Metabolomics may have the capacity to revolutionize disease diagnosis through the identification of scores of metabolites that vary during environmental, pathogenic, or toxicological insult. NMR spectroscopy has become one of the main tools for measuring these changes since an NMR spectrum can accurately identify metabolites and their concentrations. The predominant approach in analyzing NMR data has been through the technique of spectral binning. However, identification of spectral areas in an NMR spectrum is insufficient for diagnostic evaluation, since it is unknown whether areas of interest are strictly caused by metabolic changes or are simply artifacts. In this paper, we explore differences in gender, diurnal variation, and age in a human population. We use the example of gender differences to compare traditional spectral binning techniques (NMR spectral areas) to novel targeted profiling techniques (metabolites and their concentrations). We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences in a healthy population. Metabolites relating to mitochondrial energy metabolism were found to differentiate gender and age. Dietary components and some metabolites related to circadian rhythms were found to differentiate time of day urine collection. The mechanisms by which these differences arise will be key to the discovery of new diagnostic tests and new understandings of the mechanism of disease.

Urine metabolomics has recently emerged as a prominent field for the discovery of non-invasive biomarkers that can detect subtle metabolic discrepancies in response to a specific disease or therapeutic intervention. Urine, compared to other biofluids, is characterized by its ease of collection, richness in metabolites and its ability to reflect imbalances of all biochemical pathways within the body. Following urine collection for metabolomic analysis, samples must be immediately frozen to quench any biogenic and/or non-biogenic chemical reactions. According to the aim of the experiment; sample preparation can vary from simple procedures such as filtration to more specific extraction protocols such as liquid-liquid extraction. Due to the lack of comprehensive studies on urine metabolome stability, higher storage temperatures (i.e. 4 °C) and repetitive freeze-thaw cycles should be avoided. To date, among all analytical techniques, mass spectrometry (MS) provides the best sensitivity, selectivity and identification capabilities to analyze the majority of the metabolite composition in the urine. Combined with the qualitative and quantitative capabilities of MS, and due to the continuous improvements in its This is the peer reviewed version of: Khamis, M. M., Adamko, D. J. and El-Aneed, A. (2015), Mass Spec Rev., published in final form at https://doi.org/10.1002/mas.21455. [HILIC]), liquid chromatography (LC)-MS is unequivocally the most utilized and the most informative analytical tool employed in urine metabolomics. Furthermore, differential isotope tagging techniques has provided a solution to ion suppression from urine matrix thus allowing for quantitative analysis. In addition to LC-MS, other MS-based technologies have been utilized in urine metabolomics. These include direct injection (infusion)-MS, capillary electrophoresis-MS and gas chromatography-MS. In this article, the current progresses of different MS-based techniques in exploring the urine metabolome as well as the recent findings in providing potentially diagnostic urinary biomarkers are discussed.

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Urine is often sampled from patients participating in clinical and metabolomic studies. Biological homeostasis occurs in humans, but little is known about the variability of metabolites found in urine. It is important to define the inter-and intra-individual metabolite variance within a normal population before scientific or clinical conclusions are made regarding different pathophysiologies. This study investigates the variability of selected urine metabolites in a group of 60 healthy men and women over a period of 30 days. To monitor individual variation, 6 women from the normal population were randomly selected and followed for 30 days. To determine the influence of extraneous environmental factors urine was collected from 25 guinea pigs with similar genetics, diet, and living environment. For both studies, 24 metabolites were identified and quantified using high-resolution 1 H nuclear magnetic resonance spectroscopy (NMR). The data demonstrated large inter and intra-individual variation in metabolite concentrations in both normal human and control animal populations. A defined normal baseline is essential before any conclusions may be drawn regarding changes in urine metabolite concentrations.