Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis

Vinod K. Gupta, Kevin Y. Cunningham, Benjamin Hur, Utpal Bakshi, Harvey Huang, Kenneth J. Warrington, Veena Taneja, Elena Myasoedova, John M. Davis, Jaeyun Sung

Research output: Contribution to journalArticlepeer-review


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 the 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: We conducted a retrospective, observational cohort study on patients diagnosed with RA between 1988 and 2014. Whole metagenome shotgun sequencing was performed on 64 stool samples, which were collected from 32 patients with RA at two separate time-points approximately 6–12 months apart. 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: MCII+ (who achieved MCII; n = 12) and MCII− (who did not achieve MCII; n = 20). 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 found age to be the largest determinant of the overall compositional variance in the gut microbiome (R2 = 7.7%, P = 0.001, PERMANOVA). Interestingly, the next factor identified to explain the most variance in the gut microbiome was MCII status (R2 = 3.8%, P = 0.005). 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 Eubacterium sp. 3_1_31. Notably, patients who achieved clinical improvement had higher alpha-diversity in their gut microbiomes at both baseline and follow-up visits. Functional profiling identified fifteen biochemical pathways, most of which were involved in the biosynthesis of L-arginine, L-methionine, and tetrahydrofolate, to be differentially abundant between the MCII patient groups. Moreover, MCII+ and MCII− groups showed significantly different fold-changes (from baseline to follow-up) in eight microbial taxa and in seven biochemical pathways. 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 patients will achieve MCII (balanced accuracy = 90.0%, leave-one-out cross-validation), 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, modifying the gut microbiome to enhance clinical outcome may hold promise as a future treatment for RA.

Original languageEnglish (US)
Article number149
JournalGenome medicine
Issue number1
StatePublished - Dec 2021


  • Clinical disease activity index
  • Deep-learning neural network
  • Gut microbiome
  • Machine-learning
  • Minimum clinically important improvement
  • Rheumatoid arthritis
  • Shotgun metagenomic sequencing

ASJC Scopus subject areas

  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Genetics(clinical)


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