Metabolomics in diabetes research

Associations between metals and metabolomic profiles related to diabetes among adults in a rural region

INTRODUCTION

Exposure to metals is associated with increased risk of type 2 diabetes (T2D). Potential mechanisms for metals-T2D associations involve biological processes including oxidative stress and disruption of insulin-regulated glucose uptake. In this study, we assessed whether associations between metal exposure and metabolite profiles relate to biological pathways linked to T2D.

MATERIALS AND METHODS

We used data from 29 adults rural Colorado residents enrolled in the San Luis Valley Diabetes Study. Urinary concentrations of arsenic, cadmium, cobalt, lead, manganese, and tungsten were measured. Metabolic effects were evaluated using untargeted metabolic profiling, which included 61,851 metabolite signals detected in serum. We evaluated cross-sectional associations between metals and metabolites present in at least 50% of samples. Primary analyses adjusted urinary heavy metal concentrations for creatinine. Metabolite outcomes associated with each metal exposure were evaluated using pathway enrichment to investigate potential mechanisms underlying the relationship between metals and T2D.

RESULTS

Participants had a mean age of 58.5 years (standard deviation = 9.2), 48.3% were female, 48.3% identified as Hispanic/Latino, 13.8% were current smokers, and 65.5% had T2D. Of the detected metabolites, 455 were associated with at least one metal, including 42 associated with arsenic, 22 with cadmium, 10 with cobalt, 313 with lead, 66 with manganese, and two with tungsten. The metabolic features were linked to 24 pathways including linoleate metabolism, butanoate metabolism, and arginine and proline metabolism. Several of these pathways have been previously associated with T2D, and our results were similar when including only participants with T2D.

CONCLUSIONS

Our results support the hypothesis that metals exposure may be associated with biological processes related to T2D, including amino acid, co-enzyme, and sugar and fatty acid metabolism. Insight into biological pathways could influence interventions to prevent adverse health outcomes due to metal exposure.

Benchtop NMR-Based Metabolomics: First Steps for Biomedical Application

Nuclear magnetic resonance (NMR)-based metabolomics is a valuable tool for identifying biomarkers and understanding the underlying metabolic changes associated with various diseases. However, the translation of metabolomics analysis to clinical practice has been limited by the high cost and large size of traditional high-resolution NMR spectrometers. Benchtop NMR, a compact and low-cost alternative, offers the potential to overcome these limitations and facilitate the wider use of NMR-based metabolomics in clinical settings. This review summarizes the current state of benchtop NMR for clinical applications where benchtop NMR has demonstrated the ability to reproducibly detect changes in metabolite levels associated with diseases such as type 2 diabetes and tuberculosis. Benchtop NMR has been used to identify metabolic biomarkers in a range of biofluids, including urine, blood plasma and saliva. However, further research is needed to optimize the use of benchtop NMR for clinical applications and to identify additional biomarkers that can be used to monitor and manage a range of diseases. Overall, benchtop NMR has the potential to revolutionize the way metabolomics is used in clinical practice, providing a more accessible and cost-effective way to study metabolism and identify biomarkers for disease diagnosis, prognosis, and treatment.

Postmortem Metabolomics of Insulin Intoxications and the Potential Application to Find Hypoglycemia-Related Deaths

Postmortem metabolomics can assist death investigations by characterizing metabolic fingerprints differentiating causes of death. Hypoglycemia-related deaths, including insulin intoxications, are difficult to identify and, thus, presumably underdiagnosed. This investigation aims to differentiate insulin intoxication deaths by metabolomics, and identify a metabolic fingerprint to screen for unknown hypoglycemia-related deaths. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry data were obtained from 19 insulin intoxications (hypo), 19 diabetic comas (hyper), and 38 hangings (control). Screening for potentially unknown hypoglycemia-related deaths was performed using 776 random postmortem cases. Data were processed using XCMS and SIMCA. Multivariate modeling revealed group separations between hypo, hyper, and control groups. A metabolic fingerprint for the hypo group was identified, and analyses revealed significant decreases in 12 acylcarnitines, including nine hydroxylated-acylcarnitines. Screening of random postmortem cases identified 46 cases (5.9%) as potentially hypoglycemia-related, including six with unknown causes of death. Autopsy report review revealed plausible hypoglycemia-cause for five unknown cases. Additionally, two diabetic cases were found, with a metformin intoxication and a suspicious but unverified insulin intoxication, respectively. Further studies are required to expand on the potential of postmortem metabolomics as a tool in hypoglycemia-related death investigations, and the future application of screening for potential insulin intoxications.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Perspectives from metabolomics in the early diagnosis and prognosis of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnant women. The early detection of GDM provides an opportunity for the effective treatment of hyperglycemia in pregnancy, thus decreasing the risk of adverse perinatal outcomes for mothers and newborns. Metabolomics, an emerging technique, offers a novel point of view in understanding the onset and development of diseases and has been repeatedly used in various gestational periods in recent studies of GDM. Moreover, metabolomics provides varied opportunities in the different diagnoses of GDM from prediabetes or predisposition to diabetes, the diagnosis of GDM at a gestational age several weeks earlier than that used in the traditional method, and the assessment of prognosis considering the physiologic subtypes of GDM and clinical indexes. Longitudinal metabolomics truly facilitates the dynamic monitoring of metabolic alterations over the course of pregnancy. Herein, we review recent advancements in metabolomics and summarize evidence from studies on the application of metabolomics in GDM, highlighting the aspects of the diagnosis and differential diagnoses of GDM in an early stage. We also discuss future study directions concerning the physiologic subtypes, prognosis, and limitations of metabolomics.

The trend in application of omics in type 2 diabetes researches; A bibliometric study

AIMS

Due to the importance of omics approaches in diabetes diagnosis, we were assumed to study the scientific activities on omics and type 2 diabetes worldwide.

METHOD

Bibliometric approach was utilized to evaluate the documents on proteomics, lipidomics, and metabolomics in patients with type 2 diabetes in the Scopus database from the beginning to 2020. The articles were screened by two reviewers and the number of publications and citations on omics and type 2 diabetes, top-ranked journals, top-cited articles, country co-contributions, co-authorships, author keywords, and terms were analyzed.

RESULTS

The scientific publications in this field consisted of 551 original articles, of which the USA shares the most percent, followed by China and Germany. The frequent keywords showed that the following hotspots were of interest: "Metabolomics, proteomics, and lipidomics as biomarkers for diabetes", "Omics and diabetic nephropathy", "The application of omics in obesity, insulin resistance, and type 2 diabetes".

CONCLUSION

This study showed an increasing trend in applying omics in type 2 diabetes researches and determined the leading producers in this field. Besides, the research hotspots and the main subjects of documents were provided for future research and policy decision-making.

Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach

The study of metabolomics has improved our knowledge of the biology behind type 2 diabetes and its related metabolic physiology. We aimed to investigate markers of adipose tissue morphology, as well as insulin and glucose metabolism in 53 non-obese male individuals. The participants underwent extensive clinical, biochemical and magnetic resonance imaging phenotyping, and we also investigated non-targeted serum metabolites. We used a multi-modal machine learning approach to evaluate which serum metabolomic compounds predicted markers of glucose and insulin metabolism, adipose tissue morphology and distribution. Fasting glucose was associated with metabolites of intracellular insulin action and beta-cell dysfunction, namely cysteine-s-sulphate and n-acetylgarginine, whereas fasting insulin was predicted by myristoleoylcarnitine, propionylcarnitine and other metabolites of beta-oxidation of fatty acids. OGTT-glucose levels at 30 min were predicted by 7-Hoca, a microbiota derived metabolite, as well as eugenol, a fatty acid. Both insulin clamp and HOMA-IR were predicted by metabolites involved in beta-oxidation of fatty acids and biodegradation of triacylglycerol, namely tartrate and 3-phosphoglycerate, as well as pyruvate, xanthine and liver fat. OGTT glucose area under curve (AUC) and OGTT insulin AUC, was associated with bile acid metabolites, subcutaneous adipocyte cell size, liver fat and fatty chain acids and derivates, such as isovalerylcarnitine. Finally, subcutaneous adipocyte size was associated with long chain fatty acids, markers of sphingolipid metabolism, increasing liver fat and dopamine-sulfate 1. Ectopic liver fat was predicted by methylmalonate, adipocyte cell size, glutathione derived metabolites and fatty chain acids. Ectopic heart fat was predicted visceral fat, gamma-glutamyl tyrosine and 2-acetamidophenol sulfate. Adipocyte cell size, age, alpha-tocopherol and blood pressure were associated with visceral fat. We identified several biomarkers associated with adipose tissue pathophysiology and insulin and glucose metabolism using a multi-modal machine learning approach. Our approach demonstrated the relative importance of serum metabolites and they outperformed traditional clinical and biochemical variables for most endpoints.

Metabolomic Biomarkers in Gestational Diabetes Mellitus: A Review of the Evidence

Gestational diabetes mellitus (GDM) is the fastest growing type of diabetes, affecting between 2 to 38% of pregnancies worldwide, varying considerably depending on diagnostic criteria used and sample population studied. Adverse obstetric outcomes include an increased risk of macrosomia, and higher rates of stillbirth, instrumental delivery, and birth trauma. Metabolomics, which is a platform used to analyse and characterise a large number of metabolites, is increasingly used to explore the pathophysiology of cardiometabolic conditions such as GDM. This review aims to summarise metabolomics studies in GDM (from inception to January 2021) in order to highlight prospective biomarkers for diagnosis, and to better understand the dysfunctional metabolic pathways underlying the condition. We found that the most commonly deranged pathways in GDM include amino acids (glutathione, alanine, valine, and serine), carbohydrates (2-hydroxybutyrate and 1,5-anhydroglucitol), and lipids (phosphatidylcholines and lysophosphatidylcholines). We also highlight the possibility of using certain metabolites as predictive markers for developing GDM, with the use of highly stratified modelling techniques. Limitations for metabolomic research are evaluated, and future directions for the field are suggested to aid in the integration of these findings into clinical practice.

Combined effects of arsenic and palmitic acid on oxidative stress and lipid metabolism disorder in human hepatoma HepG2 cells

The toxicity of arsenic (As) can be influenced by many nutrients in food. However, the combined effects and underlying mechanisms of As and palmitic acid (PA) are still unclear. Here, cell viability, oxidative stress, lipids accumulation, gene expression profiles, and metabolome profiles of human hepatoma HepG2 cells exposed to As, PA, and As + PA were analyzed and compared. Results showed that co-exposure of 100 μM PA and 2 μM As induced lower cell viability, higher intracellular reactive oxygen species level, more lipid droplet accumulation, and more intracellular triglyceride contents than As alone or PA alone exposure. High-throughput quantitative PCR and ¹H NMR-based metabolomics analysis showed that co-exposure of As and PA caused all toxic effects on gene expression and metabolome profiles induced by As alone or PA alone exposure, and showed higher toxicities. Gene expression profiles in the As + PA group had higher similarity with those in the As group than the PA group. However, PA played a more important role in metabolism disorder than As in their interactive effects. Oxidative stress and lipid metabolism disorder were found to be the main toxic effects in the As + PA group. Several differentially expressed genes (such as OXR1, OXSR1, INSR, and PPARA) and changed metabolites (such as pyruvate, acetate, and L-phenylalanine) were involved in the combined toxicity of As and PA. This study provides basic information on the interactive effects of As and PA, which is useful for the health risk assessment of As and FFA.

The Effect of Exogenous Free Nε-(Carboxymethyl)Lysine on Diabetic-Model Goto-Kakizaki Rats: Metabolomics Analysis in Serum and Urine

The current study investigated the effects of exogenous free N^ε-(carboxymethyl) lysine (CML) from daily diet on diabetic-model Goto-Kakizaki rats. Rats were fed with free CML (2 mg/kg body weight) for 8 weeks, then metabolomics evaluation was performed on serum and urine, and biochemical and histopathologic examinations were conducted to verify metabolic results. Diabetic rats fed with free CML showed significantly increased (P < 0.05) fasting blood glucose (11.1 ± 1.07 mmol/L) and homeostasis model assessment values (homeostatic model assessment of insulin resistance: 16.0 ± 4.24; homeostatic model assessment of beta cell function: 6.66 ± 2.01; and modified beta cell function index: 11.5 ± 2.66) and a significantly altered (P < 0.05) oxidative stress level when compared to the control group. Serum and urine metabolomics showed a significantly altered (P < 0.05) level of aminomalonic acid, 2-oxoadipic acid, l-malic acid, β-alanine, 2-oxoglutaric acid, d-threitol, N-acetyl-leucine, methylmalonic acid, l-cysteine, thymine, glycine, l-alanine, 4-hydroxyproline, hexadecane, succinic acid, l-ornithine, gluconolactone, maleic acid, l-lactate, tryptophan, 5-methoxyindoleacetate, γ-aminobutyric acid, homoserine, maltose, and quinolinic acid. Our results indicated that these metabolites altered by exposure to exogenous free CML were mapped to the citric acid cycle and amino acid and carbohydrate metabolism, which might be related to increased progression of diabetes and some other diabetic complications, including diabetic brain and neurological diseases, retinopathy, nephropathy, and impaired wound healing.

Non-negative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine

Because of its quantitative character and capability for high-throughput screening, ¹H nuclear magnetic resonance (NMR) spectroscopy is used extensively in the profiling of biofluids such as urine and blood plasma. However, the narrow frequency bandwidth of ¹H NMR spectroscopy leads to a severe overlap of the spectra of components present in the complex mixtures such as biofluids. Therefore, ¹H NMR-based metabolomics analysis is focused on targeted studies related to concentrations of the small number of metabolites. Here, we propose a library-based approach to quantify proportions of overlapping metabolites from ¹H NMR mixture spectra. The method boils down to the linear non-negative least squares (NNLS) problem, whereas proportions of the pure components contained in the library stand for the unknowns. The method is validated on an estimation of the proportions of (i) the 78 pure spectra, presumably related to type 2 diabetes mellitus (T2DM), from their synthetic linear mixture; (ii) metabolites present in 62 ¹H NMR spectra of urine of subjects with T2DM and 62 ¹H NMR spectra of urine of control subjects. In both cases, the in-house library of 210 pure component ¹H NMR spectra represented the design matrix in the related NNLS problem. The proposed method pinpoints 63 metabolites that in a statistically significant way discriminate the T2DM group from the control group and 46 metabolites discriminating control from the T2DM group. For several T2DM-discriminative metabolites, we prove their presence by independent analytical determination or by pointing out the corresponding findings in the published literature.

1H NMR serum metabolomic profiling of patients at risk of cardiovascular diseases performing stress test

Cardiovascular diseases are the leading cause of death worldwide. Changes in lifestyle and/or pharmacological treatment are able to reduce the burden of coronary artery diseases (CAD) and early diagnosis is crucial for the timely and optimal management of the disease. Stress testing is a good method to measure the burden of CAD but it is time consuming and pharmacological testing may not fully mimic exercise test. The objectives of the present project were to characterize the metabolic profile of the population undergoing pharmacological and exercise stress testing to evaluate possible differences between them, and to assess the capacity of ¹H NMR spectroscopy to predict positive stress testing. Pattern recognition was applied to ¹H NMR spectra from serum of patients undergoing stress test and metabolites were quantified. The effects of the stress test, confounding variables and the ability to predict ischemia were evaluated using OPLS-DA. There was an increase in lactate and alanine concentrations in post-test samples in patients undergoing exercise test, but not in those submitted to pharmacological testing. However, when considering only pharmacological patients, those with a positive test result, showed increased serum lactate, that was masked by the much larger amount of lactate associated to exercise testing. In conclusion, we have established that pharmacological stress test does not reproduce the dynamic changes observed in exercise stress. Although there is promising evidence suggesting that ¹H NMR based metabolomics could predict stress test results, further studies with much larger populations will be required in order to obtain a definitive answer.

