Metabonomics, dietary influences and cultural differences: a 1H NMR-based study of urine samples obtained from healthy British and Swedish subjects

Multilevel data analysis of a crossover designed human nutritional intervention study

A new method is introduced for the analysis of 'omics' data derived from crossover designed drug or nutritional intervention studies. The method aims at finding systematic variations in metabolic profiles after a drug or nutritional challenge and takes advantage of the crossover design in the data. The method, which can be considered as a multivariate extension of a paired t test, generates different multivariate submodels for the between- and the within-subject variation in the data. A major advantage of this variation splitting is that each submodel can be analyzed separately without being confounded with the other variation sources. The power of the multilevel approach is demonstrated in a human nutritional intervention study which used NMR-based metabolomics to assess the metabolic impact of grape/wine extract consumption. The variations in the urine metabolic profiles are studied between and within the human subjects using the multilevel analysis. After variation splitting, multilevel PCA is used to investigate the experimental and biological differences between the subjects, whereas a multilevel PLS-DA model is used to reveal the net treatment effect within the subjects. The observed treatment effect is validated with cross model validation and permutations. It is shown that the statistical significance of the multilevel classification model ( p << 0.0002) is a major improvement compared to a ordinary PLS-DA model ( p = 0.058) without variation splitting. Finally, rank products are used to determine which NMR signals are most important in the multilevel classification model.

Gender-dependent progression of systemic metabolic states in early childhood

Little is known about the human intra-individual metabolic profile changes over an extended period of time. Here, we introduce a novel concept suggesting that children even at a very young age can be categorized in terms of metabolic state as they advance in development. The hidden Markov models were used as a method for discovering the underlying progression in the metabolic state. We applied the methodology to study metabolic trajectories in children between birth and 4 years of age, based on a series of samples selected from a large birth cohort study. We found multiple previously unknown age- and gender-related metabolome changes of potential medical significance. Specifically, we found that the major developmental state differences between girls and boys are attributed to sphingolipids. In addition, we demonstrated the feasibility of state-based alignment of personal metabolic trajectories. We show that children have different development rates at the level of metabolome and thus the state-based approach may be advantageous when applying metabolome profiling in search of markers for subtle (patho)physiological changes.

Metabolic fingerprinting as a diagnostic tool

Within the framework of systems biology, functional analyses at all 'omic levels have seen an intense level of activity during the first decade of the twenty-first century. These include genomics, transcriptomics, proteomics, metabolomics and lipidomics. It could be said that metabolomics offers some unique advantages over the other 'omics disciplines and one of the core approaches of metabolomics for disease diagnostics is metabolic fingerprinting. This review provides an overview of the main metabolic fingerprinting approaches used for disease diagnostics and includes: infrared and Raman spectroscopy, Nuclear magnetic resonance (NMR) spectroscopy, followed by an introduction to a wide range of novel mass spectrometry-based methods, which are currently under intense investigation and developmental activity in laboratories worldwide. It is hoped that this review will act as a springboard for researchers and clinicians across a wide range of disciplines in this exciting era of multidisciplinary and novel approaches to disease diagnostics.

Critical evaluation of 1H NMR metabonomics of serum as a methodology for disease risk assessment and diagnostics

This review focuses on (i) the current status of 1H NMR spectroscopy to quantify lipoprotein subclasses directly from serum or plasma, and (ii) the applications of 1H NMR metabonomics of serum in biomedicine. Related to both themes, experimental and data analysis methodologies are discussed together with the biochemical rationales. Particular emphasis is placed on the concepts of risk assessment and diagnostics in relation to the potential clinical role of 1H NMR metabonomics; recent applications in the area of coronary heart disease and diabetes are addressed in more detail.

