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Abstract Number: 0795

Minimum Clinically Important Improvement in Patients with Rheumatoid Arthritis Associates with Gut Microbiome

Vinod Gupta1, Kevin Cunningham2, Benjamin Hur1, John Davis1 and Jaeyun Sung1, 1Mayo Clinic, Rochester, MN, 2University of Minnesota, Minneapolis, MN

Meeting: ACR Convergence 2020

Keywords: Biomarkers, Biostatistics, Disease Activity, microbiome, rheumatoid arthritis

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Session Information

Date: Saturday, November 7, 2020

Session Title: RA – Etiology & Pathogenesis Poster

Session Type: Poster Session B

Session Time: 9:00AM-11:00AM

Background/Purpose: Within the past decade, there have been several major discoveries in cross-sectional gut microbiome studies suggesting that dysbiosis of the gut microbiota is a key hallmark in Rheumatoid arthritis (RA). However, the association of gut microbiome with improvement in disease activity in RA patients remains unknown. In this study, we aimed to investigate the association of minimum clinically important improvement (MCII) in RA disease activity with gut microbiome of RA patients. In addition, we generated a machine-learning model, incorporating gut microbiome and clinical data, that could predict the course of RA irrespective of the treatment strategy.

Methods: Illumina based DNA shotgun sequencing was performed on 72 stool samples, which were collected at two time-points, from 36 RA patients. Disease activity and gut microbiomes were assessed in order to investigate the association of gut microbiome with MCII (i.e., CDAI change of at least 1 for low (CDAI less than 10); of 6 for moderate (CDAI 10–22); and of at least 12 for high (CDAI greater than 22) disease activity) in RA disease activity. Machine-learning models based on XGBoost were also generated based on gut microbiome composition and clinical data at baseline to predict the future disease activity in RA patients.

Results: We found that distributions of gut microbiome profiles were significantly different in patients of MCII+ (i.e., patients who show improvement in disease activity) and MCII– groups (i.e., patients who did not show improvement). At baseline, Fisher’s alpha diversity and species richness were significantly higher (Fig.1b), and five microbial taxa, including Negativicutes, Selenomonadales, Prevotellaceae, Coprococcus, and Ruminococcus sp., were significantly more abundant among patients of MCII+ group than those of MCII– group (Fig.1b). Biochemical pathway-level analysis of baseline stool metagenomes suggested that eight MetaCyc pathways, including ornithine biosynthesis, arginine biosynthesis, and rhamnose degradation were significantly decreased in RA patients of MCII– group (Fig.1c). For the machine-learning model, the Pearson correlation coefficient between predicted and actual disease activity score (CDAI) at follow-up visits was 0.77 (P = 2.9 × 10-12), which shows that our machine-learning model, incorporating gut microbiome and clinical data, is very effective in forecasting the disease activity in RA patients.

Conclusion: Our findings confirm the association of gut microbiome with MCII in disease activity in RA patients, and highlights the importance of gut microbiome in predicting the course of RA irrespective of the treatment strategy.

Figure 1. Significant differences in gut microbiomes of baseline stool samples between MCII+ and MCII- groups. (a) Higher distributions of Fisher’s alpha diversity and species richness were observed in baseline stool samples of patients of MCII+ group than in those of MCII- group. (b) Five taxa, Ruminococcus sp, Coprococcus, Prevotellaceae, Prevotellaceae, and Negativicutes, were significantly more abundant in baseline stool samples of patients of MCII+ group than those of MCII- group. (c) MetaCyc pathways that were significantly differentiated between MCII+ and MCII- groups in baseline stool samples. P-values shown above the box plots were found using multiple linear regression models on arcsine square-root transformed relative abundances of microbial taxa and MetaCyc pathways, while adjusting for age group (middle age: age < 64 years; and old age: age >= 64 years), sex, smoking status and use of csDMARDs (conventional synthetic Disease-Modifying Antirheumatic Drugs). *, P < 0.05; ns, not significant. Pathway codes: A, L-ornithine de novo biosynthesis; B, L-ornithine biosynthesis; C, L-arginine biosynthesis IV; D, L-arginine biosynthesis I; E, L-arginine biosynthesis III; F, L-arginine biosynthesis II; G, L-rhamnose degradation I; H, CMP-3-deoxy-D-manno-octulosonate biosynthesis I; I, tetrapyrrole biosynthesis I.


Disclosure: V. Gupta, None; K. Cunningham, None; B. Hur, None; J. Davis, Pfizer, 2, AbbVie, 5, 8, Sanofi-Genzyme, 5, 8; J. Sung, None.

To cite this abstract in AMA style:

Gupta V, Cunningham K, Hur B, Davis J, Sung J. Minimum Clinically Important Improvement in Patients with Rheumatoid Arthritis Associates with Gut Microbiome [abstract]. Arthritis Rheumatol. 2020; 72 (suppl 10). https://acrabstracts.org/abstract/minimum-clinically-important-improvement-in-patients-with-rheumatoid-arthritis-associates-with-gut-microbiome/. Accessed April 16, 2021.
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