Metabolomics in chronic pain research

BACKGROUND AND OBJECTIVE

Metabolomics deals with the identification and quantification of small molecules (metabolites) in biological samples. As metabolite levels can reflect normal or altered metabolic pathways, their measurement provides information to improve the understanding, diagnosis and management of diseases. Despite its immense potential, metabolomics applications to pain research have been sparse. This paper describes current metabolomics techniques, reviews published human metabolomics pain research and compares successful metabolomics research in other areas of medicine with the goal of highlighting opportunities offered by metabolomics to advance pain medicine.

DATABASES AND DATA TREATMENT

Non-systematic review.

RESULTS

Our search identified 19 studies that adopted a metabolomics approach in: fibromyalgia (7), chronic widespread pain (4), other musculoskeletal pain conditions (5), neuropathic pain (1), complex regional pain syndrome (1) and pelvic pain (1). The studies used either mass spectrometry or nuclear magnetic resonance. Most are characterized by small sample sizes. Some consistency has been found for alterations in glutamate and testosterone metabolism, and metabolic imbalances caused by the gut microbiome.

CONCLUSIONS

Metabolomics research in chronic pain is in its infancy. Most studies are at the pilot stage. Metabolomics research has been successful in other areas of medicine. These achievements should motivate investigators to expand metabolomics research to improve the understanding of the basic mechanisms of human pain, as well as provide tools to diagnose, predict and monitor chronic pain conditions. Metabolomics research can lead to the identification of biomarkers to support the development and testing of treatments, thereby facilitating personalized pain medicine.

Novel Pharmacological Targets for Combat PTSD-Metabolism, Inflammation, The Gut Microbiome, and Mitochondrial Dysfunction

INTRODUCTION

Current pharmacological treatments of post-traumatic stress disorder (PTSD) have limited efficacy. Although the diagnosis is based on psychopathological criteria, it is frequently accompanied by somatic comorbidities and perhaps "accelerated biological aging," suggesting widespread physical concomitants. Such physiological comorbidities may affect core PTSD symptoms but are rarely the focus of therapeutic trials.

METHODS

To elucidate the potential involvement of metabolism, inflammation, and mitochondrial function in PTSD, we integrate findings and mechanistic models from the DOD-sponsored "Systems Biology of PTSD Study" with previous data on these topics.

RESULTS

Data implicate inter-linked dysregulations in metabolism, inflammation, mitochondrial function, and perhaps the gut microbiome in PTSD. Several inadequately tested targets of pharmacological intervention are proposed, including insulin sensitizers, lipid regulators, anti-inflammatories, and mitochondrial biogenesis modulators.

CONCLUSIONS

Systemic pathologies that are intricately involved in brain functioning and behavior may not only contribute to somatic comorbidities in PTSD, but may represent novel targets for treating core psychiatric symptoms.

The effects of high-fat diet and metformin on urinary metabolites in diabetes and prediabetes rat models

High-fat diet (HFD) interferes with the dietary plan of patients with type 2 diabetes mellitus (T2DM). However, many diabetes patients consume food with higher fat content for a better taste bud experience. In this study, we examined the effect of HFD on rats at the early onset of diabetes and prediabetes by supplementing their feed with palm olein oil to provide a fat content representing 39% of total calorie intake. Urinary profile generated from liquid chromatography-mass spectrometry analysis was used to construct the orthogonal partial least squares discriminant analysis (OPLS-DA) score plots. The data provide insights into the physiological state of an organism. Healthy rats fed with normal chow (NC) and HFD cannot be distinguished by their urinary metabolite profiles, whereas diabetic and prediabetic rats showed a clear separation in OPLS-DA profile between the two diets, indicating a change in their physiological state. Metformin treatment altered the metabolomics profiles of diabetic rats and lowered their blood sugar levels. For prediabetic rats, metformin treatment on both NC- and HFD-fed rats not only reduced their blood sugar levels to normal but also altered the urinary metabolite profile to be more like healthy rats. The use of metformin is therefore beneficial at the prediabetes stage.

Exploring the Diversity of Sugar Compounds in Healthy, Prediabetic, and Diabetic Volunteers

SCOPE

Diabetes is thought to primarily represent a disturbance of carbohydrate metabolism; however, population studies employing metabolomics have mainly identified plasma amino acids and lipids, or their products, as biomarkers. In this pilot study, the aim is to analyze a wide spectrum of sugar compounds in the fasting state and during an oral glucose tolerance test (OGTT) in healthy, prediabetic, and type 2 diabetic volunteers.

METHODS AND RESULTS

The three volunteer groups underwent a standard OGTT. Plasma samples obtained in the fasting state, 30 and 90 min after the OGTT, are subjected to a semitargeted GC-MS (gas chromatography-mass spectrometry) sugar profiling. Overall, 40 sugars are detected in plasma, of which some are yet unknown to change during an OGTT. Several sugars (e.g., trehalose) reveal significant differences between the volunteer groups both in fasting plasma and in distinct time courses after the OGTT. This suggests an endogenous production from orally absorbed glucose and/or an insulin-dependent production/removal from plasma.

CONCLUSION

It is demonstrated that more sugars than expected can be found in human plasma. Since some of these show characteristic differences depending on health status, it may be worthwhile to assess their usability as biomarkers for diagnosing early-stage insulin resistance and type 2 diabetes.

Swimming Impacts on Pancreatic Inflammatory Cytokines, miR-146a and NF-кB Expression Levels in Type-2 Diabetic Rats

BACKGROUND

Obesity-induced chronic inflammation is a key component in the pathogenesis of insulin resistance and type-2 diabetes Objective: This study aimed to evaluate the effect of swimming exercise on pancreatic expression levels of inflammatory cytokines, miR-146a and NF-кB in type-2 diabetic male rats.

METHODS

Twenty- eight male Wistar rats were divided into four groups: control (Con), exercise, diabetes and diabetic exercise (n = 7). Diabetes induction performed by the combination of high-fat diet (HFD, 4 weeks) and streptozotocin (35 mg/kg. ip). After induction of diabetes, the rats swam in the exercise groups for 12 weeks. Then, blood and tissue samples were collected.

RESULTS

Our results indicated a significant increase in expression levels of miR-146, NF-κB and inflammatory cytokines (IL-6, TNF-α, and IL-1β) while a significant decrease in pancreatic expression levels of TRAF6 and IRAK1 in diabetic group as compared to the control group. In contrast, swimming exercise resulted in a significant decrease in expression levels of miR-146a, NF-кB and inflammatory cytokines and a significant increase in expression levels of TRAF6 and IRAK1 in the exercise-diabetic group compared to the diabetic group.

CONCLUSION

Our results indicated a significant increase in expression levels of miR-146, NF-κB and inflammatory cytokines (IL-6, TNF-α, and IL-1β) while a significant decrease in pancreatic expression levels of TRAF6 and IRAK1 in diabetic group as compared to the control group. In contrast, swimming exercise resulted in a significant decrease in expression levels of miR-146a, NF-кB and inflammatory cytokines and a significant increase in expression levels of TRAF6 and IRAK1 in the exercise-diabetic group compared to the diabetic group.

