LAUSR

Crohn’s disease (CD) and ulcerative colitis (UC) evolve with alternate outbreaks and remissions of variable duration. Tumour necrosis factor α antagonists (anti-TNFα) have enhanced the treatment of patients with inflammatory bowel disease (IBD), improving the patient’s quality of life by reducing the number of surgeries and hospitalizations. Despite these advances, about 10–30% of patients do not respond to the treatment after the induction period. Recent studies have pointed, on one hand, gut microbiota can play a role in the anti-TNFα treatment response as gram-positive bacteria can modulate the response of NOD proteins and, on the other hand, gram-negative bacteria can stimulate TLR4 receptors causing activation of NFkß. This study aimed to define a microbial signature that could be used to predict the response of patients with CD and UC to anti-TNFα treatment. This observational study consisted of obtaining a stool sample from 38 IBD patients before starting an anti-TNFα treatment. Patients were recruited at Hospital Universitari Dr. Josep Trueta (Girona) and Hospital Universitari de Bellvitge (l’Hospitalet de Llobregat). During the one-year follow-up period, disease activity levels, faecal calprotectin evolution, and anti-TNFα antibody levels were analysed to assess response to treatment, differentiating 2 groups: responders and non-responders. From each sample, DNA was purified and used in a qPCR for the quantification of the following markers: F. prausnitzii (Fpra) and its phylogroups (PHG-I and PHG-II), E. coli (Eco), A. muciniphila (Akk), Ruminococcus sp. (Rum), Bacteroidetes (Bac), M. smithii (Msm), and the total bacterial load (Eub). In this proof of concept, the predictive ability to identify anti-TNFα treatment responders was analysed. Individually, none of biomarkers demonstrated the ability to differentiate between groups with high sensitivity and specificity. However, an algorithm consisting of the combination of 5 microbial markers (Msm, Fpra, PHGII, Rum, and Eub) showed a high capacity to discriminate between responders and non-responders. The algorithm proved high sensitivity and specificity reporting values ​​of 93.33% and 100%, respectively, with a positive predictive value of 100% and a negative predictive value of 75% for predicting response to biologic treatment. A specific microbial signature could beneficiate patients with IBD by predicting the therapeutic effectiveness of an anti-TNFα treatment, which could lead to a personalized therapy, improving the patients’ quality of life, saving costs, and gaining time in patient recovery.A larger prospective study will be needed to validate these results.