Skip to main content
NCBI home page
UKPMC Funders Author Manuscripts logoLink to UKPMC Funders Author Manuscripts
. Author manuscript; available in PMC: 2014 Jul 1.
Published in final edited form as: Curr Opin Gastroenterol. 2013 Jul;29(4):378–383. doi: 10.1097/MOG.0b013e328361f488

Metabolomics: is it useful for IBD?

Martin Storr 1, Hans J Vogel 2, Rudolf Schicho 3
PMCID: PMC3693066  EMSID: EMS53744  PMID: 23624676

Abstract

Purpose of the review

The assessment of metabolite profiles in biofluids has become a powerful method for the detection of biomarker molecules and disease mechanisms. This review outlines recent advances in the use of metabolomic techniques to study inflammatory bowel diseases (IBD).

Recent findings

The past few years has seen an increase in studies of experimental and human IBD focusing on the search for small metabolites, such as amino acids, bases and tricarboxylic acid (TCA) cycle intermediates. Experimental methods for the screening of metabolites in serum, urine, fecal extracts and colon tissue include 1H NMR spectroscopy, gas chromatography-mass spectrometry and liquid chromatography methods. Several studies demonstrate that IBD patients and healthy individuals, as well as the IBD subtypes, can be distinguished using metabolic profiling. Metabolomic data of fecal extracts and urine have revealed disruptions in bacterial populations, findings that are indicative of a possible involvement of the microbiome in the development of IBD.

Summary

Metabolites from biofluids can be detected in IBD by different experimental methods that allow successful separation of IBD subtypes from healthy cohorts. Knowledge of a unique metabolomic fingerprint in IBD could be useful for diagnosis, treatment, and detection of disease mechanisms.

Keywords: Metabolic profile, metabolomics, metabonomics, multivariate analysis

Introduction

Several serologic markers for inflammatory bowel diseases (IBDs) are currently available. While they are invaluable as an adjunct to endoscopic and radiologic findings they are less helpful in differentiating between the two subtypes of IBD, i.e. Crohn’s disease (CD) and ulcerative colitis (UC), or in categorizing patients with indeterminate colitis. Thus, biomarkers that clearly discriminate between these two subtypes and between remission and flare-ups would be enormously useful for clinical practice. Additionally, changes in the concentrations of metabolites may reveal important clues about severity and/or activity of IBD. Such information is important to support diagnosis and to provide an insight into the underlying pathophysiological mechanisms of IBD, which are far from clear at present.

Techniques and analysis of metabolomics

Techniques of metabolomic research have an advantage of being extremely sensitive and of allowing to perform experiments in a cost-effective high-throughput manner. Common technologies used for metabolomics include (with increasing sensitivity) 1H NMR spectroscopy, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). Ion cyclotron resonance-Fourier transform mass spectrometry (ICR-FT/MS) with ultrahigh mass resolution has allowed for the separation of thousands of masses that correspond to small but complex metabolites [1] while another advanced high resolution technique (ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry) has recently been employed in experimental IBD [2*].

Measuring metabolites by NMR and MS generates a large amount of data which needs to be organized by multivariate analysis methods in order to extract the important information. Thus, metabolomic data are usually interpreted by statistical methods such as principal component analysis (PCA) and orthogonal partial least squares projections (OPLS) [3]. OPLS is based on projection to latent structures (PLS), a pattern recognition method that reduces numerous colinear variables (metabolites) to a few subsets to demonstrate their interdependence. OPLS-discriminant analysis (DA) maximizes the independence of variables, resulting in better separation of data between cohorts and therefore in better discrimination of diseased subjects from healthy ones (Fig. 1).

Fig. 1.

Fig. 1

Open in a new tab

shows scores plots of OPLS-DA coefficients from plasma metabolites measured by 1H NMR spectroscopy in patients with (A) ulcerative colitis (UC) and (B) Crohn’s disease (CD) demonstrating good separation of metabolites that could help to diagnose IBD subtypes in patients.