Gender-related and age-related urinalysis of healthy subjects by NMR-based metabonomics

NMR-based metabonomic analysis is a well-established approach to characterizing healthy and diseased states. The aim of this study was to investigate inter-individual variability in the metabolic urinary profile of a healthy Greek population, not subjected to strict dietary limitations, by NMR-based metabonomics. The overall metabonomic urinalysis showed a homogeneous distribution among the population. The metabolic profile was examined in relation to gender and age, and reference intervals of major metabolites were determined. Multivariate data analysis led to the construction of two robust models that were able to predict the class membership of the subjects studied according to their gender and age. The most influential low molecular weight metabolites responsible for the differences in gender groups were citrate, creatinine, trimethylamine N-oxide, glycine, creatine and taurine, and for the differences in age groups they were citrate, creatinine, trimethylamine N-oxide and an unidentified metabolite (delta 3.78).

Evidence of different metabolic phenotypes in humans

The study of metabolic responses to drugs, environmental changes, and diseases is a new promising area of metabonomic research. Metabolic fingerprints can be obtained by analytical techniques such as nuclear magnetic resonance (NMR). In principle, alterations of these fingerprints due to appearance/disappearance or concentration changes of metabolites can provide early evidences of, for example, onset of diseases. A major drawback in this approach is the strong day-to-day variability of the individual metabolic fingerprint, which should be rather called a metabolic "snapshot." We show here that a thorough statistical analysis performed on NMR spectra of human urine samples reveals an invariant part characteristic of each person, which can be extracted from the analysis of multiple samples of each single subject. This finding (i) provides evidence that individual metabolic phenotypes may exist and (ii) opens new perspectives to metabonomic studies, based on the possibility of eliminating the daily "noise" by multiple sample collection.

Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics

Methods for generating and interpreting metabolic profiles based on nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), and chemometric analysis methods are summarized and the relative strengths and weaknesses of NMR and chromatography-coupled MS approaches are discussed. Given that all data sets measured to date only probe subsets of complex metabolic profiles, we describe recent developments for enhanced information recovery from the resulting complex data sets, including integration of NMR- and MS-based metabonomic results and combination of metabonomic data with data from proteomics, transcriptomics, and genomics. We summarize the breadth of applications, highlight some current activities, discuss the issues relating to metabonomics, and identify future trends.

Influence of acute phytochemical intake on human urinary metabolomic profiles

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.

Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles

MOTIVATION

Serum lipids have been traditionally studied in the context of lipoprotein particles. Today's emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today.

RESULTS

We developed a hierarchical Bayesian regression model to study lipidomic profiles in serum and in different lipoprotein classes. As a background data for the model building, we utilized lipidomic data for each of the lipoprotein fractions from 5 subjects with metabolic syndrome and 12 healthy controls. We clustered the lipid profiles and applied a regression model within each cluster separately. We found that the amount of a lipid in serum can be adequately described by the amounts of lipids in the lipoprotein classes. In addition to improved ability to interpret lipidomic data, we expect that our approach will also facilitate dynamic modelling of lipid metabolism at the individual molecular species level.

An NMR-based metabonomic investigation on effects of milk and meat protein diets given to 8-year-old boys

The objective of the study was to investigate the ability of an NMR-based metabonomic approach, applied to biofluids, to explore and identify overall exogenous and endogenous biochemical effects of a short-time high intake of milk protein or meat protein given to prepubertal children, the aim being to compare relative differences and not an absolute quantification. A total of twenty-four 8-year-old boys were asked to take 53 g protein as milk (n 12) or meat daily (n 12). At baseline and after 7 d, urine and serum samples were collected and high-resolution 1H NMR spectra were acquired on these using a 800 MHz spectrometer. The milk diet reduced the urinary excretion of hippurate, while the meat diet increased the urinary excretion of creatine, histidine and urea. The NMR measurements on serum revealed minor changes in the lipid profile, which most probably should be ascribed to an increase in the content of SCFA in the blood after consumption of the milk diet. The meat diet had no effect on the metabolic profile of serum. The study for the first time demonstrates the capability of proton NMR-based metabonomics to identify the overall biochemical effects of consumption of different animal proteins. The urine metabolite profile is more susceptible to perturbations as a result of short diet interventions than the serum metabolite profile. The milk diet-induced reduction in urinary excretion of hippurate suggests alterations in gut microflora, which may be useful information for further studies elucidating the effects of bioactive components in milk.

