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Development of machine-learning-driven signatures for diagnosing and monitoring therapeutic response in major depressive disorder using integrated immune cell profiles and plasma cytokines

文献类型：期刊论文

作者He, Shen10; Zhao, Faming5,9; Sun, Guangqiang3,6; Shi, Yue10; Xu, Tianlun10; Zhang, Yu6; Li, Siyuan10; Zhang, Linna7; Chu, Xingkun8; Du, Chen8
刊名THERANOSTICS
出版日期2024
卷号14期号:18页码:7265-7280
关键词major depressive disorder immune cell CyTOF cytokines machine-learning biomarkers
ISSN号1838-7640
DOI10.7150/thno.102602
英文摘要Background: Diagnosis and treatment efficacy of major depressive disorder (MDD) currently lack stable and reliable biomarkers. Previous research has suggested a potential association between immune cells, cytokines, and the pathophysiology and treatment of MDD. Objective: This study aims to investigate the relationship between immune cells, cytokines, and the diagnosis of MDD and treatment response, further utilizing machine learning algorithms to develop robust diagnostic and treatment response prediction models. Methods: Using mass cytometry by time-of-flight (CyTOF) technology and high-throughput cytokine detection, we analyzed 63 types of immune cells from 134 pre-treatment MDD patients. Among these patients, plasma data for 440 cytokines were obtained from 84 individuals. Additionally, we conducted the same set of immune cell and cytokine analyses on 50 healthy controls (HC). An 8-week follow-up was conducted to observe post-treatment changes in immune cells and cytokines. Results: By combing eight machine-learning algorithms with CyTOF and cytokine data, we constructed a diagnostic model for MDD patient with 16 indicators, achieving an AUC of 0.973 in the internal validation set. Additionally, a treatment response prediction model based 7 cytokines was developed, resulting in an AUC of 0.944 in the internal validation set. Furthermore, Mfuzz time-series analysis revealed that cytokines such as Basic fibroblast growth factor (bFGF), Interleukin 13 (IL-13), and Interleukin 1 receptor, type I (IL1R1) that revert towards normal levels after 8 weeks of treatment, suggesting their potential as therapeutic targets for MDD. Conclusions: Our diagnostic model derived from CyTOF and cytokines demonstrates high diagnostic value. However, relying solely on immune cells may not provide optimal predictions for antidepressant treatment response. In contrast, leveraging cytokines has proven valuable, leading to the construction of a seven-factor treatment response prediction model. Importantly, we observed that several significantly altered cytokines in MDD can normalize following antidepressant treatment, indicating their potential as therapeutic targets.
WOS关键词ANTIDEPRESSANT RESPONSE ; GROWTH-FACTOR
资助项目Collaborative Innovation Center for Clinical and Translational Science by Ministry of Education Shanghai[CCTS-202306] ; Collaborative Innovation Center for Clinical and Translational Science by Ministry of Education Shanghai[CCTS-202409PT] ; Shanghai Hospital Development Center[SHDC2020CR2053B] ; National Natural Science Foundation of China[81901371] ; Lingang Laboratory[LG202101-01-01] ; Shandong Laboratory Program[SYS202205] ; Shanghai Municipal Science and Technology Major Project
WOS研究方向Research & Experimental Medicine
WOS记录号WOS:001358746400020
出版者IVYSPRING INT PUBL
源URL[http://119.78.100.183/handle/2S10ELR8/314983]  
专题新药研究国家重点实验室
通讯作者Huang, Jingjing; Xie, Zuoquan; Li, Huafang
作者单位1.Shanghai Jiao Tong Univ, Sch Med, Shanghai Mental Hlth Ctr, 600 South Wan Ping Rd, Shanghai 200030, Peoples R China
2.Shanghai Key Lab Psychot Disorders, Shanghai, Peoples R China
3.Bohai Rim Adv Res Inst Drug Discovery, Shandong Lab Yantai Drug Discovery, Yantai 264117, Shandong, Peoples R China
4.Tongji Univ, Clin Res Ctr Mental Disorders, Shanghai Pudong New Area Mental Hlth Ctr, Sch Med, Shanghai, Peoples R China
5.Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Publ Hlth, Minist Environm Protect, Wuhan, Peoples R China
6.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China
7.Xinxiang Med Univ, Dept Neurol, Affiliated Hosp 2, Xinxiang 453002, Henan, Peoples R China
8.Green Valley Pharmaceut Technol Co Ltd, Shanghai 201203, Peoples R China
9.Huazhong Univ Sci & Technol, Tongji Med Coll,Minist Educ, Sch Publ Hlth, Key Lab Environm Hlth, Wuhan, Peoples R China
10.Shanghai Jiao Tong Univ, Sch Med, Shanghai Mental Hlth Ctr, Dept Psychiat, Shanghai, Peoples R China
推荐引用方式
GB/T 7714		 He, Shen,Zhao, Faming,Sun, Guangqiang,et al. Development of machine-learning-driven signatures for diagnosing and monitoring therapeutic response in major depressive disorder using integrated immune cell profiles and plasma cytokines[J]. THERANOSTICS,2024,14(18):7265-7280.
APA He, Shen.,Zhao, Faming.,Sun, Guangqiang.,Shi, Yue.,Xu, Tianlun.,...&Li, Huafang.(2024).Development of machine-learning-driven signatures for diagnosing and monitoring therapeutic response in major depressive disorder using integrated immune cell profiles and plasma cytokines.THERANOSTICS,14(18),7265-7280.
MLA He, Shen,et al."Development of machine-learning-driven signatures for diagnosing and monitoring therapeutic response in major depressive disorder using integrated immune cell profiles and plasma cytokines".THERANOSTICS 14.18(2024):7265-7280.

入库方式： OAI收割

来源：上海药物研究所

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