4.5 Article

Novel Prognostic Biomarkers of Mucosal Healing in Ulcerative Colitis Patients Treated With Anti-TNF: Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio

Journal

INFLAMMATORY BOWEL DISEASES
Volume 26, Issue 10, Pages 1579-1587

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/ibd/izaa062

Keywords

mucosal healing; ulcerative colitis; biomarkers

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Background Anti-tumor necrosis factor drugs (anti-TNFs) are widely used for the treatment of ulcerative colitis (UC). However, many patients experience loss of response during the first year of therapy. An early predictor of clinical remission and mucosal healing is needed. The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are markers of subclinical inflammation poorly evaluated in UC patients treated with anti-TNFs. The aim of this multicenter study was to evaluate whether NLR and PLR could be used as prognostic markers of anti-TNF treatment response. Methods Patients with UC who started anti-TNF treatment in monotherapy were evaluated. Patients with concomitant corticosteroid treatment >= 20 mg were excluded. We calculated NLR, PLR, and fecal calprotectin before treatment and after induction. The values of NLR and PLR were correlated with clinical remission and mucosal healing at the end of follow-up (54 weeks) using the Mann-Whitney U test and then multivariate analysis was conducted. Results Eighty-eight patients were included. Patients who reached mucosal healing after 54 weeks of therapy displayed lower levels of both baseline NLR and PLR (P = 0.0001 and P = 0.04, respectively); similar results were obtained at week 8 (P = 0.0001 and P = 0.001, respectively). Patients who presented with active ulcers at baseline endoscopic evaluation had higher baseline NLR and PLR values compared with those without detected ulcers (P = 0.002 and P = 0.0007, respectively). Conclusions BothNLR and PLR showed a promising role as early predictors of therapeutic response to anti-TNF therapy in UC patients. If confirmed in larger studies, classification and regression trees proposed in this article could be useful to guide clinical decisions regarding anti-TNF treatment.

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