Metabolomics Abstracts 1


Session 2: Personalised nutrition. Metabolomic applications in nutritional research
            (Brennan, 2008) Download
Metabolomics aims to profile all small molecules that are present in biological samples such as biofluids, tissue extracts and culture media. Combining the data obtained with multivariate data analysis tools allows the exploration of changes induced by a biological treatment or changes resulting from phenotype. Recently, there has been a large increase in interest in using metabolomics in nutritional research and because of the intimate relationship between nutrients and metabolism there exists great potential for the use of metabolomics within nutritional research. However, for metabolomics to reach its full potential within this field it is also important to be realistic about the challenges that are faced. Examples of such challenges include the necessity to have a clear understanding of the causes of variation in human metabolomic profiles, the effects of the gut microflora on the metabolomic profile and the interaction of the gut microflora with the host's metabolism. A further challenge that is particularly relevant for human nutritional research is the difficulty associated with biological interpretation of the data. Notwithstanding these and other challenges, several examples of successful applications to nutritional research exist. The link between the human metabolic phenotype, as characterised by metabolomic profiles, and dietary preferences proposes the potential role of metabolomics in personalised nutrition.

Metabolomics in human nutrition: opportunities and challenges
            (Gibney et al., 2005) Download
Metabolomics has been widely adopted in pharmacology and toxicology but is relatively new in human nutrition. The ultimate goal, to understand the effects of exogenous compounds on human metabolic regulation, is similar in all 3 fields. However, the application of metabolomics to nutritional research will be met with unique challenges. Little is known of the extent to which changes in the nutrient content of the human diet elicit changes in metabolic profiles. Moreover, the metabolomic signal from nutrients absorbed from the diet must compete with the myriad of nonnutrient signals that are absorbed, metabolized, and secreted in both urine and saliva. The large-bowel microflora also produces significant metabolic signals that can contribute to and alter the metabolome of biofluids in human nutrition. Notwithstanding these possible confounding effects, every reason exists to be optimistic about the potential of metabolomics for the assessment of various biofluids in nutrition research. This potential lies both in metabolic profiling through the use of pattern-recognition statistics on assigned and unassigned metabolite signals and in the collection of comprehensive data sets of identified metabolites; both objectives have the potential to distinguish between different dietary treatments, which would not have been targeted with conventional techniques. The latter objective sets out a well-recognized challenge to modern biology: the development of libraries of small molecules to aid in metabolite identification. The purpose of the present review was to highlight some early challenges that need to be addressed if metabolomics is to realize its great potential in human nutrition.

Prediction of gastric cancer metastasis through urinary metabolomic investigation using GC/MS
            (Hu et al., 2011) Download
AIM: To gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic values to predict tumor metastasis. METHODS: Human gastric cancer SGC-7901 cells were implanted into 24 severe combined immune deficiency (SCID) mice, which were randomly divided into metastasis group (n = 8), non-metastasis group (n = 8), and normal group (n = 8). Urinary metabolomic information was obtained by gas chromatography/mass spectrometry (GC/MS). RESULTS: There were significant metabolic differences among the three groups (t test, P < 0.05). Ten selected metabolites were different between normal and cancer groups (non-metastasis and metastasis groups), and seven metabolites were also different between non-metastasis and metastasis groups. Two diagnostic models for gastric cancer and metastasis were constructed respectively by the principal component analysis (PCA). These PCA models were confirmed by corresponding receiver operating characteristic analysis (area under the curve = 1.00). CONCLUSION: The urinary metabolomic profile is different, and the selected metabolites might be instructive to clinical diagnosis or screening metastasis for gastric cancer.

