Gut Microbiome Predicts Clinically Important Improvement in Patients with Rheumatoid Arthritis

Background Rapid advances in the past decade have shown that dysbiosis of the gut microbiome is a key hallmark of rheumatoid arthritis (RA). Yet, the relationship between gut microbiome and clinical improvement in RA disease activity remains unclear. In this study, we explored the gut microbiome of patients with RA to identify features that are associated with, as well as predictive of, minimum clinically important improvement (MCII) in disease activity. Methods Whole metagenome shotgun sequencing was performed on 64 stool samples, which were collected from 32 patients with RA at two separate time-points. The Clinical Disease Activity Index (CDAI) of each patient was measured at both time-points to assess achievement of MCII; depending on this clinical status, patients were distinguished into two groups. Multiple linear regression models were used to identify microbial taxa and biochemical pathways associated with MCII while controlling for potentially confounding factors. Lastly, a deep-learning neural network was trained upon gut microbiome, clinical, and demographic data at baseline to classify patients according to MCII status, thereby enabling the prediction of whether a patient will achieve MCII at follow-up. Results We determined that MCII status can explain a significant proportion of the overall compositional variance in the gut microbiome (R2 = 3.8%, P = 0.005, PERMANOVA). Additionally, by looking at patients baseline gut microbiome profiles, we observed significantly different microbiome traits between patients who eventually showed MCII and those who did not. Taxonomic features include alpha- and beta-diversity measures, as well as several microbial taxa, such as Coprococcus, Bilophila sp. 4_1_30, and Ruminococcus sp. Functional profiling identified thirteen biochemical pathways, most of which were involved in the biosynthesis of L-arginine and L-methionine, to be differentially abundant between the MCII patient groups. In addition to these observations at baseline, we found microbiome features that vary differently in fold-change (from baseline to follow-up) between the two patient groups. These results could suggest that, depending on the clinical course, gut microbiomes not only start at different ecological states, but also are on separate trajectories. Finally, the neural network proved to be highly effective in predicting which patient will achieve MCII (balanced accuracy = 90.0%), demonstrating potential clinical utility of gut microbiome profiles. Conclusions Our findings confirm the presence of taxonomic and functional signatures of the gut microbiome associated with MCII in RA patients. Ultimately, the gut microbiome may aid in the development of non-invasive tools for predicting future prognosis in RA.