MetSCORE: a molecular metric to evaluate the risk of metabolic syndrome based on serum NMR metabolomics

BACKGROUND

Metabolic syndrome (MetS) is a cluster of medical conditions and risk factors correlating with insulin resistance that increase the risk of developing cardiometabolic health problems. The specific criteria for diagnosing MetS vary among different medical organizations but are typically based on the evaluation of abdominal obesity, high blood pressure, hyperglycemia, and dyslipidemia. A unique, quantitative and independent estimation of the risk of MetS based only on quantitative biomarkers is highly desirable for the comparison between patients and to study the individual progression of the disease in a quantitative manner.

METHODS

We used NMR-based metabolomics on a large cohort of donors (n = 21,323; 37.5% female) to investigate the diagnostic value of serum or serum combined with urine to estimate the MetS risk. Specifically, we have determined 41 circulating metabolites and 112 lipoprotein classes and subclasses in serum samples and this information has been integrated with metabolic profiles extracted from urine samples.

RESULTS

We have developed MetSCORE, a metabolic model of MetS that combines serum lipoprotein and metabolite information. MetSCORE discriminate patients with MetS (independently identified using the WHO criterium) from general population, with an AUROC of 0.94 (95% CI 0.920-0.952, p < 0.001). MetSCORE is also able to discriminate the intermediate phenotypes, identifying the early risk of MetS in a quantitative way and ranking individuals according to their risk of undergoing MetS (for general population) or according to the severity of the syndrome (for MetS patients).

CONCLUSIONS

We believe that MetSCORE may be an insightful tool for early intervention and lifestyle modifications, potentially preventing the aggravation of metabolic syndrome.

Metabolomics of Human Semen: A Review of Different Analytical Methods to Unravel Biomarkers for Male Fertility Disorders

Background: Human life without sperm is not possible. Therefore, it is alarming that the fertilizing ability of human spermatozoa is continuously decreasing. The reasons for that are widely unknown, but there is hope that metabolomics-based investigations may be able to contribute to overcoming this problem. This review summarizes the attempts made so far. Methods: We will discuss liquid chromatography–mass spectrometry (LC-MS), gas chromatography (GC), infrared (IR) and Raman as well as nuclear magnetic resonance (NMR) spectroscopy. Almost all available studies apply one of these methods. Results: Depending on the methodology used, different compounds can be detected, which is (in combination with sophisticated methods of bioinformatics) helpful to estimate the state of the sperm. Often, but not in all cases, there is a correlation with clinical parameters such as the sperm mobility. Conclusions: LC-MS detects the highest number of metabolites and can be considered as the method of choice. Unfortunately, the reproducibility of some studies is poor, and, thus, further improvements of the study designs are needed to overcome this problem. Additionally, a stronger focus on the biochemical consequences of the altered metabolite concentrations is also required.

NMR Spectroscopy for Metabolomics Research

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

Risk factors and biomarkers of age-related macular degeneration

A biomarker can be a substance or structure measured in body parts, fluids or products that can affect or predict disease incidence. As age-related macular degeneration (AMD) is the leading cause of blindness in the developed world, much research and effort has been invested in the identification of different biomarkers to predict disease incidence, identify at risk individuals, elucidate causative pathophysiological etiologies, guide screening, monitoring and treatment parameters, and predict disease outcomes. To date, a host of genetic, environmental, proteomic, and cellular targets have been identified as both risk factors and potential biomarkers for AMD. Despite this, their use has been confined to research settings and has not yet crossed into the clinical arena. A greater understanding of these factors and their use as potential biomarkers for AMD can guide future research and clinical practice. This article will discuss known risk factors and novel, potential biomarkers of AMD in addition to their application in both academic and clinical settings.

Repeated implantation failure versus repeated implantation success: discrimination at a metabolomic level

STUDY QUESTION

Is there any difference at the serum metabolic level between women with recurrent implantation failure (RIF) and women with recurrent implantation success (RIS) when undergoing in vitro fertilization (IVF)?