Multivariate classification of urine metabolome profiles for breast cancer diagnosis
            (Kim et al., 2011) Download
BACKGROUND: Diagnosis techniques using urine are non-invasive, inexpensive, and easy to perform in clinical settings. The metabolites in urine, as the end products of cellular processes, are closely linked to phenotypes. Therefore, urine metabolome is very useful in marker discoveries and clinical applications. However, only univariate methods have been used in classification studies using urine metabolome. Since multiple genes or proteins would be involved in developments of complex diseases such as breast cancer, multiple compounds including metabolites would be related with the complex diseases, and multivariate methods would be needed to identify those multiple metabolite markers. Moreover, because combinatorial effects among the markers can seriously affect disease developments and there also exist individual differences in genetic makeup or heterogeneity in cancer progressions, single marker is not enough to identify cancers. RESULTS: We proposed classification models using multivariate classification techniques and developed an analysis procedure for classification studies using metabolome data. Through this strategy, we identified five potential urinary biomarkers for breast cancer with high accuracy, among which the four biomarker candidates were not identifiable by only univariate methods. We also proposed potential diagnosis rules to help in clinical decision making. Besides, we showed that combinatorial effects among multiple biomarkers can enhance discriminative power for breast cancer. CONCLUSIONS: In this study, we successfully showed that multivariate classifications are needed to precisely diagnose breast cancer. After further validation with independent cohorts and experimental confirmation, these marker candidates will likely lead to clinically applicable assays for earlier diagnoses of breast cancer.

A comprehensive urinary metabolomic approach for identifying kidney cancer
            (Kind et al., 2007) Download
The diagnosis of cancer by examination of the urine has the potential to improve patient outcomes by means of earlier detection. Due to the fact that the urine contains metabolic signatures of many biochemical pathways, this biofluid is ideally suited for metabolomic analysis, especially involving diseases of the kidney and urinary system. In this pilot study, we test three independent analytical techniques for suitability for detection of renal cell carcinoma (RCC) in urine of affected patients. Hydrophilic interaction chromatography (HILIC-LC-MS), reversed-phase ultra performance liquid chromatography (RP-UPLC-MS), and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) all were used as complementary separation techniques. The combination of these techniques is best suited to cover a very large part of the urine metabolome by enabling the detection of both lipophilic and hydrophilic metabolites present therein. In this study, it is demonstrated that sample pretreatment with urease dramatically alters the metabolome composition apart from removal of urea. Two new freely available peak alignment methods, MZmine and XCMS, are used for peak detection and retention time alignment. The results are analyzed by a feature selection algorithm with subsequent univariate analysis of variance (ANOVA) and a multivariate partial least squares (PLS) approach. From more than 2000 mass spectral features detected in the urine, we identify several significant components that lead to discrimination between RCC patients and controls despite the relatively small sample size. A feature selection process condensed the significant features to less than 30 components in each of the data sets. In future work, these potential biomarkers will be further validated with a larger patient cohort. Such investigation will likely lead to clinically applicable assays for earlier diagnosis of RCC, as well as other malignancies, and thereby improved patient prognosis.

Symposium 2: Modern approaches to nutritional research challenges: Targeted and non-targeted approaches for metabolite profiling in nutritional research
            (Lodge, 2010) Download
The present report discusses targeted and non-targeted approaches to monitor single nutrients and global metabolite profiles in nutritional research. Non-targeted approaches such as metabolomics allow for the global description of metabolites in a biological sample and combine an analytical platform with multivariate data analysis to visualise patterns between sample groups. In nutritional research metabolomics has generated much interest as it has the potential to identify changes to metabolic pathways induced by diet or single nutrients, to explore relationships between diet and disease and to discover biomarkers of diet and disease. Although still in its infancy, a number of studies applying this technology have been performed; for example, the first study in 2003 investigated isoflavone metabolism in females, while the most recent study has demonstrated changes to various metabolic pathways during a glucose tolerance test. As a relatively new technology metabolomics is faced with a number of limitations and challenges including the standardisation of study design and methodology and the need for careful consideration of data analysis, interpretation and identification. Targeted approaches are used to monitor single or multiple nutrient and/or metabolite status to obtain information on concentration, absorption, distribution, metabolism and elimination. Such applications are currently widespread in nutritional research and one example, using stable isotopes to monitor nutrient status, is discussed in more detail. These applications represent innovative approaches in nutritional research to investigate the role of both single nutrients and diet in health and disease.

