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The following is a summary of “A predictive model based on the gut microbiota improves the diagnostic effect in patients with rheumatoid arthritis,” published in the January 2025 issue of Rheumatology by Wang et al. 

Rheumatoid arthritis (RA) is an autoimmune disease affecting joints, with gut microbiota dysbiosis linked to its progression. 

Researchers conducted a retrospective study to develop an early diagnostic method for RA based on gut microbiota. 

They recruited 262 patients with RA and 475 healthy controls (HCs), collected faecal samples, and extracted microbial DNA. They amplified the V3-V4 region of the 16S rRNA gene via polymerase chain reaction (PCR) and sequenced it using Illumina MiSeq. They incorporated dataset PRJNA450340 from the European Nucleotide Archive (ENA), processed data with QIIME2, and built diagnostic models using random forest (RF), support vector machine (SVM), and generalized linear model (GLM). They used self-test data as the training set and PRJNA450340 as the validation set. 

The results showed that patients with RA had significantly lower gut microbial α-diversity than HCs. β-diversity analysis revealed distinct microbiota structures between groups. Differences were evident at the phylum and genus levels. LEfSe identified 7 key genera: Ruminococcus_gnavus_group, Fusicatenibacter, Butyricicoccus, Subdoligranulum, Erysipelotrichaceae_UCG-003, Romboutsia, and Dorea. RF, SVM, and GLM models were developed using these genera. The GLM model performed consistently, with an area under the curve (AUC) of 71.03% in the training set and 74.71% in the validation set. 

Investigators found notable gut microbiota differences between patients with RA and healthy individuals. They showed that diagnostic models using key microbial genera could aid early RA identification. 

Source: academic.oup.com/rheumatology/advance-article-abstract/doi/10.1093/rheumatology/keae706/7985590