A metabolomic perspective on coeliac disease

Abstract

Metabolomics is an "omic" science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolism.

Figure 1

Relationships between the omics sciences.

Figure 2

Examples of NMR profiles of (a) serum, (b) urine, (c) saliva, and (d) faecal extract.

Figure 3

(a) Clustering of CMPG (Carr-Purcell-Meiboom-Gill spin echo sequence) [83] serum spectra of CD patients (filled circles) and controls (open circles). The discriminant model between the two groups was calculated using a combination of partial least square [89] and (regularized) canonical analysis [90] (PLS-RCC) and was validated using cross-validation. The CPMG spectra of 13 (out of the 34) CD patients after 12 months of gluten-free diet were then projected into the discriminant space of the model (stars) and were assigned to the CD or the healthy group applying a support vector machine [91] classifier (SVM). (b) Clustering of overt CD patients (open circles) and healthy controls (filled circles) obtained with CPMG serum spectra. The discriminant model was calculated using orthogonal partial least square [92] (OPLS) and validated using double cross-validation [93]. The CPMG spectra of 29 potential CD patients were then projected in the model (triangles) and filled or not according to the results of an SVM classifier. Adapted with permission from [49, 50]. Copyright (2009 and 2011) American Chemical Society.