Metabolomics and human nutrition
            (Primrose et al., 2011) Download
The present report summarises a workshop convened by the UK Food Standards Agency (Agency) on 25 March 2010 to discuss the current Agency's funded research on the use of metabolomics technologies in human nutrition research. The objectives of this workshop were to review progress to date, to identify technical challenges and ways of overcoming them, and to discuss future research priorities and the application of metabolomics in public health nutrition research and surveys. Results from studies nearing completion showed that by using carefully designed dietary and sampling regimens, it is possible to identify novel biomarkers of food intake that could not have been predicted from current knowledge of food composition. These findings provide proof-of-principle that the metabolomics approach can be used to develop new putative biomarkers of dietary intake. The next steps will be to validate these putative biomarkers, to develop rapid and inexpensive assays for biomarkers of food intake of high public health relevance, and to test their utility in population cohort studies and dietary surveys.

Standardization of factors that influence human urine metabolomics
            Rasmussen 2011 Download

Assessment of dietary exposure related to dietary GI and fibre intake in a nutritional metabolomic study of human urine
            (Rasmussen et al., 2011) Download
There is a need for a tool to assess dietary intake related to the habitual dietary glycaemic index (GI) and fibre in groups with large numbers of individuals. Novel metabolite-profiling techniques may be a useful approach when applied to human urine. In a long-term, controlled dietary intervention study, metabolomics were applied to assess dietary patterns. A targeted approach was used to evaluate the effects on urinary C-peptide excretion caused by the dietary treatments. Seventy-seven overweight subjects followed an 8-week low-calorie diet (LCD) and were then randomly assigned to a high-GI or low-GI diet for 6 month during which they completed 24-h urine collections at baseline (prior to the 8-week LCD) and after randomisation to the dietary intervention, at month 1, 3 and 6, respectively. Metabolite profiling in 24-h urine was performed by (1)H NMR and chemometrics. Partial least squares (PLS) analysis indicated that urinary formate could discriminate between high-GI and low-GI diets (correlation coefficient r = 0.82), and this finding was confirmed statistically (P = 0.01). PLS analysis also indicated that urinary hippurate could be associated with fibre intake, but this finding was not confirmed statistically. No associations between GI and urinary C-peptide were found. Our results emphasise that application of metabolomics is useful in the assessment of dietary exposure related to dietary GI and fibre seen at group level in a nutritional metabolomic study of human urine. As our design allowed for large variations in individually selected food items, biomarkers identified at group level may be interpreted as more general and robust markers, largely not confounded with markers from single dietary factors.

Clinical applications of metabolomics in oncology: a review
            (Spratlin et al., 2009) Download
Metabolomics, an omic science in systems biology, is the global quantitative assessment of endogenous metabolites within a biological system. Either individually or grouped as a metabolomic profile, detection of metabolites is carried out in cells, tissues, or biofluids by either nuclear magnetic resonance spectroscopy or mass spectrometry. There is potential for the metabolome to have a multitude of uses in oncology, including the early detection and diagnosis of cancer and as both a predictive and pharmacodynamic marker of drug effect. Despite this, there is lack of knowledge in the oncology community regarding metabolomics and confusion about its methodologic processes, technical challenges, and clinical applications. Metabolomics, when used as a translational research tool, can provide a link between the laboratory and clinic, particularly because metabolic and molecular imaging technologies, such as positron emission tomography and magnetic resonance spectroscopic imaging, enable the discrimination of metabolic markers noninvasively in vivo. Here, we review the current and potential applications of metabolomics, focusing on its use as a biomarker for cancer diagnosis, prognosis, and therapeutic evaluation.

Effects of menstrual cycle phase on metabolomic profiles in premenopausal women
            (Wallace et al., 2010) Download
BACKGROUND: Characterization of the normal degree of physiological variation in the metabolomic profiles of healthy humans is a necessary step in the development of metabolomics as both a clinical research and diagnostic tool. This study investigated the effects of the menstrual cycle on (1)H nuclear magnetic resonance (NMR) derived metabolomic profiles of urine and plasma from healthy women. METHODS: In this study, 34 healthy women were recruited and a first void urine and fasting blood sample were collected from each woman at four different time points during one menstrual cycle. Serum hormone levels were used in combination with the menstrual calendar to classify the urine and plasma samples into five different phases i.e. menstrual, follicular, periovulatory, luteal and premenstrual. The urine and plasma samples were analysed using (1)H NMR spectroscopy and subsequent data were analysed using principal component analysis (PCA) and partial least squares discriminant analysis. RESULTS: PCA of the urine spectra showed no separation of samples based on the phases of the menstrual cycle. Multivariate analysis of the plasma spectra showed a separation of the menstrual phase and the luteal phase samples (R(2) = 0.61, Q(2) = 0.41). Subsequent analysis revealed a significant decrease in levels of glutamine, glycine, alanine, lysine, serine and creatinine and a significant increase in levels of acetoacetate and very low density lipoprotein (VLDL CH(2)) during the luteal phase. CONCLUSIONS: These results establish a need to control for metabolic changes that occur in plasma due to the menstrual cycle in the design of future metabolomic studies involving premenopausal women.

Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans
            (Walsh et al., 2006) Download
BACKGROUND: Metabolomics in human nutrition research is faced with the challenge that changes in metabolic profiles resulting from diet may be difficult to differentiate from normal physiologic variation. OBJECTIVE: We assessed the extent of intra- and interindividual variation in normal human metabolic profiles and investigated the effect of standardizing diet on reducing variation. DESIGN: Urine, plasma, and saliva were collected from 30 healthy volunteers (23 females, 7 males) on 4 separate mornings. For visits 1 and 2, free food choice was permitted on the day before biofluid collection. Food choice on the day before visit 3 was intended to mimic that for visit 2, and all foods were standardized on the day before visit 4. Samples were analyzed by using 1H nuclear magnetic resonance spectroscopy followed by multivariate data analysis. RESULTS: Intra- and interindividual variations were considerable for each biofluid. Visual inspection of the principal components analysis scores plots indicated a reduction in interindividual variation in urine, but not in plasma or saliva, after the standard diet. Partial least-squares discriminant analysis indicated time-dependent changes in urinary and salivary samples, mainly resulting from creatinine in urine and acetate in saliva. The predictive power of each model to classify the samples as either night or morning was 85% for urine and 75% for saliva. CONCLUSIONS: Urine represented a sensitive metabolic profile that reflected acute dietary intake, whereas plasma and saliva did not. Future metabolomics studies should consider recent dietary intake and time of sample collection as a means of reducing normal physiologic variation.

Influence of acute phytochemical intake on human urinary metabolomic profiles
            (Walsh et al., 2007) Download
BACKGROUND: Diversity in dietary intake contributes to variation in human metabolomic profiles and artifacts from acute dietary intake can affect metabolomics data. OBJECTIVE: We investigated the role of dietary phytochemicals on shaping human urinary metabolomic profiles. DESIGN: First void urine samples were collected from 21 healthy volunteers (12 women, 9 men) following their normal diet (ND), a 2-d low-phytochemical diet (LPD), or a 2-d standard phytochemical diet (SPD). Nutrient intake was assessed during the study. Urine samples were analyzed by using (1)H nuclear magnetic resonance spectroscopy ((1)H NMR) and mass spectrometry (MS), which was followed by multivariate data analysis. RESULTS: Macronutrient intake did not change throughout the study. Partial least-squares-discriminant analysis indicated a clear distinction between the LPD samples and the ND and SPD samples, relating to creatinine and methylhistidine excretion after the LPD and hippurate excretion after the ND and SPD. The predictive power of the LPD versus the ND model was 74 +/- 3% and 82 +/- 6% with the (1)H NMR and MS data sets, respectively. The predictive power of the LPD versus the SPD model was 83 +/- 8% and 69 +/- 4% for the (1)H NMR and MS data sets respectively. A cross platform comparison of both data sets by co-inertia analysis showed a similar distinction between the LPD and SPD. CONCLUSIONS: Acute changes in urinary metabolomic profiles occur after the consumption of dietary phytochemicals. Dietary restrictions in the 24 h before sample collection may reduce diversity in phytochemical intakes and therefore reduce variation and improve data interpretation in metabolomics studies using urine.

Urine metabolomics.
            (Zhang et al., 2012) Download
Metabolomics is a powerful technique for the discovery of novel biomarkers and elucidation of biochemical pathways to improve diagnosis, prognosis and therapy. An advantage of this approach is its ability to assess global metabolic profiles to enhance pathologic characterization. Urine is an ideal bio-medium for disease study because it is readily available, easily obtained and less complex than other body fluids. Ease of collection allows for serial sampling to monitor disease and therapeutic response. Because of this potential, this paper will review urine metabolomic analysis, discuss its significance in the post-genomic era and highlight the specific roles of endogenous small molecule metabolites in this emerging field.

