Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Targeted metabolomics-based understanding of the sleep disturbances in drug-naïve patients with schizophrenia

BACKGROUND

Sleep disturbances are a common occurrence in patients with schizophrenia, yet the underlying pathogenesis remain poorly understood. Here, we performed a targeted metabolomics-based approach to explore the potential biological mechanisms contributing to sleep disturbances in schizophrenia.

METHODS

Plasma samples from 59 drug-naïve patients with schizophrenia and 36 healthy controls were subjected to liquid chromatography-mass spectrometry (LC-MS) targeted metabolomics analysis, allowing for the quantification and profiling of 271 metabolites. Sleep quality and clinical symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Positive and Negative Symptom Scale (PANSS), respectively. Partial correlation analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) model were used to identify metabolites specifically associated with sleep disturbances in drug-naïve schizophrenia.

RESULTS

16 characteristic metabolites were observed significantly associated with sleep disturbances in drug-naïve patients with schizophrenia. Furthermore, the glycerophospholipid metabolism (Impact: 0.138, p<0.001), the butanoate metabolism (Impact: 0.032, p=0.008), and the sphingolipid metabolism (Impact: 0.270, p=0.104) were identified as metabolic pathways associated with sleep disturbances in drug-naïve patients with schizophrenia.

CONCLUSIONS

Our study identified 16 characteristic metabolites (mainly lipids) and 3 metabolic pathways related to sleep disturbances in drug-naïve schizophrenia. The detection of these distinct metabolites provide valuable insights into the underlying biological mechanisms associated with sleep disturbances in schizophrenia.

Artesunate improves glucose and lipid metabolism in db/db mice by regulating the metabolic profile and the MAPK/PI3K/Akt signalling pathway

BACKGROUND

Diabetes is a metabolic disorder characterized by chronic hyperglycaemia. Chronic metabolic abnormalities and long-term hyperglycaemia may result in a wide range of acute and chronic consequences. Previous studies have demonstrated that artesunate(ART) has antidiabetic, anti-inflammatory, antiatherosclerotic, and other beneficial effects, but the specific regulatory mechanism is not completely clear.

AIM

This study investigated the effects of ART on metabolic disorders in type 2 diabetes mellitus (T2DM) model db/db mice and explored the underlying mechanisms involved.

METHODS

C57BL/KsJ-db/db mice were used to identify the targets and molecular mechanism of ART. Metabolomic methods were used to evaluate the efficacy of ART in improving T2DM-related metabolic disorders. Network pharmacology and transcriptomic sequencing were used to analyse the targets and pathways of ART in T2DM. Finally, molecular biology experiments were performed to verify the key targets and pathways selected by network pharmacology and transcriptomic analyses.

RESULTS

After a 7-week ART intervention (160 mg/kg), the glucose and lipid metabolism levels of the db/db mice improved. Additionally, the oxidative stress indices, namely, the MDA and SOD levels, significantly improved (p<0.01). Linoleic acid and glycerophospholipid metabolism, amino acid metabolism, bile acid synthesis, and purine metabolism disorders in db/db mice were partially corrected after ART treatment. Network pharmacology analysis identified important targets of ART for the treatment of metabolic disorders in T2DM . These targets are involved in key signalling pathways, including the highest scores observed for the PI3K/Akt signalling pathway. Transcriptomic analysis revealed that ART could activate the MAPK signalling pathway and two key gene targets, HGK and GADD45. Immunoblotting revealed that ART increases p-PI3K, p-AKT, Glut2, and IRS1 protein expression and suppresses the phosphorylation of p38, ERK1/2, and JNK, returning HGK and GADD45 to their preartesunate levels.

CONCLUSION

Treatment of db/db mice with 160 mg/kg ART for 7 weeks significantly reduced fasting blood glucose and lipid levels. It also improved metabolic imbalances in amino acids, lipids, purines, and bile acids, thereby improving metabolic disorders. These effects are achieved by activating the PI3K/AKT pathway and inhibiting the MAPK pathway, thus demonstrating the efficacy of the drug.

