Plasma metabolic fingerprinting of childhood obesity by GC/MS in conjunction with multivariate statistical analysis

Metabolomics in acromegaly: a systematic review

The therapeutic response heterogeneity in acromegaly persists, despite the medical-surgical advances of recent years. Thus, personalized medicine implementation, which focuses on each patient, is justified. Metabolomics would decipher the molecular mechanisms underlying the therapeutic response heterogeneity. Identification of altered metabolic pathways would open new horizons in the therapeutic management of acromegaly. This research aimed to evaluate the metabolomic profile in acromegaly and metabolomics' contributions to understanding disease pathogenesis. A systematic review was carried out by querying four electronic databases and evaluating patients with acromegaly through metabolomic techniques. In all, 21 studies containing 362 patients were eligible. Choline, the ubiquitous metabolite identified in growth hormone (GH)-secreting pituitary adenomas (Pas) by in vivo magnetic resonance spectroscopy (MRS), negatively correlated with somatostatin receptors type 2 expression and positively correlated with magnetic resonance imaging T2 signal and Ki-67 index. Moreover, elevated choline and choline/creatine ratio differentiated between sparsely and densely granulated GH-secreting PAs. MRS detected low hepatic lipid content in active acromegaly, which increased after disease control. The panel of metabolites of acromegaly deciphered by mass spectrometry (MS)-based techniques mainly included amino acids (especially branched-chain amino acids and taurine), glyceric acid, and lipids. The most altered pathways in acromegaly were the metabolism of glucose (particularly the downregulation of the pentose phosphate pathway), linoleic acid, sphingolipids, glycerophospholipids, arginine/proline, and taurine/hypotaurine. Matrix-assisted laser desorption/ionization coupled with MS imaging confirmed the functional nature of GH-secreting PAs and accurately discriminated PAs from healthy pituitary tissue.

Markers for obese and non-obese Type 2 diabetes identified using whole blood metabolomics

Definitive differences in blood metabolite profiles between obese and non-obese Type 2 diabetes (T2D) have not been established. We performed an LC-MS-based non-targeted metabolomic analysis of whole blood samples collected from subjects classified into 4 types, based on the presence or absence of obesity and T2D. Of the 125 compounds identified, 20, comprising mainly nucleobases and glucose metabolites, showed significant increases or decreases in the T2D group. These included cytidine, UDP-glucuronate, UMP, 6-phosphogluconate, and pentose-phosphate. Among those 20 compounds, 11 enriched in red blood cells (RBCs) have rarely been studied in the context of diabetes, indicating that RBC metabolism is more extensively disrupted than previously known. Correlation analysis revealed that these T2D markers include 15 HbA1c-associated and 5 irrelevant compounds that may reflect diabetic conditions by a different mechanism than that of HbA1c. In the obese group, enhanced protein and fatty acid catabolism causes increases in 13 compounds, including methylated or acetylated amino acids and short-chain carnitines. Our study, which may be considered a pilot investigation, suggests that changes in blood metabolism due to obesity and diabetes are large, but essentially independent.

Dose effect of high-docosahexaenoic acid tuna oil on dysbiosis in high-fat diet mice

BACKGROUND

The health benefits of tuna oil, which is different from the fish oil commonly studied, and its higher docosahexaenoic acid (DHA) content, have attracted much scientific attention in recent years. In this study, prepared tuna oil with higher DHA (HDTO) content was employed. It was the first to integrate microbiome and metabolome from a dose-effect perspective to investigate the influence of HDTO on gut dysbiosis and metabolic disorders in diet-induced obese mice.

RESULTS

Higher DHA tuna oil was effective in reversing high-fat-diet-induced metabolic disorders and altering the composition and function of gut microbiota, but these effects were not uniformly dose dependent. The flora and metabolites that were targeted to be regulated by HDTO supplementation were Prevotella, Bifidobacterium, Olsenella, glycine, l-aspartate, l-serine, l-valine, l-isoleucine, l-threonine, l-tyrosine, glyceric acid, glycerol, butanedioic acid, and citrate, respectively. Functional pathway analysis revealed that alterations in these metabolic biomarkers were associated with six main metabolic pathways: glycine, serine, and threonine metabolism; glycerolipid metabolism; glyoxylate and dicarboxylate metabolism; alanine, aspartate, and glutamate metabolism; aminoacyl-tRNA biosynthesis, and the citrate cycle (TCA cycle).