Multivariate techniques and their application in nutrition: a metabolomics case study

The post-genomic technologies are generating vast quantities of data but many nutritional scientists are not trained or equipped to analyse it. In high-resolution NMR spectra of urine, for example, the number and complexity of spectral features mean that computational techniques are required to interrogate and display the data in a manner intelligible to the researcher. In addition, there are often multiple underlying biological factors influencing the data and it is difficult to pinpoint which are having the most significant effect. This is especially true in nutritional studies, where small variations in diet can trigger multiple changes in gene expression and metabolite concentration. One class of computational tools that are useful for analysing this highly multivariate data include the well-known 'whole spectrum' methods of principal component analysis and partial least squares. In this work, we present a nutritional case study in which NMR data generated from a human dietary Cu intervention study is analysed using multivariate methods and the advantages and disadvantages of each technique are discussed. It is concluded that an alternative approach, called feature subset selection, will be important in this type of work; here we have used a genetic algorithm to identify the small peaks (arising from metabolites of low concentration) that have been altered significantly following a dietary intervention.

Short-chain aliphatic amines in human urine: a mathematical examination of metabolic interrelationships

The relationships between several small molecular weight aliphatic amines (methylamine, dimethylamine, trimethylamine, and ethylamine) and an associated N-oxide (trimethylamine N-oxide) quantified in human urine collected from 203 healthy volunteers have been assessed mathematically. Principal component analysis highlighted a female subgroup with raised trimethylamine levels and the possibility of hormonal influence on the N-oxidation of trimethylamine has been proposed. A second subgroup of men, who ate a large meal of fish before the study, displayed raised levels of all compounds except ethylamine. In all cases, ethylamine was least significantly correlated with the other urinary components and appeared metabolically unrelated.

Investigation of analytical variation in metabonomic analysis using liquid chromatography/mass spectrometry

Sources of analytical variation in high-performance liquid chromatography/mass spectrometry (HPLC/MS), such as changes in retention, mass accuracy or signal intensity, have been investigated to assess their importance as a variable in the metabonomic analysis of human urine. In this study chromatographic retention and mass accuracy were found to be quite reproducible with the most significant source of analytical variation in the data sets obtained being the result of changes in detector response. Depending on the signal intensity threshold used to define the presence of a peak a sample component could be present in some replicate injections and absent in others within the same run. The implementation of a more sophisticated data software analysis package was found to greatly reduce the impact of detector response variability resulting in improved data analysis.

Nuclear Magnetic Resonance Metabonomics

Nuclear magnetic resonance (NMR)-based metabonomics is gaining popularity in drug discovery and development and in academia in a variety of settings, ranging from toxicology, preclinical, and clinical approaches to nutrition research, studies on microorganisms, and research on plants. This chapter focuses on the basic steps in a metabonomics study and emphasizes experience and lessons learned in our lab where we focused on metabonomic analyses of plant extracts, cell lines, and a variety of animal tissues and biofluids. We emphasize that a comprehensive and suitable study design is pivotal for a correct biological interpretation of the results, as well as highly controlled experimental conditions. Sample preparation and NMR protocols are detailed for a wide range of sample types. We discuss alternative data processing strategies and considerations for a general data analysis approach, paying particular attention to the statistical interpretation and validation of the results while also highlighting approaches to avoid possible pitfalls resulting from systematic and random errors. A tutorial written for the R statistical package and other small utilities are available from the authors upon request.