Long-term physical inactivity induces significant changes in biochemical pathways related to metabolism of proteins and glycerophospholipids in mice

Physical inactivity affects multiple organ systems, including the musculoskeletal system, which upsets the delicate balance of several secretory factors leading to metabolic derailment. This reduces contractile recruitment of the skeletal muscle with dampening of its oxidative capacity resulting in impaired intramuscular lipid metabolism and substrate utilization. We hypothesized that this altered phenotype would also have an indispensable effect on circulatory cytokines and the level of metabolic intermediates. In this study, comparison between sedentary (SED) and exercised (EXER) animal models showed that organismal metabolic parameters (body mass, oxygen utilization and glucose tolerance) are altered based on physical activity. Our data suggest that cytokines linked to glycemic excursions (insulin, c-peptide, glucagon) and their passive regulators (leptin, BDNF, active ghrelin, and GIP) exhibit changes in the SED group. Furthermore, some of the proinflammatory cytokines and myokines were upregulated in SED. Interestingly, serum metabolite analysis showed that the levels of glucogenic amino acids (alanine, glycine, tryptophan, proline and valine), nitrogenous amino acids (ornithine, asparagine, and glutamine) and myogenic metabolites (taurine, creatine) were altered due to the level of physical activity. A pyrimidine nucleoside (uridine), lipid metabolite (glycerol) and ketone bodies (acetoacetate and acetate) were found to be altered in SED. A Spearman rank correlation study between SED and CTRL showed that cytokines build a deformed network with metabolites in SED, indicating significant modifications in amino acids, phosphatidylinositol phosphate and glycerophospholipid metabolic pathways. Overall, long-term physical inactivity reorganizes the profile of proinflammatory cytokines, glucose sensing hormones, and protein and glycerophospholipid metabolism, which might be the initial factors of metabolic diseases due to SED.

Profile of plasma free amino acids, carnitine and acylcarnitines, and JAK2v617f mutation as potential metabolic markers in children with type 1 diabetic nephropathy

Fifty diabetic nephropathy (DN) children with type 1 diabetes mellitus (T1DM) and 50 healthy matched controls were included. Chromatographic assays of 14 amino acids, free carnitine and 27 carnitine esters using high-performance liquid chromatography/electrospray ionization-mass spectroscopy, and genetic testing for JAK2v617f mutation using real-time PCR were performed. Patients had significantly lower levels of tyrosine, branched-chain amino acids (BCAAs), and BCAA/AAA (aromatic chain amino acids) ratios, glycine, arginine, ornithine, free carnitine and some carnitine esters (C5, 6, 12 and 16) and higher phenylalanine, phenylalanine/tyrosine ratio and C18 compared with the controls and in the macro-albuminuria vs. the microalbuminuria group (p < 0.05 for all) except for free carnitine. Plasma carnitine was negatively correlated with eGFR (r = -0.488, p = 0.000). There were significant positive correlations between tyrosine with UACR ratio (r = 0.296, p = 0.037). The plasma BCAA/AAA ratio showed significant negative correlations with UACR (r = -0.484, p = 0.000). There was a significantly higher frequency of the JAK2V617F gene mutation in diabetic nephropathy patients compared with the control group and in macro-albuminuria than the microalbuminuria group (p = 0.000) for both. When monitoring children with T1DM, plasma free amino acids and acylcarnitine profiles should be considered, especially if they have tested positive for JAK2V617F for the early diagnosis of DN.

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer

Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing's syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing's syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine.

Preliminary observational study of metabonomics in patients with early and late-onset type 2 diabetes mellitus based on UPLC-Q-TOF/MS