Metabolomics and models of IBD

Over the years, several mouse models of IBD have been used to measure differences in the metabolic profiles of serum, urine, fecal extracts and gut tissues between diseased and non-diseased animals. These models include the interleukin 10−/− knockout model [4,5,6,7], the TNFΔARE/WT model that mimics CD-like ileitis with great similarity [8*], and the dextran sulfate sodium (DSS)-induced experimental colitis model that largely resembles UC [9*,10,11]. 1H NMR spectroscopy [4,5,8*,10,11], GC-MS [6,9*] and LC-MS [8*] were employed to profile metabolites in these models. Despite having lower sensitivity, 1H NMR spectroscopy may be preferred to MS because of its higher reproducibility [12*] and the application of ‘targeted’ metabolomics. ‘Targeted’ (quantitative) metabolic profiling in 1H NMR spectroscopy, as compared to the traditional spectral binning, significantly improves the chances for the discovery of disease-related biomarkers. The technique involves the analysis of a large group of compounds whose characteristics (e.g., individual NMR spectra) are established and stored in a database library. From the complex mixture of individual metabolite spectra in the biofluid, the spectra in the database can be used to identify and quantify the targeted metabolites [13]. Quantitative metabolic profiling has already been used in two different mouse models of colitis to discriminate between diseased vs. non-diseased states [4,10] and in serum, plasma and urine of IBD patients [14**].

Changes in metabolic profiles: a possible clue to pathogenesis?

Changes in the metabolism of cholesterol, trigycerides and phospholipids that were demonstrated in a mouse model of Crohn’s disease [8*], highlight the importance of inflammatory lipid mediators as potential biomarkers of IBD. H. hepaticus Rag2−/− mice, another model of IBD, revealed significant differences between diseased and non-diseased animals in compounds involved in tryptophan, fatty acid and purine metabolism, as well as in methionine-homocysteine and TCA cycles [15*]. Some of these changes, such as those in tryptophan metabolism and TCA cycle, were also observed in other studies of experimental IBD [5,10]. In IL-10−/− knockout mice, a model in which mice develop a spontaneous colitis, NMR-based metabolic profiling was studied over time and revealed many changes in molecules related to energy household including lactate, pyruvate and citrate [4,5]. In a DSS colitis model, metabolic profiles were measured by GC-MS to differentiate between acute colitis and the resolution phase suggesting that metabolomics could be useful for determining disease progression [9*]. NMR has also been successfully used to monitor the effects of antibiotics on the urine and fecal metabolic profiles of mice [16].

Metabolomics and human IBD

The use of metabolomic analysis in human IBD arose from the need to improve diagnosis and differentiation of IBD subtypes and to classify indeterminate colitis. Simultaneously, the method offered a new approach to screen for small molecules that might play a role in disease mechanisms. A non-invasive approach of diagnosing IBD may also be preferable over endoscopy which is not without risk, although serious complications are reportedly low [17]. Non-invasive fecal and serologic markers with variable specificity and sensitivity for IBD are already in use. Among them, lactoferrin, calprotectin, PMN-elastase and CRP are used to differentiate active from inactive IBD as well as from irritable bowel syndrome (IBS) with quite good diagnostic accuracy (e.g. 83% for lactoferrin in UC and 81.4% for calprotectin in CD [18]). Other serologic markers such as anti-Saccharomyces cerevisiae (ASCA) and perinuclear antineutrophil cytoplasmic antibodies (pANCA) or the more experimental anti-CBir1 and anti-OmpC are less sensitive for IBD [19] although a combination of these markers has recently shown high predictive value in distinguishing IBD subtypes [20*].