CONCLUSION

Various doses of HDTO could attenuate endogenous disorders to varying degrees by regulating multiple perturbed pathways to the normal state. This explicit dose research for novel fish oil with high-DHA will provide a valuable reference for those seeking to exploit its clinical therapeutic potential. © 2022 Society of Chemical Industry.

A systematic review of metabolomic studies of childhood obesity: State of the evidence for metabolic determinants and consequences

Childhood obesity has become a global epidemic and carries significant long-term consequences to physical and mental health. Metabolomics, the global profiling of small molecules or metabolites, may reveal the mechanisms of development of childhood obesity and clarify links between obesity and metabolic disease. A systematic review of metabolomic studies of childhood obesity was conducted, following Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, searching across Scopus, Ovid, Web of Science and PubMed databases for articles published from January 1, 2005 to July 8, 2020, retrieving 1271 different records and retaining 41 articles for qualitative synthesis. Study quality was assessed using a modified Newcastle-Ottawa Scale. Thirty-three studies were conducted on blood, six on urine, three on umbilical cord blood, and one on saliva. Thirty studies were primarily cross-sectional, five studies were primarily longitudinal, and seven studies examined effects of weight-loss following a life-style intervention. A consistent metabolic profile of childhood obesity was observed including amino acids (particularly branched chain and aromatic), carnitines, lipids, and steroids. Although the use of metabolomics in childhood obesity research is still developing, the identified metabolites have provided additional insight into the pathogenesis of many obesity-related diseases. Further longitudinal research is needed into the role of metabolic profiles and child obesity risk.

Causative Mechanisms of Childhood and Adolescent Obesity Leading to Adult Cardiometabolic Disease: A Literature Review

The past few decades have shown a worrisome increase in the prevalence of obesity and its related illnesses. This increasing burden has a noteworthy impact on overall worldwide mortality and morbidity, with significant economic implications as well. The same trend is apparent regarding pediatric obesity. This is a particularly concerning aspect when considering the well-established link between cardiovascular disease and obesity, and the fact that childhood obesity frequently leads to adult obesity. Moreover, most obese adults have a history of excess weight starting in childhood. In addition, given the cumulative character of both time and severity of exposure to obesity as a risk factor for associated diseases, the repercussions of obesity prevalence and related morbidity could be exponential in time. The purpose of this review is to outline key aspects regarding the current knowledge on childhood and adolescent obesity as a cardiometabolic risk factor, as well as the most common etiological pathways involved in the development of weight excess and associated cardiovascular and metabolic diseases.

Associations of Obesity Measurements with Serum Metabolomic Profile: A Chinese Twin Study

The objective of this study was to investigate how different obesity measures link to circulating metabolites, and whether the connections are due to genetic or environmental factors. A cross-sectional analysis was performed on follow-up survey data at the Chinese National Twin Registry (CNTR), which was conducted in four areas of China (Shandong, Jiangsu, Zhejiang and Sichuan) in 2013. The survey collected detailed questionnaire information and conducted physical examinations, fasting blood sampling and untargeted metabolomic measurements among 439 adult twins. Linear regression models and bioinformatics analysis were used to examine the relation of obesity measures, including body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) with serum metabolite levels and related pathways. A co-twin control study was additionally conducted among 15 obesity-discordant monozygotic (MZ) pairs (intrapair BMI difference >3 kg/m2) to examine any differences in metabolites controlling for genetic factors. Eleven metabolites were associated with BMI, WC and WHR after controlling for genetic and shared environmental factors. Pathway analysis identified pathways such as phenylalanine metabolism, purine metabolism, valine, leucine and isoleucine biosynthesis that were associated with obesity. A wide range of unfavorable alterations in the serum metabolome was associated with obesity. Obesity-discordant twin analysis suggests that these associations are independent of genetic liability.