Non-targeted metabonomic techniques were used to explore changes in metabolic profiles of patients with early onset and late onset T2DM. Newly diagnosed early onset T2DM (EarT2DM) and late onset T2DM (LatT2DM) patients were recruited, and the matched age, sex, and low-risk population of diabetes mellitus were selected as the control group. 117 adults were recruited in the study, including 21 in EarT2DM group with 25 in corresponding control group (heaCG1), and 48 in LatT2DM group with 23 in corresponding control group (heaCG2). There were 15 relatively distinctive metabolic variants in EarT2DM group and 10 distinctive metabolic variants in LatT2DM group. The same changing pathways mainly involved protein, aminoacyl-tRNA biosynthesis, fatty acid biosynthesis, taurine metabolism, arginine biosynthesis, lysosome and mTOR signaling pathway. The independent disturbed pathways in EarT2DM included branched chain amino acids, alanine, aspartate and glutamate metabolism. The independent disturbed pathways in LatT2DM involved linoleic acid metabolism, biosynthesis of unsaturated fatty acids, arginine, proline metabolism and FoxO signaling pathway. T2DM patients at different diagnosed ages may have different metabolite profiles. These metabolic differences need to be further verified.

A Review of the Impact of Pharmacogenetics and Metabolomics on the Efficacy of Metformin in Type 2 Diabetes

Metformin is the most often prescribed drug for people with type 2 diabetes (T2D). More than 120 million patients with T2D use metformin worldwide. However, monotherapy fails to achieve glycemic control in a third of the treated patients. Genetics contribute to some of the inter-individual variations in glycemic response to metformin. Numerous pharmacogenetic studies have demonstrated that variations in genes related to pharmacokinetics and pharmacodynamics of metformin's encoding transporters are mainly associated with metformin response. The goal of this review is to evaluate the current state of metformin pharmacogenetics and metabolomics research, discuss the clinical and scientific issues that need to be resolved in order to increase our knowledge of patient response variability to metformin, and how to improve patient outcomes. Metformin's hydrophilic nature and absorption as well as its action mechanism and effectiveness on T2D initiation are discussed. The impacts of variations associated with various genes are analysed to identify and evaluate the effect of genetic polymorphisms on the therapeutic activity of metformin. The metabolic pattern of T2D and metformin is also indicated. This is to emphasise that studies of pharmacogenetics and metabolomics could expand our knowledge of metformin response in T2D.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

Integrating network pharmacology and non-targeted metabolomics to explore the common mechanism of Coptis Categorized Formula improving T2DM zebrafish

ETHNOPHARMACOLOGICAL RELEVANCE

Coptis Categorized Formula (CCF) is one of the core prescriptions in Treatise on Febrile Diseases. Its efficacy can be available not only in exogenous diseases but widely in various internal injuries and miscellaneous diseases. CCF (i.e., Huanglian Jiedu Decoction, Huanglian Ejiao Decoction, Dahuang Huanglian Xiexin Decoction, Gegen Qinlian Decoction) is different in composition, but they all play a favorable role in curative effect on type 2 diabetes mellitus (T2DM). Therefore, it is of great significance to reveal the common mechanism of CCF in treating T2DM.

AIM OF THE STUDY

Based on network pharmacology and non-targeted metabolomics research strategy, the common mechanism of the CCF treating T2DM was discussed.

MATERIALS AND METHODS

Firstly, Ultra-high performance liquid chromatography-quadrupole-time of flight/mass spectrometry was used to identify the chemical constituents of the CCF. Then, the targets of these chemical components were used for network pharmacology analysis associated with therapeutic effect. Finally, the diabetic zebrafish model was constructed to further verify the common mechanism of the CCF in treating T2DM.

RESULTS

A total of 160 chemical compositions were identified and 16 of them were common chemical compositions of the four CCF, including berberine, baicalin, coptisine and so forth. Network pharmacology results showed that Dipeptidyl peptidase (DPP)-4, cysteinyl aspartate specific proteinase (CASP)3, nitric oxide synthase (NOS)2, NOS3, and other 37 targets were common targets of CCF, and advanced glycation end products (AGE)-receptor of advanced glycation end products (RAGE) signaling pathway in diabetic complications, mitogen-activated protein kinase (MAPK) signaling pathway and hypoxia inducible factor (HIF)-1 signaling pathway were critical pathways of four CCF in the treatment of T2DM. CCF can lessen the blood glucose of diabetic zebrafish. The contents of 25 differential metabolites in diabetic zebrafish were altered. These metabolites were mainly related to phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, sphingolipid metabolism, and tyrosine metabolism.

