Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC-HRMS and Machine Learning

Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.

Supplementation with Ginseng, Lilii Bulbus, and Poria induces alterations in the serum metabolic profile of healthy adults

The preventive and therapeutic effects of herbal supplementation containing Ginseng, Lilii Bulbus, and Poria (GLP) on inflammation and oxidative stress in healthy adults have been demonstrated in our previous studies....

Defining Acute Coronary Syndrome through Metabolomics

As an emerging platform technology, metabolomics offers new insights into the pathomechanisms associated with complex disease conditions, including cardiovascular diseases. It also facilitates assessing the risk of developing the disease before its clinical manifestation. For this reason, metabolomics is of growing interest for understanding the pathogenesis of acute coronary syndromes (ACS), finding new biomarkers of ACS, and its associated risk management. Metabolomics-based studies in ACS have already demonstrated immense potential for biomarker discovery and mechanistic insights by identifying metabolomic signatures (e.g., branched-chain amino acids, acylcarnitines, lysophosphatidylcholines) associated with disease progression. Herein, we discuss the various metabolomics approaches and the challenges involved in metabolic profiling, focusing on ACS. Special attention has been paid to the clinical studies of metabolomics and lipidomics in ACS, with an emphasis on ischemia/reperfusion injury.

Exacerbation of cardiovascular ageing by diabetes mellitus and its associations with acyl-carnitines

Objective: To demonstrate differences in cardiovascular structure and function between diabetic and non-diabetic older adults. To investigate associations between acyl-carnitines and cardiovascular function as indexed by imaging measurements.

Methods: A community-based cohort of older adults without cardiovascular disease underwent current cardiovascular imaging and metabolomics acyl-carnitines profiling based on current and archived sera obtained fifteen years prior to examination.

Results: A total of 933 participants (women 56%, n=521) with a mean age 63±13 years were studied. Old diabetics compared to old non-diabetics had lower myocardial relaxation (0.8±0.2 vs 0.9±0.3, p=0.0039); lower left atrial conduit strain (12±4.3 vs 14±4.1, p=0.045), lower left atrial conduit strain rate (-1.2±0.4 vs -1.3±0.5, p=0.042) and lower ratio of left atrial conduit strain to left atrial booster strain (0.5±0.2 vs 0.7±0.3, p=0.0029). Higher levels of archived short chain acyl-carnitine were associated with present-day impairments in myocardial relaxation (C5:1; OR 1.03, p=0.011), worse left atrial conduit strain function (C5:1; OR 1.03, p=0.037). Increases in hydroxylated acyl-carnitines were associated with worse left atrial conduit strain [(C4-OH; OR 1.05, p=0.0017), (C16:2-OH; OR 1.18, p=0.037)]. Current, archived and changes in long chain acyl-carnitines were associated with cardiovascular functions [(C16; OR 1.02, p=0.002), (C20:3; OR 1.01, p=0.014), (C14:3; OR 1.12, p=0.033), (C18:1; OR 1.01, p=0.018), (C18:2; OR 1.01, p=0.028), (C20:4; OR 1.10, p=0.038)] (all p<0.05).

Conclusion: Older diabetic adults had significant impairments in left ventricular myocardial relaxation and left atrial strain, compared to older non-diabetic adults. Short chain and long chain, di-carboxyl and hydroxylated acyl-carnitines were associated with these cardiovascular functional differences.

Metabolomics and cardiovascular imaging: a combined approach for cardiovascular ageing

The purpose of this review is to explore how metabolomics can help uncover new biomarkers and mechanisms for cardiovascular ageing. Cardiovascular ageing refers to cardiovascular structural and functional alterations that occur with chronological ageing and that can lead to the development of cardiovascular disease. These alterations, which were previously only detectable on tissue histology or corroborated on blood samples, are now detectable with modern imaging techniques. Despite the emergence of powerful new imaging tools, clinical investigation into cardiovascular ageing is challenging because ageing is a life course phenomenon involving known and unknown risk factors that play out in a dynamic fashion. Metabolomic profiling measures large numbers of metabolites with diverse chemical properties. Metabolomics has the potential to capture changes in biochemistry brought about by pathophysiologic processes as well as by normal ageing. When combined with non-invasive cardiovascular imaging tools, metabolomics can be used to understand pathological consequences of cardiovascular ageing. This review will summarize previous metabolomics and imaging studies in cardiovascular ageing. These methods may be a clinically relevant and novel approach to identify mechanisms of cardiovascular ageing and formulate or personalize treatment strategies.

Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry

Untargeted methods are typically used in the detection and discovery of small organic compounds in metabolomics research, and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is one of the most commonly used platforms for untargeted metabolomics. Although they are non-biased and have high coverage, untargeted approaches suffer from unsatisfying repeatability and a requirement for complex data processing. Targeted metabolomics based on triple-quadrupole mass spectrometry (TQMS) could be a complementary tool because of its high sensitivity, high specificity and excellent quantification ability. However, it is usually applicable to known compounds: compounds whose identities are known and/or are expected to be present in the analyzed samples. Pseudotargeted metabolomics merges the advantages of untargeted and targeted metabolomics and can act as an alternative to the untargeted method. Here, we describe a detailed protocol of pseudotargeted metabolomics using UHPLC-TQMS. An in-depth, untargeted metabolomics experiment involving multiple UHPLC-HRMS runs with MS at different collision energies (both positive and negative) is performed using a mixture obtained using small amounts of the analyzed samples. XCMS, CAMERA and Multiple Reaction Monitoring (MRM)-Ion Pair Finder are used to find and annotate peaks and choose transitions that will be used to analyze the real samples. A set of internal standards is used to correct for variations in retention time. High coverage and high-performance quantitative analysis can be realized. The entire protocol takes ~5 d to complete and enables the simultaneously semiquantitative analysis of 800-1,300 metabolites.

Heavy metals interfere with plasma metabolites, including lipids and amino acids, in patients with breast cancer

The aim of the present study was to examine the association between plasma heavy metals and the metabolome in patients with breast cancer (BC), and the association with cancer development. Nuclear magnetic resonance was used to determine the metabolites involved and an inductively coupled plasma mass spectrometry system was used to quantify the heavy metals in the plasma samples. It was indicated that cadmium was significantly higher in the plasma of patients with BC compared with that in the control population (~15-fold increase). Chromium, arsenic and lead were also elevated in the plasma of patients with BC by ~3.24, 2.14 and 1.52 fold, respectively. A number of small molecules, including amino acids and salts, were altered in the plasma of patients with BC compared with the control population. Another notable finding in this investigation was that plasma lipid levels were elevated in patients with BC compared with those in the control population. The findings of the present study suggest that exposure to heavy metals, including cadmium, arsenic, chromium and lead, may influence blood lipid levels and other small molecule metabolites, which in turn may be involved in BC development. Further studies surrounding urinary heavy metals and the metabolome are required to further determine the impact of metals on metabolism and on BC development.

A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis

Understanding metabotype (multicomponent metabolic characteristics) variation can help to generate new diagnostic and prognostic biomarkers, as well as models, with potential to impact on patient management. We present a suite of conceptual approaches for the generation, analysis, and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of (i) metabolite levels, (ii) metabolic trajectories, (iii) metabolic entropies, and (iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalized medicine, with the aim of improving clinical outcomes for patients, but the framework will have value and utility in areas of metabolic profiling well beyond this exemplar.

Fish-oil supplementation in pregnancy, child metabolomics and asthma risk

Background

We recently demonstrated that maternal dietary supplementation with fish oil-derived n-3 long-chain polyunsaturated fatty acids (n-3 LCPUFAs) during pregnancy reduces the risk of asthma in the offspring but the mechanisms involved are unknown.