Indolic Structure Metabolites as Potential Biomarkers of Non-infectious Diseases

Interest in indolic structure metabolites, including a number of products of microbial biotransformation of the aromatic amino acid tryptophan, is increasingly growing. The review prepared by a team of authors is based on in-depthscrutiny of data available in PubMed, Scopus, Cyberleninka, Clinical Trials, and Cochrane Library, eventually narrowing the search to a set of keywords such as tryptophan metabolites; plasma metabolomics profiling; metabolomics fingerprinting; gas-, liquid chromatography mass spectrometry; serotonin; melatonin; tryptamine; indoxyl sulfate; indole-3-acetic acid; indole-3-propionic acid; 5-hydroxyindole-3-acetic acid; gut microbiota and microbial metabolites. It provides a summary that outlines the pattern of changes in the level of indolic structure metabolites in a number of diseases and deals with the data from the field of human microbiota metabolites. In modern experimental studies, including the use of gnotobiological (germ-free) animals, it has been convincingly proved that the formation of tryptophan metabolites such as indole-3-acetic acid, indole-3-propionic acid, tryptamine, and indoxyl sulfate is associated with gut bacteria. Attention to some concentration changes of indolic compounds is due to the fact that pronounced deviations and a significant decrease of these metabolites in the blood were found in a number of serious cardiovascular, brain or gastrointestinal diseases. The literature-based analysis allowed the authors to conclude that a constant (normal) level of the main metabolites of the indolic structure in the human body is maintained by a few strict anaerobic bacteria from the gut of a healthy body belonging to the species of Clostridium, Bacteroides, Peptostreptococcus, Eubacteria, etc. The authors focus on several metabolites of the indolic structure that can be called clinically significant in certain diseases, such as schizophrenia, depression, atherosclerosis, colorectal cancer, etc. Determining the level of indole metabolites in the blood can be used to diagnose and monitor the effectiveness of a comprehensive treatment approach.

Metabolic profiling of acromegaly using a GC-MS-based nontargeted metabolomic approach

PURPOSE

Acromegaly is a rare disease caused by chronic hypersecretion of growth hormone, which leads to multiple comorbidities and reduced life expectancy. The objective of this study was to characterize the serum metabolic profiles of acromegaly patients and identify metabolic biomarkers using metabolomics.

METHODS

Twenty-nine active acromegaly patients and age- and sex-matched normal controls were recruited. Serum samples were collected, and serum metabolites were analyzed using gas chromatography-mass spectrometry coupled with a series of multivariate statistical analyses.

RESULTS

The orthogonal projections to latent structures-discriminate analysis (OPLS-DA) model identified and validated significant metabolic differences between individuals with acromegaly and normal controls (R²Y = 0.908 and Q²Y = 0.601). Compared with normal controls, acromegaly patients had elevated levels of 5-aminovaleric acid, glyceric acid, L-dithiothreitol, dihydrocoumarin, N-acetyl-L-glutamic acid, gluconic acid, and monoolein (P < 0.05) and reduced serum levels of D-erythronolactone, taurine, carbamoyl-aspartic acid, and mucic acid (P < 0.01). Furthermore, glyceric acid and taurine possessed higher area under the receiver operating characteristic curve values (AUC values, 0.914 and 0.931, respectively), suggesting an excellent clinical ability to distinguish acromegaly patients from normal controls. Pathway analysis revealed that the pentose phosphate pathway and the taurine and hypotaurine metabolic pathway are significant pathways (P = 0.002 and 0.004, respectively).

CONCLUSIONS

Metabolic activity is significantly altered in the serum of individuals with active acromegaly. Glyceric acid and taurine may be considered potential biomarkers for distinguishing acromegaly patients from normal controls.

Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

INTRODUCTION

Obesity is a disorder characterized by a disproportionate increase in body weight in relation to height, mainly due to the accumulation of fat, and is considered a pandemic of the present century by many international health institutions. It is associated with several non-communicable chronic diseases, namely, metabolic syndrome, type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and cancer. Metabolomics is a useful tool to evaluate changes in metabolites due to being overweight and obesity at the body fluid and cellular levels and to ascertain metabolic changes in metabolically unhealthy overweight and obese individuals (MUHO) compared to metabolically healthy individuals (MHO).

OBJECTIVES

We aimed to conduct a systematic review (SR) of human studies focused on identifying metabolomic signatures in obese individuals and obesity-related metabolic alterations, such as inflammation or oxidative stress.

METHODS

We reviewed the literature to identify studies investigating the metabolomics profile of human obesity and that were published up to May 7th, 2019 in SCOPUS and PubMed through an SR. The quality of reporting was evaluated using an adapted of QUADOMICS.

RESULTS

Thirty-three articles were included and classified according to four types of approaches. (i) studying the metabolic signature of obesity, (ii) studying the differential responses of obese and non-obese subjects to dietary challenges (iii) studies that used metabolomics to predict weight loss and aimed to assess the effects of weight loss interventions on the metabolomics profiles of overweight or obese human subjects (iv) articles that studied the effects of specific dietary patterns or dietary compounds on obesity-related metabolic alterations in humans.