SUMMARY ANSWER

Eight metabolites, including valine, adipic acid, l-lysine, creatine, ornithine, glycerol, d-glucose and urea, were found to be significantly up-regulated in women with RIF when compared with women with RIS.

WHAT IS KNOWN ALREADY

Despite transfer of three high-grade embryos per cycle, RIF following three or more consecutive IVF attempts occurs in a group of infertile women. Conversely, there is a group of women who undergo successful implantation each cycle, yet have a poor obstetric history.

STUDY DESIGN, SIZE, DURATION

This study was conducted over a period of 10 years (January 2004-October 2014). Groups of 28 women with RIF (age ≤40 years and BMI ≤28) and 24 women with RIS (age and BMI matched) were selected from couples with primary infertility reporting at the Institute of Reproductive Medicine, Kolkata, India. Women recruited in the RIF group had history of implantation failure in at least three consecutive IVF attempts, in which three embryos of high-grade quality were transferred in each cycle.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Blood samples were collected from both the groups during the implantation window following overnight fasting for at least 10 h (7-10 days post ovulation). Samples were analyzed using a 700 MHz NMR spectrometer and acquired spectra were subjected to chemometric and statistical analysis. Serum levels of endothelial nitric oxide synthase (eNOS) were measured using an enzyme immunoassay technique.

MAIN RESULTS AND THE ROLE OF CHANCE

Valine, adipic acid, l-lysine, creatine, ornithine, glycerol, d-glucose and urea were found to be significantly down-regulated in women with RIS when compared with those with RIF, with fold change values of 0.81, 0.82, 0.79, 0.80, 0.78, 0.68, 0.76 and 0.74, respectively. Further, serum eNOS was found to be significantly lower in women with RIF when compared with RIS (P < 0.05), indicating possible impairment in nitric oxide production. Metabolites, mostly related to energy metabolism, lipid metabolism and the arginine metabolic pathway were found to be considerably altered and are likely to be associated with the RIF phenomenon. However, the interplay between these molecules in RIF is complex and holds merit for further exploration.

LIMITATIONS, REASONS FOR CAUTION

In-depth studies of the arginine metabolic pathway in endometrial tissues seem necessary to validate our findings. A limitation of the present study is that the metabolic level changes, eNOS and nitric oxide levels have not been investigated in the endometrial tissues of the two groups of women. It would be interesting to investigate whether there exists a direct link between metabolic dysregulation and genetic factors that affects implantation in RIF women.

WIDER IMPLICATIONS OF THE FINDINGS

We speculate that tissue metabolomics can provide an improved understanding of the metabolic dysfunction associated with RIF. The identification of serum metabolic marker(s) in women with RIS may help with strategies of early therapeutic intervention, which may improve the chances of implantation significantly in women otherwise susceptible to IVF failure.

STUDY FUNDING/COMPETING INTERESTS

One of the authors, S.R.C. acknowledges the Council of Scientific and Industrial Research (CSIR), Government of India [No: 9/81(1228)/14, EMR-I] for financial support.

