OP0072 Identification and Validation of Diagnostic and Activity Urinary Metabolomic Biomarkers in Immune-Mediated Inflammatory Diseases. (9th June 2015)

• Record Type: Journal Article
• Authors: Alonso, A.; Tornero, J.; Nebro, A.F.; Cañete, J.; Domènech, E.; Gisbert, J.P.; Ferrándiz, C.; Fonseca, E.; García, V.; Blanco, F.; Rodríguez, J.; Gratacόs, J.; Carreira, P.; Tortosa, R.; Lόpez-Lasanta, M.; Julià, A.; Correig, X.; Marsal, S.
• Is Part Of: Annals of the rheumatic diseases. Volume 74(2015)Supplement 2
• Page Start: 95
• Page End: 96
• Publication Date: 2015-06-09
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2015-eular.3321
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: The recent advances in metabolomics have allowed the study of the regulatory processes linked to metabolism. The holistic analysis of the metabolites in biological samples can provide new insights to identify pathological processes and to develop new biomarkers. The present study represents the first high-throughput metabolomics analysis of immune-mediated inflammatory diseases (IMIDs). Objectives: Identification and validation of diagnostic and activity biomarkers through the analysis of the urine metabolome within two independent cohorts of >2, 500 individuals including healthy controls and IMID patients. Methods: The metabolomics analysis was performed using nuclear magnetic resonance on 2 independent cohorts. The discovery cohort included 100 controls and 200 patients per IMID: rheumatoid arthritis (RA), psoriatic arthritis (PsA), psoriasis (Ps), ulcerative colitis (UC), Crohn's disease (CD), and systemic lupus erythematosus (SLE). The validation cohort included 200 controls and 200 patients per IMID. The patients of each IMID were selected to define 2 groups: low and high disease activity patients. n=37 metabolites were accurately quantified. The association analyses were performed at 3 levels: diagnostic -comparing each IMID vs controls-, differential -comparing similar IMIDs between them-, and activity -comparing low and high activity patients of each IMID-. The statistical analysis was performed using linear regression adjusted by epidemiological variables. Results: The diagnostic analysis identified n=41 disease-metabolite associations, from which n=37 were replicated in the validation cohort. These associations involved n=15 different metabolites from which n=6 were jointly associated to ≥3 IMIDs (Figure ). When analyzing differences between IMIDs, we validated n=6 associations: n=5 when comparing CD vs UC, and n=1 when comparing RA vs PsA. We also validated at the nominal level 2 more associations related with CD vs UC and RA vs PsA. The analysis of disease activity identified and validated n=3 associations related with disease activity in CD patients. We also validated at the nominal level n=2 associations in UC and n=1 association in PsA, SLE and CD. Conclusions: We have identified and validated significant differences in metabolite concentrations when comparing IMID patients vs healthy controls. CD, UC and RA gathered the largest number of metabolic associations. Importantly, n=6 metabolites were associated to ≥3 IMIDs. These metabolites are then candidate proxies for the physiopathological processes shared by these diseases. Regarding to the discrimination between related IMIDs, the urine metabolome has shown significant differences when comparing CD vs UC and RA vs PsA. The disease activity analysis also identified significant associations but with a lower impact than that from the diagnostic analysis. Disclosure of Interest: None declared