The biological revolution: understanding the impact of SNPs on diet-cancer interrelationships

Evidence is accumulating that individual risk of neoplasia depends on complex interactions among genetic inheritance, a range of exposures both in utero and in postnatal life, and the play of chance. Knowledge of the portfolio of genetic variants that confer susceptibility or resistance to cancer is limited, and there is potential for genome-wide scans and hypothesis-driven studies to reveal novel polymorphisms and haplotypes that modify risk. There is only fragmentary evidence of the scale and nature of diet-gene interactions that modulate risk of neoplasia, but it seems probable that such interactions will play a significant role as they do in other complex diseases including cardiovascular disease and type 2 diabetes. All existing evidence about diet-gene interactions and cancer risk comes from observational studies, and it will be necessary to undertake intervention studies to test the hypotheses generated by epidemiologic investigations. Because it is very unlikely that primary cancer will be an endpoint in dietary intervention studies in the foreseeable future, development of robust surrogate endpoints is a high priority. Emerging biological science using epigenomics, proteomics, and other molecular technologies appears to offer novel approaches to the discovery and validation of surrogate endpoints.

Metabolomics as a functional genomic tool for understanding lipid dysfunction in diabetes, obesity and related disorders

With the rise of systems biology, a number of approaches have been developed to globally profile a tier of organization in a cell, tissue or organism. Metabolomics is an approach that attempts to profile all the metabolites in a biological matrix. One of the major challenges of this approach, as with other 'omic' technologies, is that the metabolome is context-dependent and will vary with pathology, developmental stage and environmental factors. Thus, the possibility of globally profiling the metabolome of an organism is a genuine analytical challenge, as by definition this must also take into consideration all relevant factors that influence metabolism. Despite these challenges, the approach has already been applied to understand the metabolism in a range of animal models, and has more recently started to be projected into the clinical situation. In this review, the technologies currently being used in metabolomics will be assessed prior to examining their use to study diseases related to the metabolic syndrome, including Type II diabetes, obesity, cardiovascular disease and fatty liver disease.

Metabolomic approaches to phenotype characterization and applications to complex diseases

Metabolites are the key regulators of systems homeostasis. As such, concentration changes of specific groups of metabolites may reflect systemic responses to environmental, therapeutic or genetic interventions. Thus, the study of metabolites is a powerful tool for the characterization of complex phenotypes as well as for the development of biomarkers for specific physiological responses. Therefore, metabolomics is a valuable platform for studies of complex diseases and the development of new therapies, both in nonclinical disease model characterization and clinical settings.

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( approximately 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs.

Metabolomics-based systems biology and personalized medicine: moving towards n = 1 clinical trials?

Personalized medicine – defined as customized medical care for each patient’s unique condition – in the broader context of personalized health, will make significant strides forward when a systems approach is implemented to achieve the ultimate in disease phenotyping and to create novel therapeutics that address system-wide molecular perturbations caused by disease processes. Combination drug therapies with individualized optimization are likely to become a major focus. Metabolomics incorporates the most advanced approaches to molecular phenotype system readout and provides the ideal theranostic technology platform for the discovery of biomarker patterns associated with healthy and diseased states, for use in personalized health monitoring programs and for the design of individualized interventions.

The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball?

To date most global approaches to functional genomics have centred on genomics, transcriptomics and proteomics. However, since a number of high-profile publications, interest in metabolomics, the global profiling of metabolites in a cell, tissue or organism, has been rapidly increasing. A range of analytical techniques, including 1H NMR spectroscopy, gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), Fourier Transform mass spectrometry (FT-MS), high performance liquid chromatography (HPLC) and electrochemical array (EC-array), are required in order to maximize the number of metabolites that can be identified in a matrix. Applications have included phenotyping of yeast, mice and plants, understanding drug toxicity in pharmaceutical drug safety assessment, monitoring tumour treatment regimes and disease diagnosis in human populations. These successes are likely to be built on as other analytical and bioinformatic approaches are developed to fully exploit the information obtained in metabolic profiles. To assist in this process, databases of metabolomic data will be necessary to allow the passage of information between laboratories. In this prospective review, the capabilities of metabolomics in the field of medicine will be assessed in an attempt to predict the impact this 'Cinderella approach' will have at the 'functional genomic ball'.