Applications of NMR spectroscopy to systems biochemistry

The past decades of advancements in NMR have made it a very powerful tool for metabolic research. Despite its limitations in sensitivity relative to mass spectrometric techniques, NMR has a number of unparalleled advantages for metabolic studies, most notably the rigor and versatility in structure elucidation, isotope-filtered selection of molecules, and analysis of positional isotopomer distributions in complex mixtures afforded by multinuclear and multidimensional experiments. In addition, NMR has the capacity for spatially selective in vivo imaging and dynamical analysis of metabolism in tissues of living organisms. In conjunction with the use of stable isotope tracers, NMR is a method of choice for exploring the dynamics and compartmentation of metabolic pathways and networks, for which our current understanding is grossly insufficient. In this review, we describe how various direct and isotope-edited 1D and 2D NMR methods can be employed to profile metabolites and their isotopomer distributions by stable isotope-resolved metabolomic (SIRM) analysis. We also highlight the importance of sample preparation methods including rapid cryoquenching, efficient extraction, and chemoselective derivatization to facilitate robust and reproducible NMR-based metabolomic analysis. We further illustrate how NMR has been applied in vitro, ex vivo, or in vivo in various stable isotope tracer-based metabolic studies, to gain systematic and novel metabolic insights in different biological systems, including human subjects. The pathway and network knowledge generated from NMR- and MS-based tracing of isotopically enriched substrates will be invaluable for directing functional analysis of other 'omics data to achieve understanding of regulation of biochemical systems, as demonstrated in a case study. Future developments in NMR technologies and reagents to enhance both detection sensitivity and resolution should further empower NMR in systems biochemical research.

Ancient Wheat Diet Delays Diabetes Development in a Type 2 Diabetes Animal Model

AIM

The main objective was to investigate the physiological effects of ancient wheat whole grain flour diets on the development and progression of type 2 diabetes in Zucker diabetic fatty (ZDF) rats, and specifically to look at the acute glycemic responses.

METHODS

An intervention study was conducted, involving 40 ZDF rats consuming one of 5 different diets (emmer, einkorn, spelt, rye and refined wheat) for 9 weeks. Refined wheat flour and whole grain rye flour were included as negative and positive controls, respectively.

RESULTS

After 9 weeks of intervention, a downregulation of the hepatic genes PPAR-α, GLUT2, and SREBP-1c was observed in the emmer group compared to the control wheat group. Likewise, expression of hepatic SREBP-2 was lower for emmer, einkorn, and rye compared with the control group. Furthermore, spelt and rye induced a low acute glycemic response. The wheat group had higher HDL- and total cholesterol levels.

CONCLUSIONS

Ancient wheat diets caused a downregulation of key regulatory genes involved in glucose and fat metabolism, equivalent to a prevention or delay of diabetes development. Spelt and rye induced a low acute glycemic response compared to wheat.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

[Nuclear magnetic resonance based metabolic phenotyping for patient evaluations in operating rooms and intensive care units]

Metabolic phenotyping consists in the identification of subtle and coordinated metabolic variations associated with various pathophysiological stimuli. Different analytical methods, such as nuclear magnetic resonance, allow the simultaneous quantification of a large number of metabolites. Statistical analyses of these spectra thus lead to the discrimination between samples and the identification of a metabolic phenotype corresponding to the effect under study. This approach allows the extraction of candidate biomarkers and the recovery of perturbed metabolic networks, driving to the generation of biochemical hypotheses (pathophysiological mechanisms, diagnostic tests, therapeutic targets…). Metabolic phenotyping could be useful in anaesthesiology and intensive care medicine for the evaluation, monitoring or diagnosis of life-threatening situations, to optimise patient managements. This review introduces the physical and statistical fundamentals of NMR-based metabolic phenotyping, describes the work already achieved by this approach in anaesthesiology and intensive care medicine. Finally, potential areas of interest are discussed for the perioperative and intensive management of patients, from newborns to adults.

Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome

The complex aetiology of type 2 diabetes makes effective screening, diagnosis and prognosis a substantial challenge for the physician. The rapidly developing area of metabolomics, which uses analytical techniques such as mass spectrometry and nuclear magnetic resonance, has emerged as a promising approach to identify biomarkers of diabetes and the insulin-resistant state that precedes overt pathology. Initial successes with metabolomic studies have indicated potential biomarkers for insulin resistance and for identifying people at risk of developing diabetes, with particular focus on aminoacids and lipid metabolism. These biomarkers will help to improve research and management of diabetes. In particular, several biomarkers identified could be used for early identification of diabetes risk. Furthermore, changes in selected biomarkers can indicate effectiveness of therapeutic interventions for type 2 diabetes and the metabolic syndrome. Indeed, there is much promise that branched-chain aminoacids might provide a screening biomarker for type 2 diabetes risk, allowing early dietary and exercise interventions to treat or even prevent the disease.