Metabolomics-Based Prospective Studies and Prediction of Type 2 Diabetes Mellitus Risks

The preceding decade has witnessed an intense upsurge in the diabetic population across the world making type 2 diabetes mellitus (T2DM) more of an epidemic than a lifestyle disease. Metabolic disorders are often latent for a while before becoming clinically evident, thus reinforcing the pursuit of early biomarkers of metabolic alterations. A prospective study along with metabolic profiling is the most appropriate way to detect the early pathophysiological changes in metabolic diseases such as T2DM. The aim of this review was to summarize the different potential biomarkers of T2DM identified in prospective studies, which used tools of metabolomics. The review also demonstrates on how metabolomic profiling-based prospective studies can be used to address a concern like population-specific disease mechanism. We performed a literature search on metabolomics-based prospective studies on T2DM using the key words "metabolomics," "Type 2 diabetes," "diabetes mellitus", "metabolite profiling," "prospective study," "metabolism," and "biomarker." Additional articles that were obtained from the reference lists of the articles obtained using the above key words were also examined. Articles on dietary intake, type 1 diabetes mellitus, and gestational diabetes were excluded. The review revealed that many studies showed a direct association of branched-chain amino acids and an inverse association of glycine with T2DM. Majority of the prospective studies conducted were targeted metabolomics-based, with Caucasians as their study cohort. The whole disease risk in populations, including Asians, could therefore not be identified. This review proposes the utility of prospective studies in conjunction with metabolomics platform to unravel the altered metabolic pathways that contribute to the risk of T2DM.

The metabolomics side of frailty: Toward personalized medicine for the aged

Frailty encompasses several domains (i.e., metabolic, physical, cognitive). The multisystem derangements underlying frailty pathophysiology, its phenotypic heterogeneity, and the fluctuations of individuals across severity states have hampered a comprehensive appraisal of the condition. Circulating biomarkers emerged as an alleged tool for capturing this complexity and, as proxies for organismal metabolic changes, may hold the advantages of: 1) supporting diagnosis, 2) tracking the progression, 3) assisting healthcare professionals in clinical and therapeutic decision-making, and 4) verifying the efficacy of an intervention before measurable clinical manifestations occur. Among available analytical tools, metabolomics are able to identify and quantify the (ideally) whole repertoire of small molecules in biological matrices (i.e., cells, tissues, and biological fluids). Results of metabolomics analysis may define the final output of genome-environment interactions at the individual level. This entire collection of metabolites is called "metabolome" and is highly dynamic. Here, we discuss how monitoring the dynamics of metabolic profiles may provide a read-out of the environmental and clinical disturbances affecting cell homeostasis in frailty-associated conditions.

Early, Non-Invasive Sensing of Sustained Hyperglycemia in Mice Using Millimeter-Wave Spectroscopy

Diabetes is a very complex condition affecting millions of people around the world. Its occurrence, always accompanied by sustained hyperglycemia, leads to many medical complications that can be greatly mitigated when the disease is treated in its earliest stage. In this paper, a novel sensing approach for the early non-invasive detection and monitoring of sustained hyperglycemia is presented. The sensing principle is based on millimeter-wave transmission spectroscopy through the skin and subsequent statistical analysis of the amplitude data. A classifier based on functional principal components for sustained hyperglycemia prediction was validated on a sample of twelve mice, correctly classifying the condition in diabetic mice. Using the same classifier, sixteen mice with drug-induced diabetes were studied for two weeks. The proposed sensing approach was capable of assessing the glycemic states at different stages of induced diabetes, providing a clear transition from normoglycemia to hyperglycemia typically associated with diabetes. This is believed to be the first presentation of such evolution studies using non-invasive sensing. The results obtained indicate that gradual glycemic changes associated with diabetes can be accurately detected by non-invasively sensing the metabolism using a millimeter-wave spectral sensor, with an observed temporal resolution of around four days. This unprecedented detection speed and its non-invasive character could open new opportunities for the continuous control and monitoring of diabetics and the evaluation of response to treatments (including new therapies), enabling a much more appropriate control of the condition.

NMR-Based Metabolomic Approach Tracks Potential Serum Biomarkers of Disease Progression in Patients with Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by chronic hyperglycemia associated with alterations in carbohydrate, lipid, and protein metabolism. The prognosis of T2DM patients is highly dependent on the development of complications, and therefore the identification of biomarkers of T2DM progression, with minimally invasive techniques, is a huge need. In the present study, we applied a ¹H-Nuclear Magnetic Resonance (¹H-NMR)-based metabolomic approach coupled with multivariate data analysis to identify serum metabolite profiles associated with T2DM development and progression. To perform this, we compared the serum metabolome of non-diabetic subjects, treatment-naïve non-complicated T2DM patients, and T2DM patients with complications in insulin monotherapy. Our analysis revealed a significant reduction of alanine, glutamine, glutamate, leucine, lysine, methionine, tyrosine, and phenylalanine in T2DM patients with respect to non-diabetic subjects. Moreover, isoleucine, leucine, lysine, tyrosine, and valine levels distinguished complicated patients from patients without complications. Overall, the metabolic pathway analysis suggested that branched-chain amino acid (BCAA) metabolism is significantly compromised in T2DM patients with complications, while perturbation in the metabolism of gluconeogenic amino acids other than BCAAs characterizes both early and advanced T2DM stages. In conclusion, we identified a metabolic serum signature associated with T2DM stages. These data could be integrated with clinical characteristics to build a composite T2DM/complications risk score to be validated in a prospective cohort.

Metabolomics and Heart Diseases: From Basic to Clinical Approach

BACKGROUND

The field of metabolomics has been steadily increasing in size for the last 15 years. Advances in analytical and statistical methods have allowed metabolomics to flourish in various areas of medicine. Cardiovascular diseases are some of the main research targets in metabolomics, due to their social and medical relevance, and also to the important role metabolic alterations play in their pathogenesis and evolution. Metabolomics has been applied to the full spectrum of cardiovascular diseases: from patient risk stratification to myocardial infarction and heart failure. However - despite the many proof-ofconcept studies describing the applicability of metabolomics in the diagnosis, prognosis and treatment evaluation in cardiovascular diseases - it is not yet used in routine clinical practice. Recently, large phenome centers have been established in clinical environments, and it is expected that they will provide definitive proof of the applicability of metabolomics in clinical practice. But there is also room for small and medium size centers to work on uncommon pathologies or to resolve specific but relevant clinical questions.

OBJECTIVES

In this review, we will introduce metabolomics, cover the metabolomic work done so far in the area of cardiovascular diseases.

CONCLUSION

The cardiovascular field has been at the forefront of metabolomics application and it should lead the transfer to the clinic in the not so distant future.

Metabolomics Reveals Effect of Zishen Jiangtang Pill, a Chinese Herbal Product on High-Fat Diet-Induced Type 2 Diabetes Mellitus in Mice

A Chinese herbal decoction, Zishen Jiangtang Pill (ZJP), has been clinically prescribed to diabetic patients to prevent excessive blood sugar levels for decades. However, the potential mechanisms of this action have not been well investigated. The purpose of this study was to explore the metabolic variations in response to ZJP treatment for an animal model of obese type 2 diabetes. An UHPLC-Orbitrap/MS-based metabolomics tool was conducted to reveal the potential mechanisms of ZJP on diabetic mice. The treatment with ZJP significantly restored the increased levels of insulin, glucose and total cholesterol in high-fat diet mice. A total of 26 potential biomarkers were found and identified in serum samples, amongst which 24 metabolites were robustly affected and driven back to the control-like levels after ZJP treatment. By analyzing the metabolic pathways, glutathione metabolism, steroid hormone biosynthesis and glycerophospholipid metabolism were suggested to be closely involved in diabetes disease. From the above outcomes, it can be concluded that ZJP exhibits a promising anti-diabetic activity, largely due to the regulation of phospholipid metabolism, including phosphatidylcholines, lysophosphatidylcholines, and phosphatidylinositol.