To date, 1H NMR spectroscopy has been employed most widely to characterize activity and severity of human IBD. Studies have been performed on biofluids such as serum, plasma, urine and fecal extracts as well as on biopsies, isolated colonocytes and lymphocytes of IBD patients and were compared to matched healthy persons. These studies have primarily focused on small and non-complex molecules, such as amino acids and related metabolites, on TCA cycle intermediates, and on metabolites involved in fatty acid and purine metabolism. Differences in metabolic profiles were reported to exist between IBD patients and healthy controls [14**,21*,22,23*,24,25] as well as between the IBD subtypes [14**,21*,25]. Although multivariate statistical models are the best choice to discriminate patients from healthy subjects, Balasubramanian et al. [26] were able to detect significant shifts using univariate statistical methods in a variety of metabolites of the colonic mucosa in patients with CD and UC in comparison to healthy persons or IBD patients in remission.

With regard to confounding factors, medication does not seem to influence the metabolomic data very much [14**,24,25] although in one study, IBD patients that were on TNF-α antibodies had different urinary metabolic profiles than those without TNF-α antibody treatment [22]. Inclusion of IBD patients with bowel resections may also confound statistical outcome [22].

Urinary metabolomics

In human IBD, the type of biofluid investigated may have a significant impact on the metabolic profile. For instance, inter-subject variability is reported to be higher in urine than in serum [27]. Several studies have demonstrated that urinary metabolites are more influenced by environmental cues (diet, diurnal rhythms, age, gender and cultural influences) than serum or plasma metabolites [28,29,30]. Despite these variations, urinary metabolomic data may provide an important insight into changes within the bacterial population that colonize the gut, information that may be of high relevance. Metabolites of gut bacteria can usually be detected in urine [31]. In fact, a lot has already been learned about the role of the gut microbiome through various metabolomic studies [32**].

The gut microbiome is intricately involved in the pathogenesis of IBD [33] and dysbiosis may occur either as a cause or as consequence of the disease [34]. An altered population in the microbiome may thus lead to an altered urinary metabolic profile. Williams et al. [25] investigated metabolites in longitudinal samples of urine from CD and UC patients by 1H NMR spectroscopy using multivariate analysis and PLS-DA. Their model of evaluation distinguished well between IBD patients and healthy subjects and also between UC and CD patients. Significant decreases in hippurate, a metabolite that derives from microbiota, were found in IBD patients. Similarly, good separation of patients with IBD from healthy subjects by 1H NMR spectroscopy and multivariate analysis were reported by two other groups [14**,22].

They also noticed low hippurate levels in urine of IBD patients suggesting that hippurate may be a biomarker candidate. The findings are of high interest as hippurate levels were recently shown to correlate with the presence of Clostridia in the gut [35] and Clostridia bacteria have been found widely in CD patients [34]. Nevertheless, more data are necessary concerning the specificity of such markers as NMR can, for instance, reveal changes in hippurate, citrate, 2-oxoglutarate and creatinine already after slight changes in weight, suggesting that these compounds may represent general stress markers rather than specific biomakers [36]. Other studies [14**, 22,24] failed to distinguish between UC and CD through urinary metabolic profiling emphasizing the influence of environmental variation in urinary metabolomics and the multifactorial nature of IBD. In fact, IBD shows high variations in phenotype and severity and UC is easily confounded with colonic CD and indeterminate colitis [37].

Metabolomics of serum and plasma

Schicho et al. [14**] showed that quantitative metabolic profiling of serum and plasma by 1H NMR spectroscopy and multivariate analysis discriminated between UC and CD patients, albeit with a weaker predictability than between healthy and UC/CD patients. These observations were recently confirmed in serum in a comparable study [21*]. In a mouse model of colitis, serum metabolomics measured by the same method were clearly different between diseased and non-diseased animals [10]. Another group that analyzed plasma of patients with IBD with the aim to investigate differences in metabolites demonstrated that amino acid profiles evaluated by a novel statistical data modelling (aminogram) discriminated between IBD and healthy and between UC and CD [38**]. Statistical indices also correlated well with disease activity indicating that metabolic profiles of amino acids could be useful in monitoring disease activity during progression of IBD [38**]. A further report using GC/MS showed that IBD patients with either UC or CD could be readily distinguished from each other on the basis of amino acids and TCA cycle-related molecules [39]. Altogether, these studies demonstrate that metabolic serum and plasma profiles can separate between patients with IBD and healthy controls as well as between the IBD subtypes irrespective of the technique, favoring the use of serum and plasma for reliable measurement of metabolic profiles. However, analysis of serum may not provide us with information on important changes in the population of the gut microbiota.