Metabolic Profiling of Green Tea Treatments in Zucker Diabetic Rats Using 1H NMR

This study has investigated the metabolic effects of catechin-rich green tea (GT) and its formulation with ascorbic acid (AA) on the Zucker rat model of type 2 diabetes. AA is used to protect the GT catechins during digestion and increase bioavailability. Thirty two Zucker diabetic fatty (ZDF) rats were randomly divided into four groups (n=8 in each group) and treated with water, GT, AA and GT+AA respectively for five weeks. Urinary metabolic profiles were determined using ¹H NMR spectroscopy. Fourteen metabolites were identified and their 24-hr excretions were quantified. Changes in the 14 metabolites demonstrated differential treatment effects on the metabolism of ZDF rats. GT and AA were found to be able to independently reduce urinary excretions of most metabolites that were over-excreted in the control diabetic rats, such as oxidative stress marker metabolites and TCA cycle metabolites. GT showed a great potential in controlling metabolic acidosis by suppressing the excretion of lactic acid and acetic acid from diabetic rats and GT+AA showed a remarkably stronger suppression than GT while AA was unable to suppress these two acids. Further investigation is needed to better understand the role of GT and/or formulated GT in altering the metabolic pathways in the diabetic animal model as well as in humans.

Perspective: a systems approach to diabetes research

We review here the status of human type 2 diabetes studies from a genetic, epidemiological, and clinical (intervention) perspective. Most studies limit analyses to one or a few omic technologies providing data of components of physiological processes. Since all chronic diseases are multifactorial and arise from complex interactions between genetic makeup and environment, type 2 diabetes mellitus (T2DM) is a collection of sub-phenotypes resulting in high fasting glucose. The underlying gene–environment interactions that produce these classes of T2DM are imperfectly characterized. Based on assessments of the complexity of T2DM, we propose a systems biology approach to advance the understanding of origin, onset, development, prevention, and treatment of this complex disease. This systems-based strategy is based on new study design principles and the integrated application of omics technologies: we pursue longitudinal studies in which each subject is analyzed at both homeostasis and after (healthy and safe) challenges. Each enrolled subject functions thereby as their own case and control and this design avoids assigning the subjects a priori to case and control groups based on limited phenotyping. Analyses at different time points along this longitudinal investigation are performed with a comprehensive set of omics platforms. These data sets are generated in a biological context, rather than biochemical compound class-driven manner, which we term “systems omics.”

NMR-based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis

Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so-called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice. In this review, we will present several special examples of a successful application of NMR for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and discuss possible future improvements.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Metabolomics study of type 2 diabetes using ultra-performance LC-ESI/quadrupole-TOF high-definition MS coupled with pattern recognition methods

Type 2 diabetes (T2D), called the burden of the twenty-first century, is growing with an epidemic rate. Here, we explored the differences in metabolite concentrations between T2D patients and healthy volunteers. Metabolomics represents an emerging discipline concerned with comprehensive analysis of small molecule metabolites and provides a powerful approach to discover biomarkers in biological systems. The acquired data were analyzed by ultra-performance liquid chromatography-electrospray ionization/quadrupole time-of-flight high-definition mass spectrometry coupled with pattern recognition approach [principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)] to identify potential disease-specific biomarkers. PCA showed satisfactory clustering between patients and healthy volunteers. Biomarkers reflected the biochemical events associated with early stages of T2D which were observed in PLS-DA loading plots. These urinary differential metabolites, such as adiponectin, acylcarnitines, citric acid, kynurenic acid, 3-indoxyl sulfate, urate, and glucose, were identified involving several key metabolic pathways such as taurine and hypotaurine metabolism; cysteine and methionine metabolism; valine, leucine, and isoleucine biosynthesis metabolism, etc. Our data suggest that robust metabolomics has the potential as a noninvasive strategy to evaluate the early diagnosis of T2D patients and provides new insight into pathophysiologic mechanisms and may enhance the understanding of its cause of disease.