CONCLUSION

Our research shows that the common mechanism of CCF in improving T2DM is as follows: berberine, baicalin, coptisine and other chemical components can directionally regulate DPP-4, CASP3, NOS2, NOS3 and other targets, which are mediated by AGE-RAGE signaling pathway in diabetic complications, MAPK signaling pathway and HIF-1 signaling pathway. The content of endogenous metabolites such as L-valine and L-sorbitose changes, and further regulates the metabolism of amino acid metabolism, lipid metabolism, purine metabolism, sphingosine metabolism and arachidonic acid metabolism, so as to play a significant role in regulating glycolipid metabolism, improving insulin resistance, inhibiting cell apoptosis, anti-oxidation and anti-inflammation, and finally ameliorating T2DM.

Possible Gender Influence in the Mechanisms Underlying the Oxidative Stress, Inflammatory Response, and the Metabolic Alterations in Patients with Obesity and/or Type 2 Diabetes

The number of patients afflicted by type 2 diabetes and its morbidities has increased alarmingly, becoming the cause of many deaths. Normally, during nutrient intake, insulin secretion is increased and glucagon secretion is repressed, but when plasma glucose concentration increases, a state of prediabetes occurs. High concentration of plasma glucose breaks the redox balance, inducing an oxidative stress that promotes chronic inflammation, insulin resistance, and impaired insulin secretion. In the same context, obesity is one of the most crucial factors inducing insulin resistance, inflammation, and contributing to the onset of type 2 diabetes. Measurements of metabolites like glucose, fructose, amino acids, and lipids exhibit significant predictive associations with type 2 diabetes or a prediabetes state and lead to changes in plasma metabolites that could be selectively affected by gender and age. In terms of gender, women and men have biological dissimilarities that might have an important role for the development, diagnosis, therapy, and prevention of type 2 diabetes, obesity, and relevant hazards in both genders, for type 2 diabetes. Therefore, the present review attempts to analyze the influence of gender on the relationships among inflammatory events, oxidative stress, and metabolic alterations in patients undergoing obesity and/or type 2 diabetes.

Anticipation of Precision Diabetes and Promise of Integrative Multi-Omics

Precision diabetes is a concept of customizing delivery of health practices based on variability of diabetes. The authors reviewed recent research on type 2 diabetes heterogeneity and -omic biomarkers, including genomic, epigenomic, and metabolomic markers associated with type 2 diabetes. The emerging multiomics approach integrates complementary and interconnected molecular layers to provide systems level understanding of disease mechanisms and subtypes. Although the multiomic approach is not currently ready for routine clinical applications, future studies in the context of precision diabetes, particular in populations from diverse ethnic and demographic groups, may lead to improved diagnosis, treatment, and management of diabetes and diabetic complications.

How Perturbated Metabolites in Diabetes Mellitus Affect the Pathogenesis of Hypertension?

The presence of hypertension (HTN) in type 2 diabetes mellitus (DM) is a common phenomenon in more than half of the diabetic patients. Since HTN constitutes a predictor of vascular complications and cardiovascular disease in type 2 DM patients, it is of significance to understand the molecular and cellular mechanisms of type 2 DM binding to HTN. This review attempts to understand the mechanism via the perspective of the metabolites. It reviewed the metabolic perturbations, the biological function of perturbated metabolites in two diseases, and the mechanism underlying metabolic perturbation that contributed to the connection of type 2 DM and HTN. DM-associated metabolic perturbations may be involved in the pathogenesis of HTN potentially in insulin, angiotensin II, sympathetic nervous system, and the energy reprogramming to address how perturbated metabolites in type 2 DM affect the pathogenesis of HTN. The recent integration of the metabolism field with microbiology and immunology may provide a wider perspective. Metabolism affects immune function and supports immune cell differentiation by the switch of energy. The diverse metabolites produced by bacteria modified the biological process in the inflammatory response of chronic metabolic diseases either. The rapidly evolving metabolomics has enabled to have a better understanding of the process of diseases, which is an important tool for providing some insight into the investigation of diseases mechanism. Metabolites served as direct modulators of biological processes were believed to assess the pathological mechanisms involved in diseases.