Metabolomics of fecal extracts

The possible involvement of microbiota in the pathogenesis of IBD has also led to an interest in the use of metabolic profiling of fecal extracts for diagnosis and biomarker detection [40]. Work by Marchesi et al. [41] has already highlighted that NMR-based metabolic profiles in human fecal extracts of IBD patients show decreased levels of short chain fatty acids and that profiles are significantly distinct from healthy subjects. Recently, another study using 1H NMR spectroscopy of fecal samples showed clear separation of UC patients from control cohorts while the model was less successful in distinguishing between IBS patients and healthy controls [23*]. NMR spectra in this study correlated well between gut microbiota profiles as well as fecal metabolite composition indicating that metabolomics of fecal samples not only discriminates between diseased and healthy but reveals valuable insight into disturbances of gut bacteria [23*]. Fecal extracts may therefore provide important clues on how the microbiome contributes to the pathology of IBD. Jansson et al. [1] used ion cyclotron resonance-Fourier transform mass spectrometry (ICR-FT/MS) with an ultrahigh mass resolution in fecal extracts of CD patients. They were able to measure thousands of masses that differentiated between healthy and diseased subjects. Compounds of amino acid pathways and bile acid metabolism, saturated and unsaturated fatty acids and arachidonic acid were among the discriminating metabolites. This points again to the important role of inflammatory lipid mediators in IBD and to their use in metabolic profiles for classifying IBD patients and healthy individuals.

Metabolomics of colon tissue

Biopsies from colon tissue represent another source material to study metabolomics in IBD. Sharma et al. [42] assessed macroscopically involved and uninvolved mucosa of the colon from IBD patients by 1H NMR spectroscopy and detected similar metabolic profiles in these biopsies while another study reported differences between biopsies from IBD patients and healthy subjects with regard to amino acids, membrane components and lactate [26]. Using multivariate analysis, Bjerrum et al. [24] demonstrated that active UC can be separated from inactive UC by metabolic profiles of biopsies and isolated colonocytes. Interestingly, these workers showed that 20% of inactive UC patients had metabolic profiles similar to those with active UC. The authors suggested that the UC-like profiles in these patients could have indicated pathogenic changes that occurred prior to or immediately after flare-ups instead of representing subclinical inflammation [43].

Conclusion and perspectives

Metabolic profiling of biofluids from IBD patients represents a powerful approach for the detection of biomarkers and underlying mechanisms of IBD. In addition, the biofluids investigated can hold different information on IBD, i. e. while metabolic profiles of serum and plasma allow reproducible separation of patients with IBD from healthy controls and additionally allows separation of IBD subtypes, urinary metabolic profiles are harder to interpret because of a higher susceptibility of urine composition to environmental variation. However, since gut microbiota are regarded as possible important players in IBD pathogenesis, urinary and fecal metabolic profiles may provide important information on disease development, perpetuation and progression. A recent study shows that fecal transplants significantly improved recurrent C. difficile infection [44] suggesting such treatment might also be useful in alleviating or curing IBD, or maintaining remission. It would be very interesting to follow these patients in future studies following such treatment in order to determine whether their serum, urine and fecal extract profiles returned to normal and to explore the prognostic value of metabolomic analysis in this setting. Whatever technique and biofluid is going to be used for metabolic profiling of IBD patients in the future, metabolomics certainly will become an important adjunct in the clinical assessment and management of IBD.