Early metabolic adaptation in C57BL/6 mice resistant to high fat diet induced weight gain involves an activation of mitochondrial oxidative pathways

We investigated the short-term (7 days) and long-term (60 days) metabolic effect of high fat diet induced obesity (DIO) and weight gain in isogenic C57BL/6 mice and examined the specific metabolic differentiation between mice that were either strong-responders (SR), or non-responders (NR) to weight gain. Mice (n = 80) were fed a standard chow diet for 7 days prior to randomization into a high-fat (HF) (n = 56) or a low-fat (LF) (n = 24) diet group. The (1)H NMR urinary metabolic profiles of LF and HF mice were recorded 7 and 60 days after the diet switch. On the basis of the body weight gain (BWG) distribution of HF group, we identified NR mice (n = 10) and SR mice (n = 14) to DIO. Compared with LF, HF feeding increased urinary excretion of glycine conjugates of β-oxidation intermediate (hexanoylglycine), branched chain amino acid (BCAA) catabolism intermediates (isovalerylglycine, α-keto-β-methylvalerate and α-ketoisovalerate) and end-products of nicotinamide adenine dinucleotide (NAD) metabolism (N1-methyl-2-pyridone-5-carboxamide, N1-methyl-4-pyridone-3-carboxamide) suggesting up-regulation of mitochondrial oxidative pathways. In the HF group, NR mice excreted relatively more hexanoylglycine, isovalerylglycine, and fewer tricarboxylic acid (TCA) cycle intermediate (succinate) in comparison to SR mice. Thus, subtle regulation of ketogenic pathways in DIO may alleviate the saturation of the TCA cycle and mitochondrial oxidative metabolism.

NMR-based metabolomics of urine in a mouse model of Alzheimer's disease: identification of oxidative stress biomarkers

Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia among elderly patients. A biomarker for the disease could make diagnosis easier and more accurate, and accelerate drug discovery. In this study, NMR-based metabolomics analysis in conjunction with multivariate statistics was applied to examine changes in urinary metabolites in transgenic AD mice expressing mutant tau and β-amyloid precursor protein. These mice showed significant changes in urinary metabolites throughout the progress of the disease. Levels of 3-hydroxykynurenine, homogentisate and allantoin were significantly higher compared to control mice in 4 months (prior to onset of AD symptoms) and reverted to control values by 10 months of age (early/middle stage of AD), which highlights the relevance of oxidative stress to this neurodegenerative disorder even prior the onset of dementia. The level of these changed metabolites at very early period may provide an indication of disease risk at asymptomatic stage.

A Metabolomics Profiling Study in Hand-Foot-and-Mouth Disease and Modulated Pathways of Clinical Intervention Using Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry

Hand-foot-and-mouth disease (HFMD), with poorly understood pathogenesis, has become a major public health threat across Asia Pacific. In order to characterize the metabolic changes of HFMD and to unravel the regulatory role of clinical intervention, we have performed a metabolomics approach in a clinical trial. In this study, metabolites profiling was performed by liquid chromatography/quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS) platform from the HFMD clinical patient samples. The outcome of this study suggested that 31 endogenous metabolites were mainly involved and showed marked perturbation in HFMD patients. In addition, combination therapy intervention showed normalized tendency in HFMD patients in differential pathway. Taken together, these results indicate that metabolomics approach can be used as a complementary tool for the detection and the study of the etiology of HFMD.

