The metabonomic signature of celiac disease

Ivano Bertini*, Antonio Calabró, Valeria De Carli, Claudio Luchinat, Stefano Nepi, Berardino Porfirio, Daniela Renzi, Edoardo Saccenti, Leonardo Tenori

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

121 Citations (Scopus)

Abstract

Celiac disease (CD) is a multifactorial disorder involving genetic and environmental factors, thus, having great potential impact on metabolism. This study aims at defining the metabolic signature of CD through Nuclear Magnetic Resonance (NMR) of urine and serum samples of CD patients. Thirty-four CD patients at diagnosis and 34 healthy controls were examined by 1H NMR of their serum and urine. A CD patients' subgroup was also examined after a gluten-free diet (GFD). Projection to Latent Structures provided data reduction and clustering, and Support Vector Machines provided pattern recognition and classification. The classification accuracy of CD and healthy control groups was 79.7-83.4% for serum and 69.3% for urine. Sera of CD patients were characterized by lower levels (P < 0.01) of several metabolites such as amino acids, lipids, pyruvate and choline, and by higher levels of glucose and 3-hydroxybutyric acid, while urines showed altered levels (P < 0.05) of, among others, indoxyl sulfate, meta-[hydroxyphenyl]propionic acid and phenylacetylglycine. After 12 months of GFD, all but one of the patients were classified as healthy by the same statistical analysis. NMR thus reveals a characteristic metabolic signature of celiac disease. Altered serum levels of glucose and ketonic bodies suggest alterations of energy metabolism, while the urine data point to alterations of gut microbiota. Metabolomics may thus provide further hints on the biochemistry of the disease.

Original languageEnglish
Pages (from-to)170-177
Number of pages8
JournalJournal of Proteome Research
Volume8
Issue number1
DOIs
Publication statusPublished - 1 Jan 2009

Keywords

  • Celiac disease
  • Energy metabolism
  • Gut microflora
  • NMR spectroscopy
  • Support vector machines

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