Background: In rheumatoid arthritis (RA), dysregulation of intestinal microbiota and metabolism as well as associations with comorbidities are a subject of increasing scientific interest.

Objectives: The aim of this study was the characterization and correlation of the “OMICS” layers microbiota and metabolome with RA, as well as with developing RA before diagnosis (preRA).

Methods: For the analysis the FoodChain Plus (FoCus) cohort (n=1,795 participants) was used, which consists of a cross-sectional survey of the population, as well as subjects with obesity, diabetes and inflammatory diseases. Only subjects with available data for intestinal microbiota (16S rRNA gene sequencing from stool samples grouped in amplicon sequence variants), serum metabolome and nutrition data were included. For every subject with RA and every subject with preRA (no RA at biosampling but known to develop RA during follow-up), two matched controls were assigned. The serum metabolome was measured using direct injection FT-ICR mass spectrometry. The analysis was conducted using a semi-targeted approach and a customized local database (including metabolites from the “Human Metabolome Database” [1]). Identified metabolites were evaluated for the predictive value for RA and preRA by sparse partial least squares-discriminant analysis (sPLS-DA).

Results: For every subject with RA (n=60) and every subject with preRA (n=21), two matched controls were assigned. Compared to RA, those with preRA showed a higher BMI (median 28.2 VS 33.1, p<0.05). Chronic respiratory diseases were more prevalent in preRA compared to RA and controls (p<0.001). Significant differences in beta-diversity of the core measurable microbiota (CMM) between RA and preRA, RA and controls and preRA and controls were observed using Jaccard-index (p=0.01), but not in complete microbiota by Bray-Curtis distance (p>0.05). Differences of alpha diversity were not statistically significant when comparing RA and preRA with their matched controls (p>0.05). Via sPLS-DA 50 metabolites that most accurately discriminated RA, preRA and controls were identified. After adjusting by false discovery rate n=12 candidate metabolites remained (Kruskal-Wallis, p<0.05). For 132 subjects metabolome data from urine were available, no significant metabolites remained using the same exploratory approach.

Conclusion: Not only subjects with RA, but also those with preRA showed significant differences in gut microbiota composition, serum metabolome and comorbidities. The presented results are preliminary.

REFERENCES: [1] Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, Dizon R, Sayeeda Z, Tian S, Lee BL, Berjanskii M, Mah R, Yamamoto M, Jovel J, Torres-Calzada C, Hiebert-Giesbrecht M, Lui VW, Varshavi D, Varshavi D, Allen D, Arndt D, Khetarpal N, Sivakumaran A, Harford K, Sanford S, Yee K, Cao X, Budinski Z, Liigand J, Zhang L, Zheng J, Mandal R, Karu N, Dambrova M, Schiöth HB, Greiner R, Gautam V. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res. 2022 Jan 7;50(D1):D622-D631. DOI: 10.1093/nar/gkab1062.

Acknowledgements: NIL.

Disclosure of Interests: Jan Schirmer: None declared, Kristina Schlicht: None declared, Tobias Demetrowitsch: None declared, Nathalie Rohmann: None declared, Kathrin Türk: None declared, Dominik Schulte: None declared, Katharina Hartmann: None declared, Ute Settgast: None declared, Andre Franke: None declared, Karin Schwarz: None declared, Stefan Schreiber Abbvie, Amgen, Arena, Biogen, BMS, Celgene, Celltrion, Falk, Ferring, Fresenius Kabi, Galapagos, Gilead, HIKMA, IMAB, Janssen, Lilly, MSD, Mylan, Novartis, Pfizer, Protagonist, Provention Bio, Roche, Sandoz/Hexal, Takeda and Theravance, Bimba Franziska Hoyer: None declared, Matthias Laudes: None declared.