Metabolic effects of VO(dmpp)2– anex vivo1H-HRMAS NMR study to unveil its pharmacological properties

VO(dmpp)₂ameliorates liver metabolic profile of obese pre-diabetic Zucker rats after 4 weeks of treatment, as demonstrated byex vivo¹H-HRMAS NMR study.

Neutrophils as a source of branched-chain, aromatic and positively charged free amino acids

Neutrophils release branched-chain (valine, isoleucine, leucine), aromatic (tyrosine, phenylalanine) and positively charged free amino acids (arginine, ornithine, lysine, hydroxylysine, histidine) when adhere and spread onto fibronectin. In the presence of agents that impair cell spreading or adhesion (cytochalasin D, fMLP, nonadhesive substrate), neutrophils release the same amino acids, except for a sharp decrease in hydroxylysine and an increase in phenylalanine, indicating their special connection with cell adhesion. Plasma of patients with diabetes is characterized by an increased content of branched-chain and aromatic amino acids and a reduced ratio of arginine/ornithine compared to healthy human plasma. Our data showed that the secretion of neutrophils, regardless of their adhesion state, can contribute to this shift in the amino acid content.

Abbreviations: BCAAs: branched-chain amino acids; Е2: 17β-estradiol; LPS: lipopolysaccharide from Salmonella enterica serovar Typhimurium; fMLP: N-formylmethionyl-leucyl-phenylalanine.

Chronic d-serine supplementation impairs insulin secretion

OBJECTIVE

The metabolic role of d-serine, a non-proteinogenic NMDA receptor co-agonist, is poorly understood. Conversely, inhibition of pancreatic NMDA receptors as well as loss of the d-serine producing enzyme serine racemase have been shown to modulate insulin secretion. Thus, we aim to study the impact of chronic and acute d-serine supplementation on insulin secretion and other parameters of glucose homeostasis.

METHODS

We apply MALDI FT-ICR mass spectrometry imaging, NMR based metabolomics, 16s rRNA gene sequencing of gut microbiota in combination with a detailed physiological characterization to unravel the metabolic action of d-serine in mice acutely and chronically treated with 1% d-serine in drinking water in combination with either chow or high fat diet feeding. Moreover, we identify SNPs in SRR, the enzyme converting L-to d-serine and two subunits of the NMDA receptor to associate with insulin secretion in humans, based on the analysis of 2760 non-diabetic Caucasian individuals.

RESULTS

We show that chronic elevation of d-serine results in reduced high fat diet intake. In addition, d-serine leads to diet-independent hyperglycemia due to blunted insulin secretion from pancreatic beta cells. Inhibition of alpha 2-adrenergic receptors rapidly restores glycemia and glucose tolerance in d-serine supplemented mice. Moreover, we show that single nucleotide polymorphisms (SNPs) in SRR as well as in individual NMDAR subunits are associated with insulin secretion in humans.

CONCLUSION

Thus, we identify a novel role of d-serine in regulating systemic glucose metabolism through modulating insulin secretion.

Glycosphingolipids promote pro-atherogenic pathways in the pathogenesis of hyperglycemia-induced accelerated atherosclerosis

INTRODUCTION

Three out of four people with diabetes will die of cardiovascular disease. However, the molecular mechanisms by which hyperglycemia promotes atherosclerosis, the major underlying cause of cardiovascular disease, are not clear.

OBJECTIVES

Three distinct models of hyperglycemia-associated accelerated atherosclerosis were used to identify commonly altered metabolites and pathways associated with the disease.

METHODS

Normoglycemic apolipoprotein-E-deficient mice served as atherosclerotic control. Hyperglycemia was induced by multiple low-dose streptozotocin injections, or by introducing a point-mutation in one copy of insulin-2 gene. Glucosamine-supplemented mice, which experience accelerated atherosclerosis to a similar extent as hyperglycemia-induced models without alterations in glucose or insulin levels, were also included in the analysis. Untargeted plasma metabolomics were used to investigate hyperglycemia-associated accelerated atherosclerosis in three disease models. The effect of specific significantly altered metabolites on pro-atherogenic processes was investigated in cultured human vascular cells.

RESULTS

Hyperglycemic and glucosamine-supplemented mice showed distinct metabolomic profiles compared to controls. Meta-analysis of three disease models revealed 62 similarly altered metabolite features (FDR-adjusted p < 0.05). Identification of shared metabolites revealed alterations in glycerophospholipid and sphingolipid metabolism, and pro-atherogenic processes including inflammation and oxidative stress. Post-multivariate and pathway analyses indicated that the glycosphingolipid pathway is strongly associated with hyperglycemia-induced accelerated atherosclerosis in these atherogenic mouse models. Glycosphingolipids induced oxidative stress and inflammation in cultured human vascular cells.

CONCLUSION

Glycosphingolipids are strongly associated with hyperglycemia-induced accelerated atherosclerosis in three distinct models. They also promote pro-atherogenic processes in cultured human cells. These results suggest glycosphingolipid pathway may be a potential therapeutic target to block or slow atherogenesis in diabetic patients.

Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project

Population-based prospective cohort studies are indispensable for modern medical research as they provide important knowledge on the influences of many kinds of genetic and environmental factors on the cause of disease. Although traditional cohort studies are mainly conducted using questionnaires and physical examinations, modern cohort studies incorporate omics and genomic approaches to obtain comprehensive physical information, including genetic information. Here, we report the design and midterm results of multi-omics analysis on population-based prospective cohort studies from the Tohoku Medical Megabank (TMM) Project. We have incorporated genomic and metabolomic studies in the TMM cohort study as both metabolome and genome analyses are suitable for high-throughput analysis of large-scale cohort samples. Moreover, an association study between the metabolome and genome show that metabolites are an important intermediate phenotype connecting genetic and lifestyle factors to physical and pathologic phenotypes. We apply our metabolome and genome analyses to large-scale cohort samples in the following studies.

Metabolism, Metabolomics, and Inflammation in Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is defined by classic psychological manifestations, although among the characteristics are significantly increased rates of serious somatic comorbidities, such as cardiovascular disease, immune dysfunction, and metabolic syndrome. In this review, we assess the evidence for disturbances that may contribute to somatic pathology in inflammation, metabolic syndrome, and circulating metabolites (implicating mitochondrial dysfunction) in individuals with PTSD and in animal models simulating features of PTSD. The clinical and preclinical data highlight probable interrelated features of PTSD pathophysiology, including a proinflammatory milieu, metabolomic changes (implicating mitochondrial and other processes), and metabolic dysregulation. These data suggest that PTSD may be a systemic illness, or that it at least has systemic manifestations, and the behavioral manifestations are those most easily discerned. Whether somatic pathology precedes the development of PTSD (and thus may be a risk factor) or follows the development of PTSD (as a result of either shared pathophysiologies or lifestyle adaptations), comorbid PTSD and somatic illness is a potent combination placing affected individuals at increased physical as well as mental health risk. We conclude with directions for future research and novel treatment approaches based on these abnormalities.