Pharmaco-metabonomic phenotyping and personalized drug treatment

There is a clear case for drug treatments to be selected according to the characteristics of an individual patient, in order to improve efficacy and reduce the number and severity of adverse drug reactions. However, such personalization of drug treatments requires the ability to predict how different individuals will respond to a particular drug/dose combination. After initial optimism, there is increasing recognition of the limitations of the pharmacogenomic approach, which does not take account of important environmental influences on drug absorption, distribution, metabolism and excretion. For instance, a major factor underlying inter-individual variation in drug effects is variation in metabolic phenotype, which is influenced not only by genotype but also by environmental factors such as nutritional status, the gut microbiota, age, disease and the co- or pre-administration of other drugs. Thus, although genetic variation is clearly important, it seems unlikely that personalized drug therapy will be enabled for a wide range of major diseases using genomic knowledge alone. Here we describe an alternative and conceptually new 'pharmaco-metabonomic' approach to personalizing drug treatment, which uses a combination of pre-dose metabolite profiling and chemometrics to model and predict the responses of individual subjects. We provide proof-of-principle for this new approach, which is sensitive to both genetic and environmental influences, with a study of paracetamol (acetaminophen) administered to rats. We show pre-dose prediction of an aspect of the urinary drug metabolite profile and an association between pre-dose urinary composition and the extent of liver damage sustained after paracetamol administration.

Probing gender-specific metabolism differences in humans by nuclear magnetic resonance-based metabonomics

The measurement of metabolite profiles that are interpreted to yield biomarkers using multivariate data analysis is now a well-established approach for gaining an improved understanding of the impact of genetic modifications, toxicological and therapeutic interventions, and exposure to stimuli (e.g., noxious agents, stressors, nutrients) on the network of transcripts, proteins, and metabolites present in cells, tissues, or whole organisms. This has been termed metabonomics. In this study, multivariate analysis of (1)H nuclear magnetic resonance (NMR) spectra of metabolite profiles of urine and plasma from 150 healthy humans revealed that in young people and/or individuals with low body mass indexes, females had higher rates of lipid biosynthesis than did males, whereas males had higher rates of protein turnover than did females. With increasing age, overall lipid biosynthesis decreased in females, whereas metabolism increasingly favored lipid synthesis over protein turnover in males. By relating the derived metabonomic data to known metabolic pathways and published biochemical data, it appears that females synthesize relatively more lipoproteins and unsaturated lipids than do males. Furthermore, the changes in lipid biosynthesis and urinary citrate excretion in females showed a positive correlation. Estrogen most likely plays an essential role in the regulation of, and communication between, protein and lipid biosynthesis by controlling pH in mitochondria and the cytoplasm and hence the observed altered citrate levels.

Applying biofluid 1H NMR-based metabonomic techniques to distinguish between HIV-1 positive/AIDS patients on antiretroviral treatment and HIV-1 negative individuals

Metabonomics, the study of metabolites and their role in various physiological states, is a novel methodology arising from the post-genomics era and has extensive biomedical application. This technology has exhibited vast success in the identification and study of human diseases and may find further application in the study of HIV/AIDS. Specifically, the wide range of clinical and metabolic abnormalities associated with the use of antiretroviral (ARV) treatment may be investigated. To this end, this preliminary study evaluated whether metabonomic techniques could distinguish between HIV-1 positive/AIDS patients utilizing antiretroviral therapy and HIV-1 negative individuals. Serum metabolic profiles determined by 1H nuclear magnetic resonance (NMR) spectroscopy combined with pattern recognition analysis of the data showed that this distinction was attainable; suggesting that ARV-associated side-effects could be monitored utilizing NMR metabonomic techniques.