CONCLUSION

The present SR provides state-of-the-art information about the use of metabolomics as an approach to understanding the dynamics of metabolic processes involved in human obesity and emphasizes metabolic signatures related to obesity phenotypes.

Dose-dependent target diversion of Danhong injection on the Glu-GLT-1/Gly-GlyRα dynamic balance module of cerebral ischemia

Function-oriented modular structure analysis is a great challenge in module-based pharmacological studies. A strategy to uncover target-target interaction (TTI) and dynamic balance regularity (DBR) was established to discover the structural factors influencing modular functions and explore the mechanism of Danhong injection (DHI) in treating cerebral ischemia. The dose-related metabolic features of DHI intervention were investigated using metabolomics and modular pharmacology. The findings indicated that Glu/Gly was a biomarker and Glu-GLT-1/Gly-GlyRα was the core unit regulated by DHI. Gly and Glu displayed opposite patterns and functional roles, representing intra-modular balance. GlyRα was identified as the upstream target and GLT-1 as the downstream target by inhibiting or activating GlyRα, indicating that DHI has two dose-dependent regulatory modes. GlyRα was the major target at low doses, while GLT-1 was activated as the dominant target as doses accumulated. Our study reveals that target-target interaction and dynamic balance regularity are the key factors influencing modular functions, which is a promising breakthrough for module-based pharmacological studies.

Amino acid levels in nascent metabolic syndrome: A contributor to the pro-inflammatory burden

AIMS

Metabolic Syndrome (MetS) is a cluster of cardio-metabolic risk factors characterized by low-grade inflammation which confers an increased risk for type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD). Prior studies have linked elevated branched chain amino acids (BCAA) and aromatic amino acids (AAA) with T2DM and CVD. Due to the paucity of data in MetS, the aim of this study was to investigate the status of amino acids as early biomarkers of nascent MetS patients without T2DM and CVD or smoking.

RESEARCH DESIGN AND METHODS

Healthy controls (n = 20) and MetS (n = 29) patients were recruited for the study. MetS was defined by criteria of National Cholesterol Education Program Adult Treatment Panel III of having at least 3 risk factors. Urinary amino acids were quantified by gas chromatography time-of-flight mass spectrometry at the Western NIH Metabolomics Center as expressed to urinary creatinine.

RESULTS

Tyrosine and Isoleucine levels were significantly elevated in MetS patients. Isoleucine positively correlated with salient cardio-metabolic features and inflammatory biomarkers. Lysine and Methionine levels were decreased in MetS patients. Lysine correlated negatively with cardio-metabolic features and inflammatory bimarkers. Methionine also correlated negatively with blood pressure and certain inflammatory biomarkers.

CONCLUSION

Our novel results suggest that with regards to the cardio-metabolic risk factors and pro-inflammatory features of MetS, isoleucine (BCAA) demonstrated a positive correlation while lysine demonstrated a negative correlation. Thus, increased levels of isoleucine and decreased levels of lysine could be potential early biomarkers of MetS.

Plasma Amino Acids During 8 Weeks of Overfeeding: Relation to Diet Body Composition and Fat Cell Size in the PROOF Study

OBJECTIVE

Different amounts of dietary protein during overfeeding produced similar fat gain but different amounts of gain in fat-free body mass. Protein and energy intake may have differential effects on amino acids during overfeeding.

METHODS

Twenty-three healthy adult men and women were overfed by 40% for 8 weeks with 5%, 15%, or 25% protein diets. Plasma amino acids were measured by gas chromatography and mass spectrometry at baseline and week 8. Body composition was measured by dual-energy x-ray absorptiometry, fat cell size (FCS) from subcutaneous fat biopsies, and insulin resistance by euglycemic-hyperinsulinemic clamp.

RESULTS

The following three amino acid patterns were seen: increasing concentration of five essential and three nonessential amino acids with increasing protein intake, higher levels of six nonessential amino acids with the low-protein diet, and a pattern that was flat or "V" shaped. Dietary fat and protein were both correlated with changes in valine, leucine/isoleucine/norleucine, phenylalanine, and tyrosine, but energy intake was not. The change in fat mass and weight was related to the change in several amino acids. Baseline FCS and the interaction between glucose disposal and FCS were associated with changes in several amino acids during overfeeding.

CONCLUSIONS

Overfeeding dietary protein affects the levels of both essential and nonessential amino acids.

