目的 解析近10年临床治疗抗精神病药物所致代谢综合征（antipsychotic-induced metabolic syndromes，AIMS）的中药复方的用药规律及预测这些中药的潜在作用机制。方法 采用数据挖掘的方法对中文数据库和英文数据库中关于中药复方治疗AIMS的临床研究进行归纳总结，对其用药规律进行分析；再利用网络药理学方法预测高频中药的潜在作用靶点和作用通路。结果 从44篇文献中整理出复方50个（最高频复方为六郁汤），涉及86味中药（最高频君药为半夏）；通过频数统计和关联分析发现这些中药多味甘，性温，归脾、肺经。从中药数据库与分析平台（Traditional Chinese Medicine Database and Analysis Platform，TCMSP）数据库筛选出排名前5的高频中药已验证的靶点285个（经过合并、去重），从GeneCards数据库筛选出抗精神病药物奥氮平、氯氮平和利培酮的作用靶点675个（经过合并、去重），从DisGeNET数据库中筛选出代谢综合征相关的靶点1027个，将3部分靶点取交集，发现共有靶点33个。最后，将这33个靶点在String数据库构建蛋白质-蛋白质互作网络，将结果在Cytoscape软件中进行可视化并进行拓扑分析，找到核心靶点8个。采用Metascape数据库进行京都基因与基因组百科全书（Kyoto encyclopedia of genes and genomes，KEGG）分析，发现这些靶点主要富集在磷酸腺苷活化蛋白激酶（adenosine monophosphate activated protein kinase，AMPK）信号通路上。结论 治疗AIMS的中药属性及作用机制具备一定的分布规律，可为中药治疗AIMS提供理论依据，为进一步发掘其潜在作用机制提供方向。

Objective To analyze the rule of medication of Chinese materia medica formulae in the treatment of antipsychotic metabolic syndromes (AIMS) in recent 10 years and predict the potential mechanism. Methods Data mining method was used to summarize the studies of Chinese materia medica formulae in the treatment of AIMS in Chinese and English databases. The network pharmacology method was used to predict the potential targets and pathways of high-frequency herbs. Results From 44 studies, 50 formulas[the most used formulae was Liuyu Decoction (六郁汤)] and 86 traditional Chinese medicines[the most used monarch drug was Banxia (Pinelliae Rhizoma)] were sorted out. Through the frequency statistics and correlation analysis, it was found that most traditional Chinese medicines were sweet in taste, warm in nature, and attributed to the spleen and lung meridian. After merging and deweight, 285 validated targets of the most-used five traditional Chinese medicines were screened out through the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) database, 675 targets of olanzapine, clozapine and risperidone were screened out through the GeneCards database, and 1027 genes correlated with metabolic syndrome were found in the DisGeNET database. After overlapping those targets, 33 mutual targets were found. The protein-protein interaction networks were constructed for these 33 targets through the String database, and the results were visualized and topological analyzed in Cytoscape, and eight major targets were identified. The Kyoto encyclopedia of genes and genomes (KEGG) analysis was performed by the Metascape database and found that those targets were enriched in the adenosine monophosphate activated protein kinase (AMPK) signaling pathway. Conclusion The above results indicated that the traditional Chinese medicines which can treat AIMS have the distribution law in the properties and pharmacological mechanism to some extent, which may provide the theoretical basis for the herbs for the treatment of AIMS, and the direction for further study of the potential mechanism.

R285.1

国家重点研发计划“重大慢性非传染性疾病防控研究”重点专项（2016YFC1306900）；国家自然科学基金项目（81871051，82071500）；上海市精神心理疾病临床医学研究中心（19MC1911100）；上海市重性精神病重点实验室（13dz2260500）；上海市“科技创新行动计划”医学创新研究专项（21Y11921100）；复杂基质样本生物分析湖南省重点实验室（FZJZ-202101）；上海交通大学医学院高峰学科—临床医学“研究型医师”项目