Key points.

  • Metabolic profiling of biofluids combined with multivariate analysis is extremely useful for discriminating between IBD patients and healthy subjects

  • IBD subtypes can be discriminated by metabolic profiling of serum and plasma

  • Through metabolic profiling we are able to identify biomarkers of IBD as well as components of disease mechanisms

Acknowledgements

MS is supported by the Deutsche Forschungsgemeinschaft (DFG). HJV is a Scientist of the Alberta Heritage Foundation for Medical Research. RS is funded by the Austrian Science Fund (FWF P 22771) and the Austrian National Bank (14429).

Footnotes

Disclosures

The authors have no conflicts of interest to disclose.

References

*of special interest

**of outstanding interest

  • [1].Jansson J, Willing B, Lucio M, et al. Metabolomics reveals metabolic biomarkers of Crohn’s disease. PLoS One. 2009;4:e6386. doi: 10.1371/journal.pone.0006386. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • *[2].Zhang X, Choi FF, Zhou Y, et al. Metabolite profiling of plasma and urine from rats with TNBS-induced acute colitis using UPLC-ESI-QTOF-MS-based metabonomics--a pilot study. FEBS J. 2012;279:2322–2338. doi: 10.1111/j.1742-4658.2012.08612.x. [DOI] [PubMed] [Google Scholar]; The study shows that a sensitve ultra-high resolution method can be used to identify specific compounds of experimental colitis in plasma and urine.
  • [3].Eriksson L, Antti H, Gottfries J, et al. Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm) Anal Bioanal Chem. 2004;380:419–429. doi: 10.1007/s00216-004-2783-y. [DOI] [PubMed] [Google Scholar]
  • [4].Murdoch TB, Fu H, MacFarlane S, et al. Urinary metabolic profiles of inflammatory bowel disease in interleukin-10 gene-deficient mice. Anal Chem. 2008;80:5524–5531. doi: 10.1021/ac8005236. [DOI] [PubMed] [Google Scholar]
  • [5].Martin FP, Rezzi S, Philippe D, et al. Metabolic assessment of gradual development of moderate experimental colitis in IL-10 deficient mice. J Proteome Res. 2009;8:2376–2387. doi: 10.1021/pr801006e. [DOI] [PubMed] [Google Scholar]
  • [6].Lin HM, Edmunds SI, Helsby NA, et al. Nontargeted urinary metabolite profiling of a mouse model of Crohn’s disease. J Proteome Res. 2009;8:2045–2057. doi: 10.1021/pr800999t. [DOI] [PubMed] [Google Scholar]
  • [7].Otter D, Cao M, Lin HM, et al. Identification of urinary biomarkers of colon inflammation in IL10−/− mice using Short-Column LCMS metabolomics. J Biomed Biotechnol. 2011;2011:974701. doi: 10.1155/2011/974701. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • *[8].Baur P, Martin FP, Gruber L, et al. Metabolic phenotyping of the Crohn’s disease-like IBD etiopathology in the TNF(ΔARE/WT) mouse model. J Proteome Res. 2011;10:5523–5535. doi: 10.1021/pr2007973. [DOI] [PubMed] [Google Scholar]; In this study, 1H NMR spectroscopy and LC-MS were combined to track changes in metabolite composition during disease development highlighting many changes in lipid-dependent processes.
  • *[9].Shiomi Y, Nishiumi S, Ooi M, et al. GCMS-based metabolomic study in mice with colitis induced by dextran sulfate sodium. Inflamm Bowel Dis. 2011;17:2261–2274. doi: 10.1002/ibd.21616. [DOI] [PubMed] [Google Scholar]; Low molecular weight metabolites correlate with disease progression in experimental colitis.