Adaptive Immunity and Metabolic Health: Harmony Becomes Dissonant in Obesity and Aging

Adipose tissue (AT) is the primary energy reservoir organ, and thereby plays a critical role in energy homeostasis and regulation of metabolism. AT expands in response to chronic overnutrition or aging and becomes a major source of inflammation that has marked influence on systemic metabolism. The chronic, sterile inflammation that occurs in the AT during the development of obesity or in aging contributes to onset of devastating diseases such as insulin resistance, diabetes, and cardiovascular pathologies. Numerous studies have shown that inflammation in the visceral AT of humans and animals is a critical trigger for the development of metabolic syndrome. This work underscores the well-supported conclusion that the inflammatory immune response and metabolic pathways in the AT are tightly interwoven by multiple layers of relatively conserved mechanisms. During the development of diet-induced obesity or age-associated adiposity, cells of the innate and the adaptive immune systems infiltrate and proliferate in the AT. Macrophages, which dominate AT-associated immune cells in mouse models of obesity, but are less dominant in obese people, have been studied extensively. However, cells of the adaptive immune system, including T cells and B cells, contribute significantly to AT inflammation, perhaps more in humans than in mice. Lymphocytes regulate recruitment of innate immune cells into AT, and produce cytokines that influence the helpful-to-harmful inflammatory balance that, in turn, regulates organismal metabolism. This review describes inflammation, or more precisely, metabolic inflammation (metaflammation) with an eye toward the AT and the roles lymphocytes play in regulation of systemic metabolism during obesity and aging. © 2017 American Physiological Society. Compr Physiol 7:1307-1337, 2017.

Metabolomics, Nutrition, and Potential Biomarkers of Food Quality, Intake, and Health Status

Diet, dietary patterns, and other environmental factors such as exposure to toxins are playing an important role in the prevention/development of many diseases, like obesity, type 2 diabetes, and consequently on the health status of individuals. A major challenge nowadays is to identify novel biomarkers to detect as early as possible metabolic dysfunction and to predict evolution of health status in order to refine nutritional advices to specific population groups. Omics technologies such as genomics, transcriptomics, proteomics, and metabolomics coupled with statistical and bioinformatics tools have already shown great potential in this research field even if so far only few biomarkers have been validated. For the past two decades, important analytical techniques have been developed to detect as many metabolites as possible in human biofluids such as urine, blood, and saliva. In the field of food science and nutrition, many studies have been carried out for food authenticity, quality, and safety, as well as for food processing. Furthermore, metabolomic investigations have been carried out to discover new early biomarkers of metabolic dysfunction and predictive biomarkers of developing pathologies (obesity, metabolic syndrome, type-2 diabetes, etc.). Great emphasis is also placed in the development of methodologies to identify and validate biomarkers of nutrients exposure.

Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery

Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single "super"-list reflective of the overall preference or importance within the population. This "super"-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable.

A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes

Obesity and diabetes arise from an intricate interplay between both genetic and environmental factors. It is well recognized that obesity plays an important role in the development of insulin resistance and diabetes. Yet, the exact mechanism of the connection between obesity and diabetes is still not completely understood. Metabolomics is an analytical approach that aims to detect and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics to the identification of disease biomarkers, with a number of well-known biomarkers identified. Metabolomics is a potent approach to unravel the intricate relationships between metabolism, obesity and progression to diabetes and, at the same time, has potential as a clinical tool for risk evaluation and monitoring of disease. Moreover, metabolomics applications have revealed alterations in the levels of metabolites related to obesity-associated diabetes. This review focuses on the part that metabolomics has played in elucidating the roles of metabolites in the regulation of systemic metabolism relevant to obesity and diabetes. It also explains the possible metabolic relation and association between the two diseases. The metabolites with altered profiles in individual disorders and those that are specifically and similarly altered in both disorders are classified, categorized and summarized.

Metabolomics: a tool for the diagnosis of GH deficiency and for monitoring GH replacement?

