Liu Feng-Qi, An Zhuo-Yu, Cui Li-Juan, Xiao Meng-Yu, Wu Ye-Jun, Li Wei, Zhang Bang-Shuo, Yu Li, Feng Jia, Liu Zhuo-Gang, Feng Ru, Jiang Zhong-Xing, Huang Rui-Bin, Jing Hong-Mei, Ren Jin-Hai, Zhu Xiao-Yu, Cheng Yun-Feng, Li Yu-Hua, Zhou He-Bing, Gao Da, Liu Yi, Yu Fan, Wang Xin, Qiao Jian-Lin, Hu Dai-Hong, Wang Lu-Lu, Zang Meng-Tong, Chen Qi, Qu Qing-Yuan, Zhou Jian-Ying, Li Meng-Lin, Chen Yu-Xiu, Huang Qiu-Sha, Fu Hai-Xia, Li Yue-Ying, Wang Qian-Fei, Huang Xiao-Jun, Zhang Xiao-Hui
Peking University People's Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Collaborative Innovation Center of Hematology, Beijing, 100044, China.
Department of Hematology, The General Hospital of Ningxia Medical University, Yinchuan, 750004, China.
Adv Sci (Weinh). 2025 Jun;12(22):e2410417. doi: 10.1002/advs.202410417. Epub 2025 Mar 5.
Corticosteroids (CSs) are the initial therapy for immune thrombocytopenia (ITP); however, their efficacy is not adequately predicted. As a novel biomarker, the composition of the gut microbiota is non-invasively tested and altered in patients with ITP. This study aims to develop a predictive model that leverages gut microbiome data to predict the CS response in patients with ITP within the initial four weeks of treatment. Metagenomic sequencing is performed on fecal samples from 212 patients with ITP, 152 of whom underwent CS treatment and follow-up. Predictive models are trained using six machine-learning algorithms, integrating clinical indices and gut microbiome data. The support vector machine (SVM) algorithm-based model has the highest accuracy (AUC = 0.80). This model utilized a comprehensive feature set that combined clinical data (including sex, age, duration, platelet count, and bleeding scales) with selected microbial species (including Bacteroides ovatus, Bacteroides xylanisolvens, and Parabacteroides gordonii), alpha diversities, KEGG pathways, and microbial modules. This study will provide new ideas for the prediction of clinical CS efficacy, enabling informed decision-making regarding the initiation of CS or personalized treatment in patients with ITP.
糖皮质激素（CSs）是免疫性血小板减少症（ITP）的初始治疗方法；然而，其疗效尚无法得到充分预测。作为一种新型生物标志物，肠道微生物群的组成可通过非侵入性方式检测，且ITP患者的肠道微生物群组成会发生改变。本研究旨在开发一种预测模型，利用肠道微生物组数据预测ITP患者在治疗最初四周内对CS的反应。对212例ITP患者的粪便样本进行宏基因组测序，其中152例接受了CS治疗及随访。使用六种机器学习算法训练预测模型，整合临床指标和肠道微生物组数据。基于支持向量机（SVM）算法的模型具有最高的准确率（AUC = 0.80）。该模型利用了一个综合特征集，将临床数据（包括性别、年龄、病程、血小板计数和出血量表）与选定的微生物种类（包括卵形拟杆菌、解木聚糖拟杆菌和戈登副拟杆菌）、α多样性、KEGG通路及微生物模块相结合。本研究将为临床CS疗效的预测提供新思路，有助于在ITP患者中就CS的起始治疗或个性化治疗做出明智决策。
