NMR-based metabolomics in human disease diagnosis: applications, limitations, and recommendations

Metabolomics is a dynamic and emerging research field, similar to proteomics, transcriptomics and genomics in affording global understanding of biological systems. It is particularly useful in functional genomic studies in which metabolism is thought to be perturbed. Metabolomics provides a snapshot of the metabolic dynamics that reflect the response of living systems to both pathophysiological stimuli and/or genetic modification. Because this approach makes possible the examination of interactions between an organism and its diet or environment, it is particularly useful for identifying biomarkers of disease processes that involve the environment. For example, the interaction of a high fat diet with cardiovascular disease can be studied via such a metabolomics approach by modeling the interaction between genes and diet. The high reproducibility of NMR-based techniques gives this method a number of advantages over other analytical techniques in large-scale and long-term metabolomic studies, such as epidemiological studies. This approach has been used to study a wide range of diseases, through the examination of biofluids, including blood plasma/serum, urine, blister fluid, saliva and semen, as well as tissue extracts and intact tissue biopsies. However, complicating the use of NMR spectroscopy in biomarker discovery is the fact that numerous variables can effect metabolic composition including, fasting, stress, drug administration, diet, gender, age, physical activity, life style and the subject's health condition. To minimize the influence of these variations in the datasets, all experimental conditions including sample collection, storage, preparation as well as NMR spectroscopic parameters and data analysis should be optimized carefully and conducted in an identical manner as described by the local standard operating protocol . This review highlights the potential applications of NMR-based metabolomics studies and gives some recommendations to improve sample collection, sample preparation and data analysis in using this approach.