Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

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 (1)H 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.

Differences between individual phenotypes are due both to differences in genotype and to exposure to different environmental factors. A fundamental contribution to the definition of the individual phenotype for clinical and therapeutic applications would come from a deeper understanding of the metabolic phenotype. The existence of unique individual metabolic phenotypes has been hypothesized, but the experimental evidence has been only recently collected. Analysis of individual phenotypes over the timescale of years shows that the metabolic phenotypes are largely invariant. The present work also supports the idea that the individual metabolic phenotype can also be considered a metagenomic entity that is strongly affected by both gut microbiome and host metabolic phenotype, the latter defined by both genetic and environmental contributions.

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.