Wang Qi, Li Chen-Long, Yu Si-Yuan, Dong Hui-Jing, Yang Lei, Liu Yang, He Pei-Feng, Zhang Sheng-Xiao, Yu Qi
School of Management, Shanxi Medical University, Taiyuan, China.
School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.
Rheumatology (Oxford). 2025 Jun 1;64(6):4014-4021. doi: 10.1093/rheumatology/keae706.
Rheumatoid arthritis (RA) is a chronic, destructive autoimmune disorder predominantly targeting the joints, with gut microbiota dysbiosis being intricately associated with its progression. The aim of the present study is to develop effective early diagnostic methods for early RA based on gut microbiota.
A cohort comprising 262 RA patients and 475 healthy controls (HCs) was recruited. Faecal samples were collected from all participants, and microbial DNA was subsequently extracted. The V3-V4 region of the 16S rRNA gene was amplified via polymerase chain reaction (PCR) and subjected to high-throughput sequencing using the Illumina MiSeq platform. Additionally, a dataset with the accession number PRJNA450340 from the European Nucleotide Archive (ENA) was incorporated into the study. The sequencing data underwent processing and analysis utilizing QIIME2. To construct microbiome-based diagnostic models, Random Forest (RF), Support Vector Machine (SVM) and Generalized Linear Model (GLM) methodologies were employed, with the self-test data functioning as the training set and the PRJNA450340 dataset serving as the validation set.
The results indicated that patients with RA exhibited a significantly reduced gut microbial α-diversity compared with the HCs group. The β-diversity analysis demonstrated notable distinctions in the gut microbiota structure between RA patients and HCs. Variations in the gut microbiome composition between RA patients and HCs were evident at both the phylum and genus levels. LEfSe analysis revealed a substantial number of significantly different microbiota between RA patients and HC, and seven key genera were obtained by intersection of the different flora in the two data sets: Ruminococcus_gnavus_group, Fusicatenibacter, Butyricicoccus, Subdoligranulum, Erysipelotrichaceae_UCG-003, Romboutsia and Dorea. Utilizing these seven core genera, RA diagnostic models were developed employing RF, SVM and GLM methodologies. The GLM model exhibited consistent performance, achieving an area under the curve (AUC) of 71.03% in the training set and 74.71% in the validation set.
Notable differences in gut microbiota exist between RA patients and healthy individuals. Diagnostic models based on key microbial genera hold potential for aiding in the early identification of individuals at risk for developing RA, thereby suggesting new avenues for its diagnosis.
类风湿性关节炎（RA）是一种主要累及关节的慢性破坏性自身免疫性疾病，肠道微生物群失调与其病情进展密切相关。本研究旨在基于肠道微生物群开发针对早期类风湿性关节炎的有效早期诊断方法。
招募了一个由262例类风湿性关节炎患者和475名健康对照（HC）组成的队列。收集所有参与者的粪便样本，随后提取微生物DNA。通过聚合酶链反应（PCR）扩增16S rRNA基因的V3-V4区域，并使用Illumina MiSeq平台进行高通量测序。此外，将来自欧洲核苷酸档案库（ENA）中登录号为PRJNA450340的数据集纳入研究。利用QIIME2对测序数据进行处理和分析。为构建基于微生物群的诊断模型，采用了随机森林（RF）、支持向量机（SVM）和广义线性模型（GLM）方法，将自测数据作为训练集，PRJNA450340数据集作为验证集。
结果表明，与健康对照（HC）组相比，类风湿性关节炎患者的肠道微生物α多样性显著降低。β多样性分析表明，类风湿性关节炎患者和健康对照（HC）的肠道微生物群结构存在显著差异。类风湿性关节炎患者和健康对照（HC）之间肠道微生物群组成在门和属水平上均有明显差异。线性判别分析效应大小（LEfSe）分析显示，类风湿性关节炎患者和健康对照（HC）之间存在大量显著不同的微生物群，通过两个数据集中不同菌群的交集获得了七个关键属：纤细瘤胃球菌群、梭菌属、丁酸球菌属、副拟杆菌属、丹毒丝菌科UCG-003、罗姆布茨菌属和多雷亚菌属。利用这七个核心属，采用随机森林（RF）、支持向量机（SVM）和广义线性模型（GLM）方法建立了类风湿性关节炎诊断模型。广义线性模型（GLM）模型表现出一致的性能，在训练集中曲线下面积（AUC）达到71.03%，在验证集中达到74.71%。
类风湿性关节炎患者与健康个体的肠道微生物群存在显著差异。基于关键微生物属的诊断模型有助于早期识别有患类风湿性关节炎风险的个体，从而为类风湿性关节炎的诊断提供新途径。