Aqueous Extract of Black Maca Prevents Metabolism Disorder via Regulating the Glycolysis/Gluconeogenesis-TCA Cycle and PPARα Signaling Activation in Golden Hamsters Fed a High-Fat, High-Fructose Diet

Maca (Lepidium meyenii Walpers) has been used as a dietary supplement and ethnomedicine for centuries. Recently, maca has become a high profile functional food worldwide because of its multiple biological activities. This study is the first explorative research to investigate the prevention and amelioration capacity of the aqueous extract of black maca (AEM) on high-fat, high-fructose diet (HFD)-induced metabolism disorder in golden hamsters and to identify the potential mechanisms involved in these effects. For 20 weeks, 6-week-old male golden hamsters were fed the following respective diets: (1) a standard diet, (2) HFD, (3) HFD supplemented with metformin, or (4) HFD supplemented with three doses of AEM (300, 600, or 1,200 mg/kg). After 20 weeks, the golden hamsters that received daily AEM supplementation presented with the beneficial effects of improved hyperlipidemia, hyperinsulinemia, insulin resistance, and hepatic steatosis in vivo. Based on the hepatic metabolomic analysis results, alterations in metabolites associated with pathological changes were examined. A total of 194 identified metabolites were mapped to 46 relative metabolic pathways, including those of energy metabolism. In addition, via in silico profiling for secondary maca metabolites by a joint pharmacophore- and structure-based approach, a compound-target-disease network was established. The results revealed that 32 bioactive compounds in maca targeted 16 proteins involved in metabolism disorder. Considering the combined metabolomics and virtual screening results, we employed quantitative real-time PCR assays to verify the gene expression of key enzymes in the relevant pathways. AEM promoted glycolysis and inhibited gluconeogenesis via regulating the expression of key genes such as Gck and Pfkm. Moreover, AEM upregulated tricarboxylic acid (TCA) cycle flux by changing the concentrations of intermediates and increasing the mRNA levels of Aco2, Fh, and Mdh2. In addition, the lipid-lowering effects of AEM in boththe serum and liver may be partly related to PPARα signaling activation, including enhanced fatty acid β-oxidation and lipogenesis pathway inhibition. Together, our data demonstrated that AEM intervention significantly improved lipid and glucose metabolism disorder by regulating the glycolysis/gluconeogenesis-TCA cycle and by modulating gene expression levels involved in the PPARα signaling pathway.

A systematic review of metabolomics biomarkers for Bisphenol A exposure

INTRODUCTION

Bisphenol A (BPA), 2,2-bis(4-hydroxyphenyl) propane, a common industrial chemical which has extremely huge production worldwide, is ubiquitous in the environment. Human have high risk of exposing to BPA and the health problems caused by BPA exposure have aroused public concern. However, the biomarkers for BPA exposure are lacking. As a rapidly developing subject, metabolomics has accumulated a large amount of valuable data in various fields. The secondary application of published metabolomics data could be a very promising field for generating novel biomarkers whilst further understanding of toxicity mechanisms.

OBJECTIVES

To summarize the published literature on the use of metabolomics as a tool to study BPA exposure and provide a systematic perspectives of current research on biomarkers screening of BPA exposure.

METHODS

We conducted a systematic search of MEDLINE (PubMed) up to the end of June 25, 2017 with the key term combinations of 'metabolomics', 'metabonomics', 'mass spectrometry', 'nuclear magnetic spectroscopy', 'metabolic profiling' and 'amino acid profile' combined with 'BPA exposure'. Additional articles were identified through searching the reference lists from included studies.

RESULTS

This systematic review included 15 articles. Intermediates of glycolysis, Krebs cycle, β oxidation of long chain fatty acids, pentose phosphate pathway, nucleoside metabolism, branched chain amino acid metabolism, aromatic amino acids metabolism, sulfur-containing amino acids metabolism were significantly changed after BPA exposure, suggesting BPA had a highly complex toxic effects on organism which was consistent with existing studies. The biomarkers most consistently associated with BPA exposure were lactate and choline.

CONCLUSION

Existing metabolomics studies of BPA exposure present heterogeneous findings regarding metabolite profile characteristics. We need more evidence from target metabolomics and epidemiological studies to further examine the reliability of these biomarkers which link to low, environmentally relevant, exposure of BPA in human body.

Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study

AIMS/HYPOTHESIS

Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes.

METHODS

We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case-control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders.

RESULTS

There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10^-7). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10^-3) and prevalent type 2 diabetes (OR_{Val_PC ae C32:2} 2.64 [β 0.97 ± 0.09], p = 1.0 × 10^-27). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HR_{Val_PC ae C32:2} 1.57 [β 0.45 ± 0.06]; p = 1.3 × 10^-15), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose).

CONCLUSIONS/INTERPRETATION

In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.

Development of Multimarker Diagnostic Models from Metabolomics Analysis for Gestational Diabetes Mellitus (GDM)

Although metabolomics are desirable to understand the pathophysiology of gestational diabetes mellitus (GDM), comprehensive metabolomic studies of GDM are rare. We aimed to offer a holistic view of metabolites alteration in GDM patients and investigate the possible multimarker models for GDM diagnosis. Biochemical parameters and perinatal data of 131 GDM cases and 138 controls were collected. Fasting serum samples at 75 g oral glucose tolerance test were used for metabolites by ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometry, ultra performance liquid chromatography-triple triple-quadrupole-mass spectrometry and gas chromatography- time-of- flight mass spectrometry platforms. Significant changes were observed in free fatty acids, bile acids, branched chain amino acids, organic acids, lipids and organooxygen compounds between two groups. In receiver operating characteristic (ROC) analysis, different combinations of candidate biomarkers and metabolites in multimarker models achieved satisfactory discriminative abilities for GDM, with the values of area under the curve (AUC) ranging from 0.721 to 0.751. Model consisting of body mass index (BMI), retinol binding protein 4 (RBP4), n-acetylaspartic acid and C16:1 (cis-7) manifested the best discrimination [AUC 0.751 (95% CI: 0.693-0.809), p < 0.001], followed by model consisting of BMI, Cystatin C, acetylaspartic acid and 6,7-diketoLCA [AUC 0.749 (95% CI: 0.691-0.808), p < 0.001]. Metabolites alteration reflected disorders of glucose metabolism, lipid metabolism, amino acid metabolism, bile acid metabolism as well as intestinal flora metabolism in GDM state. Multivariate models combining clinical markers and metabolites have the potential to differentiate GDM subjects from healthy controls.

Alterations in the placental methylome with maternal obesity and evidence for metabolic regulation

The inflammatory and metabolic derangements of obesity in pregnant women generate an adverse intrauterine environment, increase pregnancy complications and adverse fetal outcomes and program the fetus for obesity and metabolic syndrome in later life. We hypothesized that epigenetic modifications in placenta including altered DNA methylation/hydroxymethylation may mediate these effects. Term placental villous tissue was collected following cesarean section from lean (prepregnancy BMI<25) or obese (BMI>30) women. Genomic DNA was isolated, methylated and hydroxymethylated DNA immunoprecipitated and hybridized to the NimbleGen 2.1M human DNA methylation array. Intermediate metabolites in placental tissues were measured by HPLC-ESI-MS, ascorbate levels by reverse phase HPLC and gene expression by RT-PCR. Differentially methylated and hydroxymethylated regions occurred across the genome, with a 21% increase in methylated but a 31% decrease in hydroxymethylated regions in obese vs lean groups. Whereas increased methylation and decreased methylation was evident around transcription start sites of multiple genes in the GH/CSH and PSG gene clusters on chromosomes 17 and 19 in other areas there was no relationship. Increased methylation was associated with decreased expression only for some genes in these clusters. Biological pathway analysis revealed the 262 genes which showed reciprocal differential methylation/ hydroxymethylation were enriched for pregnancy, immune response and cell adhesion-linked processes. We found a negative relationship for maternal BMI but a positive relationship for ascorbate with α-ketoglutarate a metabolite that regulates ten eleven translocase (TET) which mediates DNA methylation. We provide evidence for the obese maternal metabolic milieu being linked to an altered DNA methylome that may affect placental gene expression in relation to adverse outcomes.