  • [10].Schicho R, Nazyrova A, Shaykhutdinov R, et al. Quantitative metabolomic profiling of serum and urine in DSS-induced ulcerative colitis of mice by 1H NMR spectroscopy. J Proteome Res. 2010;9:6265–6273. doi: 10.1021/pr100547y. [DOI] [PubMed] [Google Scholar]
  • [11].Hong YS, Ahn YT, Park JC, et al. 1H NMR-based metabonomic assessment of probiotic effects in a colitis mouse model. Arch Pharm Res. 2010;33:1091–1101. doi: 10.1007/s12272-010-0716-1. [DOI] [PubMed] [Google Scholar]
  • *[12].Lin HM, Helsby NA, Rowan DD, Ferguson LR. Using metabolomic analysis to understand inflammatory bowel diseases. Inflamm Bowel Dis. 2011;17:1021–1029. doi: 10.1002/ibd.21426. [DOI] [PubMed] [Google Scholar]; Comprehensive review on metabonomics and IBD with an overview of differences in metabolites between diseased and healthy subjects.
  • [13].Weljie AM, Newton J, Mercier P, et al. Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal Chem. 2006;78:4430–4442. doi: 10.1021/ac060209g. [DOI] [PubMed] [Google Scholar]
  • **[14].Schicho R, Shaykhutdinov R, Ngo J, et al. Quantitative Metabolomic Profiling of Serum, Plasma, and Urine by 1H NMR Spectroscopy Discriminates between Patients with Inflammatory Bowel Disease and Healthy Individuals. J Proteome Res. 2012;11:3344–3357. doi: 10.1021/pr300139q. [DOI] [PMC free article] [PubMed] [Google Scholar]; This work directly compares human biofluids to investigate their usefullness in discriminating IBD patients from healthy controls and distinguish between CD and UC.
  • *[15].Lu K, Knutson CG, Wishnok JS, et al. Serum Metabolomics in a Helicobacter hepaticus Mouse Model of Inflammatory Bowel Disease Reveal Important Changes in the Microbiome, Serum Peptides, and Intermediary Metabolism. J Proteome Res. 2012;11:4916–4926. doi: 10.1021/pr300429x. [DOI] [PMC free article] [PubMed] [Google Scholar]; This study shows that experimental IBD leads to major perturbations in tryptophan metabolism and that the gut microflora may also be involved in blood metabolic profiles.
  • [16].Romick-Rosendale LE, Goodpaster AM, Hanwright PJ, et al. NMR-based metabonomics analysis of mouse urine and fecal extracts following oral treatment with the broad-spectrum antibiotic enrofloxacin (Baytril) Magn Reson Chem. 2009;47(Suppl 1):S36–46. doi: 10.1002/mrc.2511. [DOI] [PubMed] [Google Scholar]
  • [17].Crispin A, Birkner B, Munte A, et al. Process quality and incidence of acute complications in a series of more than 230,000 outpatient colonoscopies. Endoscopy. 2009;41:1018–1025. doi: 10.1055/s-0029-1215214. [DOI] [PubMed] [Google Scholar]
  • [18].Langhorst J, Elsenbruch S, Koelzer J, et al. Noninvasive markers in the assessment of intestinal inflammation in inflammatory bowel diseases: performance of fecal lactoferrin, calprotectin, and PMN-elastase, CRP, and clinical indices. Am J Gastroenterol. 2008;103:162–169. doi: 10.1111/j.1572-0241.2007.01556.x. [DOI] [PubMed] [Google Scholar]
  • [19].Reese GE, Constantinides VA, Simillis C, et al. Diagnostic precision of anti-Saccharomyces cerevisiae antibodies and perinuclear antineutrophil cytoplasmic antibodies in inflammatory bowel disease. Am J Gastroenterol. 2006;101:2410–2422. doi: 10.1111/j.1572-0241.2006.00840.x. [DOI] [PubMed] [Google Scholar]