The diagnostic value of insulin-like growth factor 1 (IGF1) for GH deficiency (GHD) in adults is not optimal. Molecular profiling could be used for biomarker discovery. The aim of this pilot study was to compare the serum metabolome between GHD patients and healthy controls, and identification of potential markers for diagnosis and/or for individual GH dosing. A total of ten patients with GHD, median age of 55 years and BMI of 27 kg/m(2), were compared with ten healthy age- and gender-matched controls. The serum metabolic profiles were generated using gas chromatography-coupled mass spectroscopy on fasting samples taken in the morning from the controls and at baseline and during 6 months of GH replacement in the patients with GHD. The difference in low-molecular weight compounds (LMC) distinguished the healthy controls from GHD patients. Among 285 measured metabolites, 13 were identified as being most important in differentiating GHD patients from controls. Of these, 11 could not be structurally annotated but many were classified as lipids. The difference in the LMC pattern persisted despite normalisation of IGF1 following GH replacement. GH replacement increased the levels of specific fatty acid compounds and decreased the levels of certain amino acids. No metabolite changed in response to GH treatment, to the same extent as IGF1. The measurement of 285 metabolites resulted in a unique pattern in GHD, but changes in the metabolite patterns during GH treatment were limited. The utility of metabolomics to find new markers in GHD and GH replacement remains to be further elucidated.

Metabolomic biomarkers for obesity in humans: a short review

BACKGROUND

The prevalence and incidence of obesity have become a major public health problem during the last decades, but the underlying biochemical and metabolic processes are not fully understood. Metabolomics, the science of small molecules of the metabolism, is helping to unravel these mechanisms via the identification of markers related to obesity. These biomarkers are used to prevent diseases in later life or for the early diagnosis of diseases. This review focuses on articles dealing with biomarkers for obesity.

KEY MESSAGES

Branched-chain amino acids (BCAA), nonesterified fatty acids, organic acids, acylcarnitines, and phospholipids were identified as potential biomarkers for obesity. This indicates a relation between elevated BCAA, and other amino acids, and the obese state. Furthermore, deregulation of β-oxidation is associated with the development of obesity. The results have several limitations, including the differing ages of the subjects in the studies, the fact that all of the studies had a case-control design and therefore no causal explanatory power, and that most looked for similar metabolites and reported almost equal results.

CONCLUSION

The strength of this review is that it gives a comprehensive overview of the current status of the knowledge on metabolomics biomarkers for obesity, but further research is needed because the methods used in the studies to date are very homogenous, e.g. most used a targeted approach and therefore analyzed almost the same group of metabolites. Moreover, prospective studies are lacking since all of the studies are either case-control or cross-sectional studies.

Clinical metabolomics and nutrition: the new frontier in neonatology and pediatrics

In the pediatric clinic, nutritional research is focusing more and more on preventing the development of long-term diseases as well as supporting the repair processes important in the therapy of already fully developed diseases. Most children who are hospitalized or affected by chronic diseases could benefit from specific and careful attention to nutrition. Indeed, the state of nutrition modulates all body functions, including the different metabolic processes which, all together, have a profound effect on the development of the health and future of all individuals. Inappropriate food, even in the first periods of life, can accelerate the development of chronic metabolic diseases, especially in the pediatric age. To gain further insights into metabolic cycles and how they are connected with diet and health, nutrition and metabolomics interact to develop and apply modern technologies for metabolic assessment. In particular, nutritionists are evaluating the metabolomic approach to establish the single nutritional phenotypes, that is, the way in which diet interacts with individuals' metabolisms. This strategy offers the possibility of providing a complete definition of the individual's nutritional and health status, predict the risk of disease, and create metabolomic databases supporting the development of “personalized nutrition,” in which diet is attuned to the nutritional needs of individual patients.

Plasma metabolomics reveals a potential panel of biomarkers for early diagnosis in acute coronary syndrome

Discovery of new biomarkers is critical for early diagnosis of acute coronary syndrome (ACS). Recent advances in metabolomic technologies have drastically enhanced the possibility of improving the knowledge of its physiopathology through the identification of the altered metabolic pathways. In this study, analyses of peripheral plasma from non-ST segment elevation ACS patients and healthy controls by gas chromatography-mass spectrometry (GC-MC) permitted the identification of 15 metabolites with statistical differences (p < 0.05) between experimental groups. Additionally, validation by GC-MC and liquid chromatography-MC permitted us to identify a potential panel of biomarkers formed by 5-OH-tryptophan, 2-OH-butyric acid and 3-OH-butyric acid. This panel of biomarkers reflects the oxidative stress and the hypoxic state that suffers the myocardial cells and consequently constitutes a metabolomic signature of the atherogenesis process that could be used for early diagnosis of ACS.