The evolving landscape of untargeted metabolomics

AIMS

Untargeted Metabolomics is a "hypothesis-generating discovery strategy" that compares groups of samples (e.g., cases vs controls); identifies the metabolome and establishes (early signs of) perturbations. Targeted Metabolomics helped gather key information in life sciences and disclosed novel strategies for the treatment of major clinical entities (e.g., malignancy, cardiovascular diabetes mellitus, drug toxicity). Because of its relevance in biomarker discovery, attention is now devoted to improving the translational potential of untargeted Metabolomics.

DATA SYNTHESIS

Expertise in laboratory medicine and in bioinformatics helps solve challenges/pitfalls that may bias metabolite profiling in untargeted Metabolomics. Clinical validation (availability/reliability of analytical instruments) and profitability (how many people will use the test) are mandatory steps for potential biomarkers. Biomarkers to predict individual patient response, patient populations that will best respond to specific strategies and/or approaches for an optimal response to treatment are now being developed. Additional help is expected from professional, and regulatory Agencies as to guidelines for study design and data acquisition and analysis, to be applied from the very beginning of a project. Evidence from food, plant, human, environmental, and animal research argues for the need of miniaturized approaches that employ low-cost, easy to use, mobile devices. ELISA kits with such characteristics that employ targeted metabolites are already available.

CONCLUSIONS

Improving knowledge of the mechanisms behind the disease status (pathophysiology) will help untargeted Metabolomics gather a direct positive impact on welfare and industrial advancements, and fade uncertainties perceived by regulators/payers and patients concerning variables related to miniaturised instruments and user-friendly software and databases.

Metabolic and biological changes in children with obesity and diabetes

The World Health Organization has stated that obesity in childhood is one of the most serious public health challenges of the 21^st century. Overweightness and obesity in early childhood lead to a higher risk of overweightness and obesity in adulthood, thus conferring an increased risk of chronic inflammatory conditions, including type 2 diabetes mellitus, cardiovascular diseases, non-alcoholic fatty liver disease, and some cancers. Therefore, metabolome analysis, targeted at screening and intervening in childhood obesity, is very important. Recent studies have indicated that amino acid and lipid metabolism could influence metabolic pathways in children with obesity. For this review, we searched clinical data addressing metabolomic profiles and insulin resistance (IR) in children with obesity from inception to February 2021 in Medline, Web of Science, and Scopus. According to our search, branched-chain amino acids (BCAAs), aromatic amino acids, and acylcarnitines have reportedly been associated with IR as biomarkers for diabetes in children. BCAAs, tyrosine, and phenylalanine could be predictors of the future development of diabetes in nondiabetic subjects. In addition, it is well known that insulin regulates BCAA metabolism, and BCAA is a biomarker for IR. To interpret the mechanism behind metabolic changes in obesity, it is very important to understand the pathways and combinations related with amino acid, lipid and glucose metabolism. In this review, we summarize studies on metabolic changes to understand metabolomics in children with obesity.

Metabolite Triplet in Serum Improves the Diagnostic Accuracy of Prediabetes and Diabetes Screening

Large-scale population screenings are not feasible by applying laborious oral glucose tolerance tests, but using fasting blood glucose (FPG) and glycated hemoglobin (HbA_1c), a considerable number of diagnoses are missed. A novel marker is urgently needed to improve the diagnostic accuracy of broad-scale diabetes screening in easy-to-collect blood samples. In this study, by applying a novel knowledge-based, multistage discovery and validation strategy, we scaled down from 108 diabetes-associated metabolites to a diagnostic metabolite triplet (Met-T), namely hexose, 2-hydroxybutyric/2-hydroxyisobutyric acid, and phenylalanine. Met-T showed in two independent cohorts, each comprising healthy controls, prediabetic, and diabetic individuals, distinctly higher diagnostic sensitivities for diabetes screening than FPG alone (>79.6 vs <68%). Missed diagnoses decreased from >32% using fasting plasma glucose down to <20.4%. Combining Met-T and fasting plasma glucose further improved the diagnostic accuracy. Additionally, a positive association of Met-T with future diabetes risk was found (odds ratio: 1.41; p = 1.03 × 10^-6). The results reveal that missed prediabetes and diabetes diagnoses can be markedly reduced by applying Met-T alone or in combination with FPG and it opens perspectives for higher diagnostic accuracy in broad-scale diabetes-screening approaches using easy to collect sample materials.