Metabolomics reveals that vine tea (Ampelopsis grossedentata) prevents high-fat-diet-induced metabolism disorder by improving glucose homeostasis in rats

Background

Vine tea (VT), derived from Ampelopsis grossedentata (Hand.-Mazz.) W.T. Wang, is an alternative tea that has been consumed widely in south China for hundreds of years. It has been shown that drinking VT on a daily basis improves hyperlipidemia and hyperglycemia. However, little is known about the preventive functions of VT for metabolic dysregulation and the potential pathological mechanisms involved. This paper elucidates the preventive effects of VT on the dysregulation of lipid and glucose metabolism using rats maintained on a high-fat-diet (HFD) in an attempt to explain the potential mechanisms involved.

Methods

Sprague Dawley (SD) rats were divided into five groups: a group given normal rat chow and water (control group); a group given an HFD and water (HFD group); a group given an HFD and Pioglitazone (PIO group), 5 mg /kg; and groups given an HFD and one of two doses of VT: 500 mg/L or 2000 mg/L. After 8 weeks, changes in food intake, tea consumption, body weight, serum and hepatic biochemical parameters were determined. Moreover, liver samples were isolated for pathology histology and liquid chromatography-mass spectrometry (LC-MS)-based metabolomic research.

Results

VT reduced the serum levels of glucose and total cholesterol, decreased glucose area under the curve in the insulin tolerance test and visibly impaired hepatic lipid accumulation. Metabolomics showed that VT treatment modulated the contents of metabolic intermediates linked to glucose metabolism (including gluconeogenesis and glycolysis), the TCA cycle, purine metabolism and amino acid metabolism.

Conclusion

The current results demonstrate that VT may prevent metabolic impairments induced by the consumption of an HFD. These effects may be caused by improved energy-related metabolism (including gluconeogenesis, glycolysis and TCA cycle), purine metabolism and amino acid metabolism, and reduced lipid levels in the HFD-fed rats.

Biochemical characterization and 1H NMR based metabolomics revealed Melicope lunu-ankenda leaf extract a potent anti-diabetic agent in rats

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by continuous hyperglycemia associated with insulin resistance and /or reduced insulin secretion. There is an emerging trend regarding the use of medicinal plants for the treatment of diabetes mellitus. Melicope lunu-ankenda (ML) is one of the Melicope species belonging to the family Rutaceae. In traditional medicines, its leaves and flowers are known to exhibit prodigious health benefits. The present study aimed at investigating anti-diabetic effect of Melicope lunu-ankenda (ML) leaves extract.

METHODS

In this study, anti-diabetic effect of ML extract is investigated in vivo to evaluate the biochemical changes, potential serum biomarkers and alterations in metabolic pathways pertaining to the treatment of HFD/STZ induced diabetic rats with ML extract using 1H NMR based metabolomics approach. Type 2 diabetic rats were treated with different doses (200 and 400 mg/kg BW) of Melicope lunu-ankenda leaf extract for 8 weeks, and serum samples were examined for clinical biochemistry. The metabolomics study of serum was also carried out using 1H NMR spectroscopy in combination with multivariate data analysis to explore differentiating serum metabolites and altered metabolic pathways.

RESULTS

The ML leaf extract (400 mg/kg BW) treatment significantly increased insulin level and insulin sensitivity of obese diabetic rats, with concomitant decrease in glucose level and insulin resistance. Significant reduction in total triglyceride, cholesterol and low density lipoprotein was also observed after treatment. Interestingly, there was a significant increase in high density lipoprotein of the treated rats. A decrease in renal injury markers and activities of liver enzymes was also observed. Moreover, metabolomics studies clearly demonstrated that, ML extract significantly ameliorated the disturbance in glucose metabolism, tricarboxylic acid cycle, lipid metabolism, and amino acid metabolism.

CONCLUSION

ML leaf extract exhibits potent antidiabetic properties, hence could be a useful and affordable alternative option for the management of T2DM.

Differential toxicity of arsenic on renal oxidative damage and urinary metabolic profiles in normal and diabetic mice

Diabetes is a common metabolic disease, which might influence susceptibility of the kidney to arsenic toxicity. However, relative report is limited. In this study, we compared the influence of inorganic arsenic (iAs) on renal oxidative damage and urinary metabolic profiles of normal and diabetic mice. Results showed that iAs exposure increased renal lipid peroxidation in diabetic mice and oxidative DNA damage in normal mice, meaning different effects of iAs exposure on normal and diabetic individuals. Nuclear magnetic resonance (NMR)-based metabolome analyses found that diabetes significantly changed urinary metabolic profiles of mice. Oxidative stress-related metabolites, such as arginine, glutamine, methionine, and β-hydroxybutyrate, were found to be changed in diabetic mice. The iAs exposure altered amino acid metabolism, lipid metabolism, carbohydrate metabolism, and energy metabolism in normal and diabetic mice, but had higher influence on metabolic profiles of diabetic mice than normal mice, especially for oxidative stress-related metabolites and metabolisms. Above results indicate that diabetes increased susceptibility to iAs exposure. This study provides basic information on differential toxicity of iAs on renal toxicity and urinary metabolic profiles in normal and diabetic mice and suggests that diabetic individuals should be considered as susceptible population in toxicity assessment of arsenic.

Pregnancy to postpartum transition of serum metabolites in women with gestational diabetes

CONTEXT

Gestational diabetes is commonly linked to development of type 2 diabetes mellitus (T2DM). There is a need to characterize metabolic changes associated with gestational diabetes in order to find novel biomarkers for T2DM.

OBJECTIVE

To find potential pathophysiological mechanisms and markers for progression from gestational diabetes mellitus to T2DM by studying the metabolic transition from pregnancy to postpartum.

DESIGN

The metabolic transition profile from pregnancy to postpartum was characterized in 56 women by mass spectrometry-based metabolomics; 11 women had gestational diabetes mellitus, 24 had normal glucose tolerance, and 21 were normoglycaemic but at increased risk for gestational diabetes mellitus. Fasting serum samples collected during trimester 3 (gestational week 32±0.6) and postpartum (10.5±0.4months) were compared in diagnosis-specific multivariate models (orthogonal partial least squares analysis). Clinical measurements (e.g., insulin, glucose, lipid levels) were compared and models of insulin sensitivity and resistance were calculated for the same time period.

RESULTS

Women with gestational diabetes had significantly increased postpartum levels of the branched-chain amino acids (BCAAs) leucine, isoleucine, and valine, and their circulating lipids did not return to normal levels after pregnancy. The increase in BCAAs occurred postpartum since the BCAAs did not differ during pregnancy, as compared to normoglycemic women.

CONCLUSIONS

Postpartum levels of specific BCAAs, notably valine, are related to gestational diabetes during pregnancy.

Insulin induces a shift in lipid and primary carbon metabolites in a model of fasting-induced insulin resistance

INTRODUCTION

Prolonged fasting in northern elephant seals (NES) is characterized by a reliance on lipid metabolism, conservation of protein, and reduced plasma insulin. During early fasting, glucose infusion previously reduced plasma free fatty acids (FFA); however, during late-fasting, it induced an atypical elevation in FFA despite comparable increases in insulin during both periods suggestive of a dynamic shift in tissue responsiveness to glucose-stimulated insulin secretion.

OBJECTIVE

To better assess the contribution of insulin to this fasting-associated shift in substrate metabolism.

METHODS

We compared the responses of plasma metabolites (amino acids (AA), FFA, endocannabinoids (EC), and primary carbon metabolites (PCM)) to an insulin infusion (65 mU/kg) in early- and late-fasted NES pups (n = 5/group). Plasma samples were collected prior to infusion (T0) and at 10, 30, 60, and 120 min post-infusion, and underwent untargeted and targeted metabolomics analyses utilizing a variety of GC-MS and LC-MS technologies.