Selective of informative metabolites using random forests based on model population analysis

One of the main goals of metabolomics studies is to discover informative metabolites or biomarkers, which may be used to diagnose diseases and to find out pathology. Sophisticated feature selection approaches are required to extract the information hidden in such complex 'omics' data. In this study, it is proposed a new and robust selective method by combining random forests (RF) with model population analysis (MPA), for selecting informative metabolites from three metabolomic datasets. According to the contribution to the classification accuracy, the metabolites were classified into three kinds: informative, no-informative, and interfering metabolites. Based on the proposed method, some informative metabolites were selected for three datasets; further analyses of these metabolites between healthy and diseased groups were then performed, showing by T-test that the P values for all these selected metabolites were lower than 0.05. Moreover, the informative metabolites identified by the current method were demonstrated to be correlated with the clinical outcome under investigation. The source codes of MPA-RF in Matlab can be freely downloaded from http://code.google.com/p/my-research-list/downloads/list.

Metabolomic profiling of amino acids and β-cell function relative to insulin sensitivity in youth

CONTEXT

In longitudinal studies of adults, elevated amino acid (AA) concentrations predicted future type 2 diabetes mellitus (T2DM).

OBJECTIVE

The aim of the present investigation was to examine whether increased plasma AA concentrations are associated with impaired β-cell function relative to insulin sensitivity [i.e. disposition index (DI)], a predictor of T2DM development.

DESIGN, SETTING, AND PARTICIPANTS

Metabolomic analysis for fasting plasma AAs was performed by tandem mass spectrometry in 139 normal-weight and obese adolescents with and without dysglycemia. First-phase insulin secretion was evaluated by a hyperglycemic (∼225 mg/dl) clamp and insulin sensitivity by a hyperinsulinemic-euglycemic clamp. DI was calculated as the product of first-phase insulin and insulin sensitivity.

RESULTS

DI was positively associated with branched-chain AAs (leucine/isoleucine and valine; r = 0.27 and 0.29, P = 0.001), neutrally transported AAs (phenylalanine and methionine; r = 0.30 and 0.35, P < 0.001), basic AAs (histidine and arginine; r = 0.28 and 0.23, P ≤ 0.007), serine (r = 0.35, P < 0.001), glycine (r = 0.26, P = 0.002), and branched-chain AAs-derived intermediates C3, C4, and C5 acylcarnitine (range r = 0.18-0.19, P ≤ 0.04).

CONCLUSION

In youth, increased plasma AA concentrations are not associated with a heightened metabolic risk profile for T2DM; rather, they are positively associated with β-cell function relative to insulin sensitivity. These contrasting observations between adults and youth may be a reflection of developmental differences along the lifespan dependent on the combined impact of the aging process together with the impact of progressive obesity.

Plants' metabolites as potential antiobesity agents

Obesity and obesity-related complications are on the increase both in the developed and developing world. Since existing pharmaceuticals fail to come up with long-term solutions to address this issue, there is an ever-pressing need to find and develop new drugs and alternatives. Natural products, particularly medicinal plants, are believed to harbor potential antiobesity agents that can act through various mechanisms either by preventing weight gain or promoting weight loss amongst others. The inhibition of key lipid and carbohydrate hydrolyzing and metabolizing enzymes, disruption of adipogenesis, and modulation of its factors or appetite suppression are some of the plethora of targeted approaches to probe the antiobesity potential of medicinal plants. A new technology such as metabolomics, which deals with the study of the whole metabolome, has been identified to be a promising technique to probe the progression of diseases, elucidate their pathologies, and assess the effects of natural health products on certain pathological conditions. This has been applied to drug research, bone health, and to a limited extent to obesity research. This paper thus endeavors to give an overview of those plants, which have been reported to have antiobesity effects and highlight the potential and relevance of metabolomics in obesity research.

Morphological and functional vascular changes induced by childhood obesity

BACKGROUND

To investigate endothelial dysfunction and morphological vascular changes in childhood obesity.

METHODS

93 overweight/obese children (body mass index 26 ± 5 kg/m(2); median 26 kg/m(2); interquartile range 22-28 kg/m(2)), mean age 10.9 ± 2.7 years, underwent a check-up of total, high-density lipoprotein- and low-density lipoprotein-cholesterol, triglycerides, C-reactive protein, erythrocyte sedimentation rate, and white blood cell count, together with ultrasound measures of flow-mediated dilatation, carotid intima-media thickness, and anterior-posterior diameter of the abdominal aorta.