  • *[20].Van Schaik FD, Oldenburg B, Hart AR, et al. Serological markers predict inflammatory bowel disease years before the diagnosis. Gut. 2012 doi: 10.1136/gutjnl-2012-302717. DOI:10.1136/gutjnl-2012-302717. [DOI] [PubMed] [Google Scholar]; This clinical study demonstrates that a combination of various serological markers of IBD may have a high value in predicting IBD.
  • *[21].Williams HR, Willsmore JD, Cox IJ, et al. Serum metabolic profiling in inflammatory bowel disease. Dig Dis Sci. 2012;57:2157–2165. doi: 10.1007/s10620-012-2127-2. [DOI] [PubMed] [Google Scholar]; The study highlights that serum is a reliable biofluid to distinguish between IBD subtypes at the metabolomic level.
  • [22].Stephens NS, Siffledeen J, Su X, et al. Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy. J Crohns Colitis. 2012 doi: 10.1016/j.crohns.2012.04.019. doi:10.1016/j.crohns.2012.04.019. [DOI] [PubMed] [Google Scholar]
  • *[23].Le Gall G, Noor SO, Ridgway K, et al. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J Proteome Res. 2011;10:4208–4218. doi: 10.1021/pr2003598. [DOI] [PubMed] [Google Scholar]; This study focuses on the metabolic changes that occur in the gut microbiome during IBD
  • [24].Bjerrum JT, Nielsen OH, Hao F, et al. Metabonomics in ulcerative colitis: diagnostics, biomarker identification, and insight into the pathophysiology. J Proteome Res. 2010;9:954–962. doi: 10.1021/pr9008223. [DOI] [PubMed] [Google Scholar]
  • [25].Williams HR, Cox IJ, Walker DG, et al. Characterization of inflammatory bowel disease with urinary metabolic profiling. Am J Gastroenterol. 2009;104:1435–1444. doi: 10.1038/ajg.2009.175. [DOI] [PubMed] [Google Scholar]
  • [26].Balasubramanian K, Kumar S, Singh RR, et al. Metabolism of the colonic mucosa in patients with inflammatory bowel diseases: an in vitro proton magnetic resonance spectroscopy study. Magn Reson Imaging. 2009;27:79–86. doi: 10.1016/j.mri.2008.05.014. [DOI] [PubMed] [Google Scholar]
  • [27].Lenz EM, Bright J, Wilson ID, et al. A 1H NMR-based metabonomic study of urine and plasma samples obtained from healthy human subjects. J Pharm Biomed Anal. 2003;33:1103–1115. doi: 10.1016/s0731-7085(03)00410-2. [DOI] [PubMed] [Google Scholar]
  • [28].Lenz EM, Bright J, Wilson ID, et al. Metabonomics, dietary influences and cultural differences: a 1H NMR-based study of urine samples obtained from healthy British and Swedish subjects. J Pharm Biomed Anal. 2004;36:841–849. doi: 10.1016/j.jpba.2004.08.002. [DOI] [PubMed] [Google Scholar]
  • [29].Stella C, Beckwith-Hall B, Cloarec O, et al. Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res. 2006;5:2780–2788. doi: 10.1021/pr060265y. [DOI] [PubMed] [Google Scholar]
  • [30].Slupsky CM, Rankin KN, Wagner J, et al. Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Anal Chem. 2007;79:6995–7004. doi: 10.1021/ac0708588. [DOI] [PubMed] [Google Scholar]
  • [31].Nicholls AW, Smith RJ, Nicholson JK. NMR spectroscopic-based metabonomic studies of urinary metabolite variation in acclimatizing germ-free rats. Chem Res Toxicol. 2003;16:1395–1404. doi: 10.1021/tx0340293. [DOI] [PubMed] [Google Scholar]
  • **[32].Nicholson JK, Holmes E, Kinross J, et al. Host-gut microbiota metabolic interactions. Science. 2012;336:1262–1267. doi: 10.1126/science.1223813. [DOI] [PubMed] [Google Scholar]; This review discusses the importance of the interplay between the gut microbiome and the host and how interactive host-microbiota metabolic signaling may affect health.