RESULTS

In early fasting, the majority (72%) of metabolite trajectories return to baseline levels within 2 h, but not in late fasting indicative of an increase in tissue sensitivity to insulin. In late-fasting, increases in FFA and ketone pools, coupled with decreases in AA and PCM, indicate a shift toward lipolysis, beta-oxidation, ketone metabolism, and decreased protein catabolism. Conversely, insulin increased PCM AUC in late fasting suggesting that gluconeogenic pathways are activated. Insulin also decreased FFA AUC between early and late fasting suggesting that insulin suppresses triglyceride hydrolysis.

CONCLUSION

Naturally adapted tolerance to prolonged fasting in these mammals is likely accomplished by suppressing insulin levels and activity, providing novel insight on the evolution of insulin during a condition of temporary, reversible insulin resistance.

Lens metabolomic profiling as a tool to understand cataractogenesis in Atlantic salmon and rainbow trout reared at optimum and high temperature

Periods of high or fluctuating seawater temperatures result in several physiological challenges for farmed salmonids, including an increased prevalence and severity of cataracts. The aim of the present study was to compare cataractogenesis in Atlantic salmon (Salmo salar L.) and rainbow trout (Oncorhynchus mykiss) reared at two temperatures, and investigate whether temperature influences lens metabolism and cataract development. Atlantic salmon (101±2 g) and rainbow trout (125±3 g) were reared in seawater at either 13°C (optimum for growth) or 19°C during the 35 days experiment (n = 4 tanks for each treatment). At the end of the experiment, the prevalence of cataracts was nearly 100% for Atlantic salmon compared to ~50% for rainbow trout, irrespective of temperature. The severity of the cataracts, as evaluated by slit-lamp inspection of the lens, was almost three fold higher in Atlantic salmon compared to rainbow trout. The global metabolic profile revealed differences in lens composition and metabolism between the two species, which may explain the observed differences in cataract susceptibility between the species. The largest differences were seen in the metabolism of amino acids, especially the histidine metabolism, and this was confirmed by a separate quantitative analysis. The global metabolic profile showed temperature dependent differences in the lens carbohydrate metabolism, osmoregulation and redox homeostasis. The results from the present study give new insight in cataractogenesis in Atlantic salmon and rainbow trout reared at high temperature, in addition to identifying metabolic markers for cataract development.

Amino acid profiling in the gestational diabetes mellitus

Background

The prevalence of gestational diabetes mellitus (GDM) is increasing globally which is associated with various side effects for mothers and fetus. It seems that metabolomic profiling of the amino acids may be useful in early diagnosis of metabolic diseases. This study aimed to explore the association of the amino acids profiles with GDM.

Methods

Eighty three pregnant women with gestational age ≥25 weeks were randomly selected among pregnant women referred to prenatal care clinic in Arash hospital of Tehran, Iran. Women divided into three groups including 1) 25 pregnant women with normal glucose tolerance test, 2) 27 pregnant women with diabetes type 2 (T2D) (n: 27) and 3) 31 women with GDM (n: 31). Plasma levels of amino acids were measured by high performance liquid chromatography and were compared in three groups. Statistical analysis was performed using SPSS 16.

Results

Compared with normal mothers, GDM mothers showed higher plasma concentrations of Arginine (P = 0.01), Glycine (P = 0.01) and Methionine (P = 0.04), whereas the pregnant women with T2D had higher plasma levels of Asparagine (P = 0.01), Tyrosine (P < 0.01), Valine (P < 0.01), Phenylalanine (P < 0.01), Glutamine (P < 0.01) and Isolucine (P < 0.01). The results of regression analyses confirmed the significantly elevated in plasma concentration of Asparagine (OR:3.64, CI 1.22–10.47), Threonine (OR:3.38, CI 1.39–8.25), Aspartic acid (OR:3.92, CI 1.19–12.91), Phenylalanine (OR:2.66, CI 1.01–6.94), Glutamine (OR:2.53, CI 1.02–6.26) and Arginine (OR:1.96, CI 1.02–3.76) after adjustment for gestational age and BMI in GDM mothers compared to normal ones.

Conclusions

Amino acids levels are associated with risk of GDM and diabetes mellitus. However further prospective studies are needed to clarify the role of different metabolites involved in mechanism of GDM.

Metabolomics insights into diabetes nephropathy and protective effects of Radix Scutellariae on rats using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry

UHPLC-Q-TOF-MS based metabolomics combined with multivariate statistical analysis for evaluating protective effects ofRadix Scutellariaeon DN rats.

Metabonomics study of the therapeutic mechanism of fenugreek galactomannan on diabetic hyperglycemia in rats, by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry

Fenugreek is a traditional plant for the treatment of diabetes. Galactomannan, an active major component in fenugreek seeds, has shown hypoglycemic activity. The present study was performed to investigate the therapeutic mechanism underlying fenugreek galactomannan (F-GAL) in treating diabetes, using a metabonomics approach based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). The F-GAL used for study was highly purified, and its yield, purity, and galactose/mannose ratio were characterized by capillary zone electrophoresis (CZE) and a modified phenol-sulfuric acid method. After treatment of streptozotocin (STZ)-induced diabetic rats with F-GAL for 28days, urine and serum samples were analyzed by UPLC-QTOF/MS. Multivariate statistical approaches such as principal component analysis (PCA) and orthogonal projection to latent structures squares-discriminant analysis (OPLS-DA) were applied to distinguish the non-diabetic/untreated, diabetic/untreated, and diabetic/F-GAL-treated groups. Then, potential biomarkers were identified that may help elucidate the underlying therapeutic mechanism of F-GAL in diabetes. The results demonstrated that there was a clear separation among the three groups in the PCA model. Fourteen potential biomarkers were identified by OPLS-DA, and they were determined to be produced in response to the therapeutic effects of F-GAL. These biomarkers were involved in histidine metabolism, tryptophan metabolism, energy metabolism, phenylalanine metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and arachidonic acid metabolism. In conclusion, our study demonstrates that a metabonomics approach is a powerful, novel tool that can be used to evaluate the underlying therapeutic mechanisms of herb extracts.

Validation of a metabolite panel for early diagnosis of type 2 diabetes

BACKGROUND

Accurate, early diagnosis of type 2 diabetes (T2D) would enable more effective clinical management and a reduction in T2D complications. Therefore, we sought to identify plasma metabolite and protein biomarkers that, in combination with glucose, can better predict future T2D compared with glucose alone.

METHODS

In this case-control study, we used plasma samples from the Bavarian Red Cross Blood Transfusion Center study (61 T2D cases and 78 non-diabetic controls) for discovering T2D-associated metabolites, and plasma samples from the Personalized Medicine Research Project in Wisconsin (56 T2D cases and 445 non-diabetic controls) for validation. All samples were obtained before or at T2D diagnosis. We tested whether the T2D-associated metabolites could distinguish incident T2D cases from controls, as measured by the area under the receiver operating characteristic curve (AUC). Additionally, we tested six metabolic/pro-inflammatory proteins for their potential to augment the ability of the metabolites to distinguish cases from controls.

RESULTS

A panel of 10 metabolites discriminated better between T2D cases and controls than glucose alone (AUCs: 0.90 vs 0.87; p=2.08×10(-5)) in Bavarian samples, and associations between these metabolites and T2D were confirmed in Wisconsin samples. With use of either a Bayesian network classifier or ridge logistic regression, the metabolites, with or without the proteins, discriminated incident T2D cases from controls marginally better than glucose in the Wisconsin samples, although the difference in AUCs was not statistically significant. However, when the metabolites and proteins were added to two previously reported T2D prediction models, the AUCs were higher than those of each prediction model alone (AUCs: 0.92 vs 0.87; p=3.96×10(-2) and AUCs: 0.91 vs 0.71; p=1.03×10(-5), for each model, respectively).

CONCLUSIONS

Compared with glucose alone or with previously described T2D prediction models, a panel of plasma biomarkers showed promise for improved discrimination of incident T2D, but more investigation is needed to develop an early diagnostic marker.