Urinary metabonomic study on biochemical changes in chronic unpredictable mild stress model of depression
            (Zheng et al., 2010) Download
BACKGROUND: Depression is a prevalent complex psychiatric disorder and its pathophysiological mechanism is not yet well understood. We investigated the metabolic profiling of urine samples from depression model rats to find potential disease biomarkers and research pathology of depression. METHODS: An animal model of depression was produced by chronic unpredictable mild stress (CUMS). Metabolic profiling of the urine was performed by using ultra performance liquid chromatography coupled to mass spectrometry (UPLC-MS). Principal component analysis (PCA) was utilized to classify and reveal the differences between the model group and control group. RESULTS: Principal component analysis displayed a clear separation between CUMS-treated rats and control rats. CUMS-treated rats were characterized by the increase of kynurenic acid, xanthurenic acid, phenylalanine, N(2)-succinyl-l-ornithine, hippuric acid and phenylacetylglycine together with the decrease of tryptophan, indoxyl sulfate, indole-3-acetate, citrate, alpha-ketoglutarate and creatinine in urine. These biochemical changes are related to the disturbance in energy metabolism, amino acid metabolism and gut microflora. CONCLUSIONS: Metabonomic approach is helpful to further understanding the pathophysiology of depression and assisting in clinical diagnosis of depression.



Brennan, L. (2008), ‘Session 2: Personalised nutrition. Metabolomic applications in nutritional research’, Proc Nutr Soc, 67 (4), 404-8. PubMed: 18847517
Gibney, M. J., et al. (2005), ‘Metabolomics in human nutrition: opportunities and challenges’, Am J Clin Nutr, 82 (3), 497-503. PubMed: 16155259
Hu, J. D., et al. (2011), ‘Prediction of gastric cancer metastasis through urinary metabolomic investigation using GC/MS’, World J Gastroenterol, 17 (6), 727-34. PubMed: 21390142
Kim, Y., et al. (2011), ‘Multivariate classification of urine metabolome profiles for breast cancer diagnosis’, BMC Bioinformatics, 11 Suppl 2 S4. PubMed: 20406502
Kind, T., et al. (2007), ‘A comprehensive urinary metabolomic approach for identifying kidney cancer’, Anal Biochem, 363 (2), 185-95. PubMed: 17316536
Lodge, J. K. (2010), ‘Symposium 2: Modern approaches to nutritional research challenges: Targeted and non-targeted approaches for metabolite profiling in nutritional research’, Proc Nutr Soc, 69 (1), 95-102. PubMed: 19954566
Primrose, S., et al. (2011), ‘Metabolomics and human nutrition’, Br J Nutr, 105 (8), 1277-83. PubMed: 21255470
Rasmussen, L. G., et al. (2011), ‘Assessment of dietary exposure related to dietary GI and fibre intake in a nutritional metabolomic study of human urine’, Genes Nutr, PubMed: 21984257
Spratlin, J. L., N. J. Serkova, and S. G. Eckhardt (2009), ‘Clinical applications of metabolomics in oncology: a review’, Clin Cancer Res, 15 (2), 431-40. PubMed: 19147747
Wallace, M., et al. (2010), ‘Effects of menstrual cycle phase on metabolomic profiles in premenopausal women’, Hum Reprod, 25 (4), 949-56. PubMed: 20150174
Walsh, M. C., et al. (2006), ‘Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans’, Am J Clin Nutr, 84 (3), 531-39. PubMed: 16960166
Walsh, M. C., et al. (2007), ‘Influence of acute phytochemical intake on human urinary metabolomic profiles’, Am J Clin Nutr, 86 (6), 1687-93. PubMed: 18065587
Zhang, A, et al. (2012), ‘Urine metabolomics.’, Clin Chim Acta, 414 65-69. PubMed: 22971357
Zheng, S., et al. (2010), ‘Urinary metabonomic study on biochemical changes in chronic unpredictable mild stress model of depression’, Clin Chim Acta, 411 (3-4), 204-9. PubMed: 19913000