies.
精神分裂症（SCZ）是一种严重的精神障碍，全球约1%的人受其影响。精神分裂症患者对抗精神病药物（APDs）反应的变异性给临床医生确定最有效的药物带来了重大挑战。在本研究中，我们基于对3269名中国精神分裂症患者的大规模全基因组关联研究，调查了生物学标志物并建立了抗精神病药物反应预测模型。每位参与者接受为期8周的治疗方案，使用五种单一抗精神病药物之一：奥氮平、利培酮、阿立哌唑、喹硫平或氨磺必利。通过将反应分为“高”、“中”和“低”三个有序组，我们减轻了治疗结果不明确的偏差，并在CDH12、WDR11和ELAVL2基因内或附近确定了三个新的显著相关基因位点。此外，我们开发了每种特定抗精神病药物反应的预测模型，准确率在79.5%至98.0%之间。总之，我们建立了一种有效的方法，通过整合新的生物标志物来指导个性化医疗策略，预测精神分裂症患者对抗精神病药物的反应，涵盖三个类别。