  • [33].Takaishi H, Matsuki T, Nakazawa A, et al. Imbalance in intestinal microflora constitution could be involved in the pathogenesis of inflammatory bowel disease. Int J Med Microbiol. 2008;298:463–472. doi: 10.1016/j.ijmm.2007.07.016. [DOI] [PubMed] [Google Scholar]
  • [34].Nagalingam NA, Lynch SV. Role of the microbiota in inflammatory bowel diseases. Inflamm Bowel Dis. 2012;18:968–984. doi: 10.1002/ibd.21866. [DOI] [PubMed] [Google Scholar]
  • [35].Li M, Wang B, Zhang M, et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc Natl Acad Sci U S A. 2008;105:2117–2122. doi: 10.1073/pnas.0712038105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Connor SC, Wu W, Sweatman BC, et al. Effects of feeding and body weight loss on the 1H-NMR-based urine metabolic profiles of male Wistar Han rats: implications for biomarker discovery. Biomarkers. 2004;9:156–179. doi: 10.1080/13547500410001720767. [DOI] [PubMed] [Google Scholar]
  • [37].Strober W, Fuss I, Mannon P. The fundamental basis of inflammatory bowel disease. J Clin Invest. 2007;117:514–521. doi: 10.1172/JCI30587. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • **[38].Hisamatsu T, Okamoto S, Hashimoto M, et al. Novel, objective, multivariate biomarkers composed of plasma amino acid profiles for the diagnosis and assessment of inflammatory bowel disease. PLoS One. 2012;7:e31131. doi: 10.1371/journal.pone.0031131. [DOI] [PMC free article] [PubMed] [Google Scholar]; Elegant work showing that plasma aminograms combined with multivariate indices discriminates between CD/UC patients and healthy controls as well as between patients with active disease and those in remission.
  • [39].Ooi M, Nishiumi S, Yoshie T, et al. GC/MS-based profiling of amino acids and TCA cycle-related molecules in ulcerative colitis. Inflamm Res. 2011;60:831–840. doi: 10.1007/s00011-011-0340-7. [DOI] [PubMed] [Google Scholar]
  • [40].Noor SO, Ridgway K, Scovell L, et al. Ulcerative colitis and irritable bowel patients exhibit distinct abnormalities of the gut microbiota. BMC Gastroenterol. 2010;10:134. doi: 10.1186/1471-230X-10-134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [41].Marchesi JR, Holmes E, Khan F, et al. Rapid and noninvasive metabonomic characterization of inflammatory bowel disease. J Proteome Res. 2007;6:546–551. doi: 10.1021/pr060470d. [DOI] [PubMed] [Google Scholar]
  • [42].Sharma U, Singh RR, Ahuja V, et al. Similarity in the metabolic profile in macroscopically involved and un-involved colonic mucosa in patients with inflammatory bowel disease: an in vitro proton (1H) MR spectroscopy study. Magn Reson Imaging. 2010;28:1022–1029. doi: 10.1016/j.mri.2010.03.039. [DOI] [PubMed] [Google Scholar]
  • [43].Olsen J, Gerds TA, Seidelin JB, et al. Diagnosis of ulcerative colitis before onset of inflammation by multivariate modeling of genome-wide gene expression data. Inflamm Bowel Dis. 2009;15:1032–1038. doi: 10.1002/ibd.20879. [DOI] [PubMed] [Google Scholar]
  • [44].Van Nood E, Vrieze A, Nieuwdorp M, et al. Duodenal Infusion of Donor Feces for Recurrent Clostridium difficile. N Engl J Med. 2013 doi: 10.1056/NEJMoa1205037. DOI: 10.1056/NEJMoa1205037. [DOI] [PubMed] [Google Scholar]

RESOURCES