Nutrition and food science go genomic

The wealth of genomic information and high-throughput profiling technologies are now being exploited by scientists in the disciplines of nutrition and food science. Diet and food components are prime environmental factors that affect the genome, transcriptome, proteome and metabolome, and this life-long interaction defines the health or disease state of an individual. For the first time the interaction of foods, and individual food constituents, with the biological systems can be defined on a molecular basis. Profiling technologies are used in basic-science applications for identifying the mode of action of foods or particular ingredients, and are similarly taken into the science-driven development of foods with a defined biofunctionality. Biomarker profiles and patterns derived from genomics applications in humans should guide nutrition and food science in developing evidence-based dietary recommendations and health-promoting foods.

Gas chromatography/flame ionisation detection mass spectrometry for the detection of endogenous urine metabolites for metabonomic studies and its use as a complementary tool to nuclear magnetic resonance spectroscopy

Metabonomics is a relatively new field of research in which the total pool of metabolites in body fluids or tissues from different patient groups is subjected to comparative analysis. Nuclear magnetic resonance (NMR) spectroscopy is the technology that is currently most widely used for the analysis of these highly complex metabolite mixtures, and hundreds of metabolites can be detected without any upfront separation. We have investigated in this study whether gas chromatography (GC) separation in combination with flame ionisation detection (FID) and mass spectrometry (MS) detection can be used for metabolite profiling from urine. We show that although GC sample preparation is much more involved than for NMR, hundreds of metabolites can reproducibly be detected and analysed by GC. We show that the data quality is sufficiently high--particularly if appropriate baseline correction and time-warping methods are applied--to allow for data comparison by chemometrics methods. A sample set of urines from eleven healthy human volunteers was analysed independently by GC and NMR, and subsequent chemometrics analysis of the two datasets showed some similar features. As judged by NIST database searches of the GC/MS data some of the major metabolites that are detected by NMR are also visible by GC/MS. Since in contrast to NMR every peak in GC corresponds to a single metabolite, the electron ionisation spectra can be used to quickly identify metabolites of interest if their reference spectra are present in a searchable database. In summary, we show that GC is a method that can be used as a complementary tool to NMR for metabolite profiling of urine samples.

Metabolomics in human nutrition: opportunities and challenges

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.

Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets

LC/MS is an analytical technique that, due to its high sensitivity, has become increasingly popular for the generation of metabolic signatures in biological samples and for the building of metabolic data bases. However, to be able to create robust and interpretable (transparent) multivariate models for the comparison of many samples, the data must fulfil certain specific criteria: (i) that each sample is characterized by the same number of variables, (ii) that each of these variables is represented across all observations, and (iii) that a variable in one sample has the same biological meaning or represents the same metabolite in all other samples. In addition, the obtained models must have the ability to make predictions of, e.g. related and independent samples characterized accordingly to the model samples. This method involves the construction of a representative data set, including automatic peak detection, alignment, setting of retention time windows, summing in the chromatographic dimension and data compression by means of alternating regression, where the relevant metabolic variation is retained for further modelling using multivariate analysis. This approach has the advantage of allowing the comparison of large numbers of samples based on their LC/MS metabolic profiles, but also of creating a means for the interpretation of the investigated biological system. This includes finding relevant systematic patterns among samples, identifying influential variables, verifying the findings in the raw data, and finally using the models for predictions. The presented strategy was here applied to a population study using urine samples from two cohorts, Shanxi (People's Republic of China) and Honolulu (USA). The results showed that the evaluation of the extracted information data using partial least square discriminant analysis (PLS-DA) provided a robust, predictive and transparent model for the metabolic differences between the two populations. The presented findings suggest that this is a general approach for data handling, analysis, and evaluation of large metabolic LC/MS data sets.