Research progress and perspective in metabolism and metabolomics of psoriasis

ABSTRACT

Psoriasis is considered a systemic disease associated with metabolic abnormalities, and it is important to understand the mechanisms by which metabolism affects pathophysiological processes both holistically and systematically. Metabolites are closely related to disease phenotypes, especially in systemic diseases under multifactorial modulation. The emergence of metabolomics has provided information regarding metabolite changes in lesions and circulation and deepened our understanding of the association between metabolic reprogramming and psoriasis. Metabolomics has great potential for the development of effective biomarkers for clinical diagnosis, therapeutic monitoring, prediction of the efficacy of psoriasis management, and further discovery of new metabolism-based therapeutic targets.

The effect of circuit resistance training on plasma levels of amino acids, alpha-hydroxybutyrate, mannose, and urinary levels of glycine conjugated adducts in obese adolescent boys

Few studies have examined the improving effects of exercise on the association between metabolites of impaired protein metabolism and insulin resistance in obese children. Therefore, this study aims to investigate the effect of circuit resistance training (CRT) on plasma levels of amino acids, alpha-hydroxybutyrate (α-HB), mannose, and urinary levels of glycine conjugated adducts in obese adolescent boys. Forty obese adolescent boys (body mass index above the 95th percentile) with an age range of 14-17 years were randomly divided into the CRT group (n = 20) and control group (n = 20). The CRT program (3 times/week, 70%-80% of 1-repetition maximum) was performed for 8 weeks. The results indicated that the body composition and plasma levels of glucose, insulin resistance, valine, mannose, lysine, and the sum of branched-chain amino acids (BCAA) were decreased because of CRT. The plasma levels of asparagine, glycine, serine, and urinary levels of glycine conjugated adduct also increased in the CRT group. Although α-HB level decreased during CRT, it had no significant difference from that of the control group. It can be concluded that the improvement in obesity complications including insulin resistance in obese adolescent boys after CRT may be due to decrease in plasma levels of mannose and BCAA and increase urinary metabolites. Novelty: CRT improves glucose metabolism and insulin resistance in obese adolescent boys. CRT decreases plasma levels of mannose and BCAA and normalizes other amino acids. CRT increases urinary levels of glycine conjugated adducts.

[Ten years of the Russian metabolomics: history of development and achievements]

Metabolomics is one of the omics sciences, the technologies of which are widely used today in many life sciences. Its application influenced the discovery of new biomarkers of diseases, the description of biochemical processes occurring in many organisms, laid the basis for a new generation of clinical laboratory diagnostics. The purpose of this review is to show how metabolomics is represented in the studies of Russian scientists, to demonstrate the main directions and achievements of the Russian science in this field. The review also highlights the history of metabolomics, existing problems and the place of Russian metabolomics in their solution.

Altered Metabolome of Lipids and Amino Acids Species: A Source of Early Signature Biomarkers of T2DM

Diabetes mellitus, a disease of modern civilization, is considered the major mainstay of mortalities around the globe. A great number of biochemical changes have been proposed to occur at metabolic levels between perturbed glucose, amino acid, and lipid metabolism to finally diagnoe diabetes mellitus. This window period, which varies from person to person, provides us with a unique opportunity for early detection, delaying, deferral and even prevention of diabetes. The early detection of hyperglycemia and dyslipidemia is based upon the detection and identification of biomarkers originating from perturbed glucose, amino acid, and lipid metabolism. The emerging “OMICS” technologies, such as metabolomics coupled with statistical and bioinformatics tools, proved to be quite useful to study changes in physiological and biochemical processes at the metabolic level prior to an eventual diagnosis of DM. Approximately 300–400 such metabolites have been reported in the literature and are considered as predicting or risk factor-reporting metabolic biomarkers for this metabolic disorder. Most of these metabolites belong to major classes of lipids, amino acids and glucose. Therefore, this review represents a snapshot of these perturbed plasma/serum/urinary metabolic biomarkers showing a significant correlation with the future onset of diabetes and providing a foundation for novel early diagnosis and monitoring the progress of metabolic syndrome at early symptomatic stages. As most metabolites also find their origin from gut microflora, metabolism and composition of gut microflora also vary between healthy and diabetic persons, so we also summarize the early changes in the gut microbiome which can be used for the early diagnosis of diabetes.

