Histologic signatures as predictors of intestinal barrier impairment in inflammatory bowel disease: integration with endoscopic and molecular markers

Abstract Background and aims Intestinal barrier impairment (BI) is a hallmark of inflammatory bowel disease (IBD), driving immune activation, neutrophil-mediated injury, and chronic mucosal damage. While endoscopy and molecular profiling provide insight into epithelial and vascular assessment, the combined prognostic role of histology and BI remains insufficiently defined. This study aimed to determine the association between histology-derived signatures and BI, and identify outcome predictors by integrating histologic, advanced imaging, and molecular data. Methods IBD patients underwent in vivo epithelial and vascular barrier assessment using probe-based confocal laser endomicroscopy and endocytoscopy. Biopsies were collected for histologic and protein expression analysis. Epithelial (ZO-1, Claudin-2, E-cadherin) and vascular-barrier proteins (CD31, PV-1) were analyzed using confocal microscopy and automated quantification with QuPath. Correlation among histology, advanced imaging, and protein expression was assessed, together with the ability to predict outcome at 12 months of follow-up. Results Eighty-eight IBD patients (52 Crohn’s disease CD and 36 ulcerative colitis UC) were included. BI was identified in 35/52 (67%) CD and 28/36 (78%) UC patients. Epithelial and lamina propria neutrophils correlated strongly with epithelial BI (r = 0.6). Chronic inflammatory infiltrate correlated significantly with vascular BI (r = 0.5) and PV-1 expression (r = 0.3) in CD. Finally, CD patients with both barrier alterations (advanced imaging + PV-1) and inflammatory infiltrate showed a higher rate of adverse outcomes than those with single alterations. Conclusion Histologic signatures correlate significantly with BI in IBD. Multimodal evaluation combining histology, advanced imaging, and molecular profiling improves outcome prediction beyond single-modality assessment and may identify patients with subclinical disease persistence, supporting accurate risk stratification and personalized management.