The emerging field of “metabolomics,” in which a large number of small molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information rich analytical techniques of nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research, and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism, and cardiovascular diseases.

We report on the development of a monitoring test for recurrent breast cancer, using metaboliteprofiling methods. Using a combination of nuclear magnetic resonance (NMR) and two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) methods, we analyzed the metabolite profiles of 257 retrospective serial serum samples from 56 previously diagnosed and surgically treated breast cancer patients. One hundred sixteen of the serial samples were from 20 patients with recurrent breast cancer, and 141 samples were from 36 patients with no clinical evidence of the disease during ∼6 years of sample collection. NMR and GC×GC-MS data were analyzed by multivariate statistical methods to compare identified metabolite signals between the recurrence samples and those with no evidence of disease. Eleven metabolite markers (seven from NMR and four from GC×GC-MS) were shortlisted from an analysis of all patient samples by using logistic regression and 5-fold cross-validation. A partial least squares discriminant analysis model built using these markers with leave-one-out cross-validation provided a sensitivity of 86% and a specificity of 84% (area under the receiver operating characteristic curve = 0.88). Strikingly, 55% of the patients could be correctly predicted to have recurrence 13 months (on average) before the recurrence was clinically diagnosed, representing a large improvement over the current breast cancer-monitoring assay CA 27.29. To the best of our knowledge, this is the first study to develop and prevalidate a prediction model for early detection of recurrent breast cancer based on metabolic profiles. In particular, the combination of two advanced analytical methods, NMR and MS, provides a powerful approach for the early detection of recurrent breast cancer. Cancer Res; 70(21); 8309-18. ©2010 AACR.

Urine metabolic profiles of patients with inborn errors of metabolism were examined with nuclear magnetic resonance (NMR) and desorption electrospray ionization mass spectrometry (DESI-MS) methods. Spectra obtained from the study of urine samples from individual patients with argininosuccinic aciduria (ASA), classic homocystinuria (HCY), classic methylmalonic acidemia (MMA), maple syrup urine disease (MSUD), phenylketonuria (PKU) and type II tyrosinemia (TYRO) were compared with six control patient urine samples using principal component analysis (PCA). Target molecule spectra were identified from the loading plots of PCA output and compared with known metabolic profiles from the literature and metabolite databases. Results obtained from the two techniques were then correlated to obtain a common list of molecules associated with the different diseases and metabolic pathways. The combined approach discussed here may prove useful in the rapid screening of biological fluids from sick patients and may help to improve the understanding of these rare diseases.

Type 1 diabetes was induced in Sprague–Dawley rats using streptozotocin. Rat urine samples (8 diabetic and 10 control) were analyzed by 1H nuclear magnetic resonance (NMR) spectroscopy. The derived metabolites using univariate and multivariate statistical analysis were subjected to correlative analysis. Plasma metabolites were measured by a series of bioassays. A total of 17 urinary metabolites were identified in the 1H NMR spectra and the loadings plots after principal components analysis. Diabetic rats showed significantly increased levels of glucose (P < 0.00001), alanine (P < 0.0002), lactate (P < 0.05), ethanol (P < 0.05), acetate (P < 0.05), and fumarate (P < 0.05) compared with controls. Plasma assays showed higher amounts of glucose, urea, triglycerides, and thiobarbituric acid-reacting substances in diabetic rats. Striking differences in the Pearson's correlation of the 17 NMR-detected metabolites were observed between control and diabetic rats. Detailed analysis of the altered metabolite levels and their correlations indicate a significant disturbance in the glucose metabolism and tricarboxylic acid (TCA) cycle and a contribution from gut microbial metabolism. Specific perturbed metabolic pathways include the glucose–alanine and Cori cycles, the acetate switch, and choline metabolism. Detection of the altered metabolic pathways and bacterial metabolites using this correlative and quantitative NMR-based metabolomics approach should help to further the understanding of diabetes-related mechanisms.

Metabolic profiling has received increasing recognition as an indispensable complement to genomics and proteomics for probing biological systems and for clinical applications. 1 H nuclear magnetic resonance (NMR) is widely used in the field but is challenged by spectral complexity and overlap. Improved and simple methods that quantitatively profile a large number of metabolites are sought to make further progress. Here, we demonstrate a simple isotope tagging strategy, in which metabolites with carboxyl groups are chemically tagged with 15 N-ethanolamine and detected using a 2D heteronuclear correlation NMR experiment. This method is capable of detecting over one hundred metabolites at concentrations as low as a few micromolar in biological samples, both quantitatively and reproducibly. Carboxyl-containing compounds are found in almost all metabolic pathways, and thus this new approach should find a variety of applications. KeywordsMetabolic Profiling; Metabolomics; Metabonomics; NMR; Heteronuclear correlation; 15 N Isotope Tagging; Carboxylic AcidThe improved characterization of the metabolome promises immense benefits for better understanding cellular activities, linking genotypes and phenotypes, and providing new understanding of complex biological states, including those of health and disease [1][2][3][4][5][6][7][8][9][10][11] . The significant interest in developing improved methods for metabolic profiling (including metabolomics and metabonomics) stems from the high sensitivity of metabolite profiles to even subtle stimuli, which is potentially important for detecting the earliest onset of various adverse biological perturbations. However, while most eukaryotic organisms are now thought to possess 3-5,000 metabolites, and possibly many more 5 , current high throughput analytical technologies are only capable of detecting a small fraction of these metabolites owing to severe limitations of sensitivity, resolution and/or reproducibility. These drawbacks critically limit metabolic profiling applications since the information extracted from a small metabolite subset may be too non-specific to draw a meaningful conclusion.* To whom correspondence should be addressed. raftery@purdue.edu, Tel: (765) Fax: (765) The information-rich analytical techniques of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the primary methods used in metabolite studies, and efforts are currently underway to improve their ability to access and measure a greater number of metabolites [12][13][14] . MS is extremely attractive in metabolite studies due to its exquisite sensitivity, experimental flexibility and ability to determine unknown molecules. While MS is more sensitive compared to NMR, the data from NMR are often more quantitative and reproducible. In particular, the same nuclei detected in an NMR experiment have the same sensitivity, independent of the properties of metabolite molecules. Therefore, the absolute quantities of different metabolites can be measured with a single internal stan...