NMR Metabolomics Analysis of Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disease, which is characterized by progressive death of dopaminergic neurons in the substantia nigra pars compacta. Although mitochondrial dysfunction and oxidative stress are linked to PD pathogenesis, its etiology and pathology remain to be elucidated. Metabolomics investigates metabolite changes in biofluids, cell lysates, tissues and tumors in order to correlate these metabolomic changes to a disease state. Thus, the application of metabolomics to investigate PD provides a systematic approach to understand the pathology of PD, to identify disease biomarkers, and to complement genomics, transcriptomics and proteomics studies. This review will examine current research into PD mechanisms with a focus on mitochondrial dysfunction and oxidative stress. Neurotoxin-based PD animal models and the rationale for metabolomics studies in PD will also be discussed. The review will also explore the potential of NMR metabolomics to address important issues related to PD treatment and diagnosis.

Satiety hormone and metabolomic response to an intermittent high energy diet differs in rats consuming long-term diets high in protein or prebiotic fiber

Large differences in the composition of diet between early development and adulthood can have detrimental effects on obesity risk. We examined the effects of an intermittent high fat/sucrose diet (HFS) on satiety hormone and serum metabolite response in disparate diets. Wistar rat pups were fed control (C), high prebiotic fiber (HF) or high protein (HP) diets (weaning to 16 weeks), HFS diet challenged (6 weeks), and finally reverted to their respective C, HF, or HP diet (4 weeks). At conclusion, measurement of body composition and satiety hormones was accompanied by ¹H NMR metabolic profiles in fasted and postprandial states. Metabolomic profiling predicted dietary source with >90% accuracy. The HF group was characterized by lowest body weight and body fat (P < 0.05) and increased satiety hormone levels (glucagon-like peptide 1 and peptide-YY). Regularized modeling confirmed that the HF diet is associated with higher gut hormone secretion that could reflect the known effects of prebiotics on gut microbiota and their fementative end products, the short chain fatty acids. Rats reared on a HF diet appear to experience fewer adverse effects from an intermittent high fat diet in adulthood when rematched to their postnatal diet. Metabolite profiles associated with the diets provide a distinct biochemical signature of their effects.

Investigating potential mechanisms of obesity by metabolomics

Obesity is a serious health problem with an increased risk of several common diseases including diabetes, cardiovascular disease, and cancer. Metabolomics is an emerging analytical technique for systemic determination of metabolite profiles, which is useful for understanding the biochemical changes in obesity or related diseases both in individual organs and at the organism level. Increasingly, this technology has been applied to the study of obesity, complementing transcriptomics and/or proteomics analyses. Indeed, the alterations of metabolites in biofluids/tissues are direct indicators of variations in physiology or pathology. In this paper, we will examine the obesity-related alterations in significant metabolites that have been identified by metabolomics as well as their metabolic pathway associations. Issues concerning the screening of biologically significant metabolites related to obesity will also be discussed.

Dynamic metabolic transformation in tumor invasion and metastasis in mice with LM-8 osteosarcoma cell transplantation

While extensive evidence indicates that tumor cells shift their global metabolic programs, the molecular details of the metabolic transformation in tumor invasion, progression, and metastasis remain largely unknown. Characterization of the time-dependent metabolic shift during the tumor invasion, development, and metastasis will describe an important aspect of tumor phenotypes and potentially allow us to design therapies that inhibit tumor cell movement. In this study, a metabonomic study was performed to characterize the global metabolic changes during the process of tumor invasion and metastasis to lung in a mouse model with subcutaneous transplantation of murine osteosarcoma cell line (LM8). The serum metabolic profiling revealed that many key metabolites in glycolysis and tricarboxylic acid (TCA) cycle, as well as most of the amino acids were elevated at rapidly growing stage of tumor, presumably resulting from a high energy demand and turnover of anabolic metabolism during the tumor cell proliferation. Serum levels of succinic acid and proline significantly increased (with fold change FC = 10.75 and 4.43, relative to controls) among all the metabolites in the third week. The serum metabolic profile of lung metastasis at week 4 was different from that at week 3, in that most of previously increased serum metabolites were found decreased, except for cholesterol and several free fatty acids, suggesting lowered carbohydrate and amino acids metabolism, but an elevated lipid metabolism associated with tumor metastasis.