Enhanced single-cell metabolomics by capillary electrophoresis electrospray ionization-mass spectrometry with field amplified sample injection

Single-cell metabolomics provides information on the biochemical state of an individual cell and its relationship with the surrounding environment. Characterization of metabolic cellular heterogeneity is challenging, in part due to the small amounts of analytes and their wide dynamic concentration ranges within individual cells. CE-ESI-MS is well suited to single-cell assays because of its low sample-volume requirements and low detection limits. While the volume of a cell is in the picoliter range, after isolation, the typical volume of the lysed cell sample is on the order of a microliter; however, only nanoliters are injected into the CE system, with the volume mismatch limiting analytical performance. Here we developed an approach for the detection of intracellular metabolites from a single neuron using field amplified sample injection (FASI) CE-ESI-MS. Through the application of FASI, we achieved 100- to 300-fold detection limit enhancement compared to hydrodynamic injections. We further enhanced the analyte identification and quantification accuracy via introduction of two internal standards. As a result, the relative standard deviations of migration times were reduced to <5%, aiding identification. Finally, we successfully applied FASI CE-ESI-MS to the untargeted profiling of metabolites of Aplysia californica pleural sensory neurons with <50 μm diameter cell somata. As a result, twenty one neurotransmitters and metabolites have been quantified in these neurons.

Random plasma glucose predicts the diagnosis of diabetes

Aims/Hypothesis

Early recognition of those at high risk for diabetes as well as diabetes itself can permit preventive management, but many Americans with diabetes are undiagnosed. We sought to determine whether routinely available outpatient random plasma glucose (RPG) would be useful to facilitate the diagnosis of diabetes.

Methods

Retrospective cohort study of 942,446 U.S. Veterans without diagnosed diabetes, ≥3 RPG in a baseline year, and ≥1 primary care visit/year during 5-year follow-up. The primary outcome was incident diabetes (defined by diagnostic codes and outpatient prescription of a diabetes drug).

Results

Over 5 years, 94,599 were diagnosed with diabetes [DIAB] while 847,847 were not [NONDIAB]. Baseline demographics of DIAB and NONDIAB were clinically similar, except DIAB had higher BMI (32 vs. 28 kg/m²) and RPG (150 vs. 107 mg/dl), and were more likely to have Black race (18% vs. 15%), all p<0.001. ROC area for prediction of DIAB diagnosis within 1 year by demographic factors was 0.701, and 0.708 with addition of SBP, non-HDL cholesterol, and smoking. These were significantly less than that for prediction by baseline RPG alone (≥2 RPGs at/above a given level, ROC 0.878, p<0.001), which improved slightly when other factors were added (ROC 0.900, p<0.001). Having ≥2 RPGs ≥115 mg/dl had specificity 77% and sensitivity 87%, and ≥2 RPGs ≥130 mg/dl had specificity 93% and sensitivity 59%. For predicting diagnosis within 3 and 5 years by RPG alone, ROC was reduced but remained substantial (ROC 0.839 and 0.803, respectively).