RESULTS

The body mass index of overweight/obese children had a statistically significant linear relationship (p < 0.05) with triglycerides, erythrocyte sedimentation rate, carotid intima-media thickness, anterior-posterior diameter of the abdominal aorta, and flow-mediated dilatation values.

CONCLUSIONS

Overweight/obese children have an initial endothelial dysfunction and vascular damage, i.e., the first stage in the development of atherosclerosis.

Emerging perspectives on essential amino acid metabolism in obesity and the insulin-resistant state

Dysregulation of insulin action is most often considered in the context of impaired glucose homeostasis, with the defining feature of diabetes mellitus being elevated blood glucose concentration. Complications arising from the hyperglycemia accompanying frank diabetes are well known and epidemiological studies point to higher risk toward development of metabolic disease in persons with impaired glucose tolerance. Although the central role of proper blood sugar control in maintaining metabolic health is well established, recent developments have begun to shed light on associations between compromised insulin action [obesity, prediabetes, and type 2 diabetes mellitus (T2DM)] and altered intermediary metabolism of fats and amino acids. For amino acids, changes in blood concentrations of select essential amino acids and their derivatives, in particular BCAA, sulfur amino acids, tyrosine, and phenylalanine, are apparent with obesity and insulin resistance, often before the onset of clinically diagnosed T2DM. This review provides an overview of these changes and places recent observations from metabolomics research into the context of historical reports in the areas of biochemistry and nutritional biology. Based on this synthesis, a model is proposed that links the FFA-rich environment of obesity/insulin resistance and T2DM with diminution of BCAA catabolic enzyme activity, changes in methionine oxidation and cysteine/cystine generation, and tissue redox balance (NADH/NAD+).

Practical non-targeted gas chromatography/mass spectrometry-based metabolomics platform for metabolic phenotype analysis

Gas chromatography coupled to mass spectrometry (GC/MS) is a core analytical method for metabolomics and has been used as a platform in non-targeted analysis, especially for hydrophilic metabolites. Non-targeted GC/MS-based metabolomics generally requires a high-throughput technology to handle a large volume of samples and an accumulated database (reference library) of the retention times and mass spectra of standard compounds for accurate peak identification. In this study, we provide a practical GC/MS platform and an auto peak identification technique that is not restricted to certain types of mass spectrometers. The platform utilizes a quadrupole mass spectrometer capable of high-speed scanning, resulting in greater output compared with Pegasus GC-time of flight (TOF)/MS, which has been an essential instrument for high-throughput experiments. Moreover, we show that our reference library is broadly applicable to other instruments; peak identification can be readily performed using the library without constructing a reference resource. The usefulness and versatility of our system are demonstrated by the analyses of three experimental metabolomics data sets, including standard mixtures and real biological samples.

A metabolic profiling strategy for biomarker screening by GC-MS combined with multivariate resolution method and Monte Carlo

A GC-MS based metabolic profiling method via a multivariate resolution method and Monte Carlo PLS-DA is proposed for screening potential biomarkers, and applied to Type 2 diabetes mellitus. The metabolic profiles of plasma samples from healthy control and Type 2 diabetes mellitus patient groups were obtained by GC-MS, and 25 compounds considered as endogenous metabolites excluding glucose were identified. With the help of a multivariate resolution method, qualitative and quantitative results of the metabolic profiles were extracted for subsequent multivariate statistical analysis. In order to select potential biomarkers, responsible for the classification of the two groups, Monte Carlo PLS-DA was introduced. The distribution of the regression coefficients of PLS-DA models corresponding to the metabolites was obtained. The levels of metabolites with all positive coefficients were considered as decreased from healthy controls to patients, and all negative coefficients were considered as increased. Univariate t-test was employed to check for metabolites whose levels changed significantly. Metabolites identified as potential biomarkers of Type 2 diabetes mellitus were ten in total, namely lactate, alanine, α-hydroxyisobutyric acid, phosphate, serine, pyroglutamic acid, palmitic acid, stearic acid, 1-monopalmitin and cholesterol. Finally, canonical correlation analysis was used to explore the correlation between the selected ten metabolites and blood glucose, which was considered to be a routine parameter reflecting the disease state. The results showed that the ten selected metabolites correlated well with blood glucose (r = 0.81, p = 0.03), and may be considered as possible biomarkers of Type 2 diabetes mellitus. The results demonstrated that the proposed method may be a useful tool to discover potential biomarkers of diseases.