Evidence- and consensus-based recommendations for selecting the goals for treat-to-target strategies in patients with IBD are made available. Prospective studies are needed to determine how these targets will change disease course and patients' quality of life.

No abstract

This study highlights the potential for metabolomics to distinguish IBD from the healthy state but shows that careful consideration must be given to establishing disease-representative cohorts that are free of confounding factors.

Inflammatory bowel disease (IBD) is a chronic debilitating disorder that is thought to have both genetic and environmental contributors. Commensal microflora have been shown to play a key part in the disease process. Metabolomics, the study of large numbers of small molecule metabolites, has demonstrated that disease and/or changes in gut microbial composition modulate mammalian urine metabolite fingerprints. The aim of this project was to associate the development of IBD with specific changes in a mouse urinary metabolic fingerprint. Interleukin-10 (IL-10) gene-deficient mice were raised alongside age-matched 129/SvEv controls in conventional housing. Urine samples (22 h) were collected at ages 4, 6, 8, 12, 16, and 20 weeks. Metabolite concentrations were derived from analysis of nuclear magnetic resonance spectra, and both multivariate and two-way analysis of variance (ANOVA) statistical techniques were applied to the resulting data. Principal component analysis and partial least-squares-discriminant analysis of urine derived from the control and IL-10 gene-deficient mice revealed that while both groups initially had similar metabolic profiles, they diverged substantially with the onset of IBD as assessed through external phenotypic changes. Several metabolites, including trimethylamine (TMA) and fucose, changed dramatically in the IL-10 gene-deficient mice following 8 weeks of age, concomitant with the known timeline for development of severe histological injury. This study illustrates that metabolomics is effective at distinguishing IBD using urinary metabolite profiles.