Conclusions

RPG levels below the diabetes “diagnostic” range (≥200 mg/dl) provide good discrimination for follow-up diagnosis. Use of such levels–obtained opportunistically, during outpatient visits–could signal the need for further testing, allow preventive intervention in high risk individuals before onset of disease, and lead to earlier identification of diabetes.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Metabolomics of Type 1 and Type 2 Diabetes

Type 1 and type 2 diabetes mellitus (DM) are chronic diseases that affect nearly 425 million people worldwide, leading to poor health outcomes and high health care costs. High-throughput metabolomics screening can provide vital insight into the pathophysiological pathways of DM and help in managing its effects. The primary aim of this study was to contribute to the understanding and management of DM by providing reliable evidence of the relationships between metabolites and type 1 diabetes (T1D) and metabolites and type 2 diabetes (T2D). Information for the study was obtained from the PubMed, MEDLINE, and EMBASE databases, and leads to additional articles that were obtained from the reference lists of the studies examined. The results from the selected studies were used to assess the relationships between diabetes (T1D and/or T2D) and metabolite markers—such as glutamine, glycine, and aromatic amino acids—in patients. Seventy studies were selected from the three databases and from the reference lists in the records retrieved. All studies explored associations between various metabolites and T1D or T2D. This review identified several plasma metabolites associated with T2D prediabetes and/or T1D and/or T2D in humans. The evidence shows that metabolites such as glucose, fructose, amino acids, and lipids are typically altered in individuals with T1D and T2D. These metabolites exhibit significant predictive associations with T2D prediabetes, T1D, and/or T2D. The current review suggests that changes in plasma metabolites can be identified by metabolomic techniques and used to identify and analyze T1D and T2D biomarkers. The results of the metabolomic studies can be used to help create effective interventions for managing these diseases.

Predictive Estimates of Risks Associated with Type 2 Diabetes Mellitus on the Basis of Biochemical Biomarkers and Derived Time-Dependent Parameters

This work contributes to the development of effective statistical methods of big data analysis for type 2 diabetes mellitus (T2DM) risk assessment to be employed in routine clinical practice. The objective of this study to be reached via machine-learning analysis is twofold: investigation of a possible application of biochemical biomarkers for the T2DM risk prediction in case of a limited knowledge of biometrical parameters of an individual, as well as study on the predictive ability of a derived parameter (rate of a biomarker change over time) in T2DM risk prediction. Obtained statistical parameters (AUC, p-value, etc.) justify a relatively high quality of the model. Nevertheless, a further improvement may be addressed through the following avenues: analysis of adding new factors and models, including lifestyle/habits, and genetic parameters.

Overview of genomics and post-genomics research on type 2 diabetes mellitus: Future perspectives and a framework for further studies

In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes. Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies. We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease. We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.

Detection of Secondary Metabolites as Biomarkers for the Early Diagnosis and Prevention of Type 2 Diabetes

BACKGROUND

Type 2 diabetes, or T2D, is a metabolic disease that results in insulin resistance. In the present study, we hypothesize that metabolomic analysis in blood samples of T2D patients sharing the same ethnic background can recover new metabolic biomarkers and pathways that elucidate early diagnosis and predict the incidence of T2D.

METHODS

The study included 34 T2D patients and 33 healthy volunteers recruited between the years 2012 and 2013; the secondary metabolites were extracted from blood samples and analyzed using HPLC.

RESULTS

Principal coordinate analysis and hierarchical clustering patterns for the uncharacterized negatively and positively charged metabolites indicated that samples from healthy individuals and T2D patients were largely separated with only a few exceptions. The inspection of the top 10% secondary metabolites indicated an increase in fucose, tryptophan and choline levels in the T2D patients, while there was a reduction in carnitine, homoserine, allothreonine, serine and betaine as compared to healthy individuals. These metabolites participate mainly in three cross-talking pathways, namely "glucagon signaling", "glycine, serine and threonine" and "bile secretion". Reduced level of carnitine in T2D patients is known to participate in the impaired insulin-stimulated glucose utilization, while reduced betaine level in T2D patients is known as a common feature of this metabolic syndrome and can result in the reduced glycine production and the occurrence of insulin resistance. However, reduced levels of serine, homoserine and allothrionine, substrates for glycine production, indicate the depletion of glycine, thus possibly impair insulin sensitivity in T2D patients of the present study.

CONCLUSION

We introduce serine, homoserine and allothrionine as new potential biomarkers of T2D.