Methods

Here we investigated potential metabolic mechanisms using untargeted liquid chromatography-mass spectrometry-based metabolomics on 577 plasma samples collected at age 6 months in the offspring of mothers participating in the n-3 LCPUFA randomized controlled trial. First, associations between the n-3 LCPUFA supplementation groups and child metabolite levels were investigated using univariate regression models and data-driven partial least square discriminant analyses (PLS-DA). Second, we analyzed the association between the n-3 LCPUFA metabolomic profile and asthma development using Cox-regression. Third, we conducted mediation analyses to investigate whether the protective effect of n-3 LCPUFA on asthma was mediated via the metabolome.

Findings

The univariate analyses and the PLS-DA showed that maternal fish oil supplementation affected the child's metabolome, especially with lower levels of the n-6 LCPUFA pathway-related metabolites and saturated and monounsaturated long-chain fatty acids-containing compounds, lower levels of metabolites of the tryptophan pathway, and higher levels of metabolites in the tyrosine and glutamic acid pathway. This fish oil-related metabolic profile at age 6 months was significantly associated with a reduced risk of asthma by age 5 and the metabolic profile explained 24% of the observed asthma-protective effect in the mediation analysis.

Interpretation

Several of the observed pathways may be involved in the asthma-protective effect of maternal n-3 LCPUFA supplementation and act as mediators between the intervention and disease development.

Funding

COPSAC is funded by private and public research funds all listed on www.copsac.com.

NMR Spectroscopy for Metabolomics Research

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

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.

Current Progress of Lipid Analysis in Metabolic Diseases by Mass Spectrometry Methods

BACKGROUND

Obesity, insulin resistance, diabetes, and metabolic syndrome are associated with lipid alterations, and they affect the risk of long-term cardiovascular disease. A reliable analytical instrument to detect changes in the composition or structures of lipids and the tools allowing to connect changes in a specific group of lipids with a specific disease and its progress, is constantly lacking. Lipidomics is a new field of medicine based on the research and identification of lipids and lipid metabolites present in human organism. The primary aim of lipidomics is to search for new biomarkers of different diseases, mainly civilization diseases.

OBJECTIVE

We aimed to review studies reporting the application of mass spectrometry for lipid analysis in metabolic diseases.

METHOD

Following an extensive search of peer-reviewed articles on the mass spectrometry analysis of lipids the literature has been discussed in this review article.

RESULTS

The lipid group contains around 1.7 million species; they are totally different, in terms of the length of aliphatic chain, amount of rings, additional functional groups. Some of them are so complex that their complex analyses are a challenge for analysts. Their qualitative and quantitative analysis of is based mainly on mass spectrometry.

CONCLUSION

Mass spectrometry techniques are excellent tools for lipid profiling in complex biological samples and the combination with multivariate statistical analysis enables the identification of potential diagnostic biomarkers.

The metabolic fingerprints of HCV and HBV infections studied by Nuclear Magnetic Resonance Spectroscopy

Few studies are available on metabolic changes in liver injuries and this is the first metabolomic study evaluating a group of HCV-positive patients, before and after viral eradication via DAA IFN-free regimens, using ¹H-NMR to characterize and compare their serum fingerprints to naïve HBV-patients and healthy donors. The investigation clearly shows differences in the metabolomic profile of HCV patients before and after effective DAA treatment. Significant changes in metabolites levels in patients undergoing therapy suggest alterations in several metabolic pathways. It has been shown that ¹H-NMR fingerprinting approach is an optimal technique in predicting the specific infection and the healthy status of studied subjects (Monte-Carlo cross validated accuracies: 86% in the HCV vs HBV model, 98.7% in the HCV vs HC model). Metabolite data collected support the hypothesis that the HCV virus induces glycolysis over oxidative phosphorylation in a similar manner to the Warburg effect in cancer, moreover our results have demonstrated a different action of the two viruses on cellular metabolism, corroborating the hypothesis that the metabolic perturbation on patients could be attributed to a direct role in viral infection. This metabolomic study has revealed some alteration in metabolites for the first time (2-oxoglutarate and 3-hydroxybutrate) concerning the HCV-infection model that could explain several extrahepatic manifestations associated with such an infection.

NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer

Background

Combination of metabolomics and epidemiological approaches opens new perspectives for ground-breaking discoveries. The aim of the present study was to investigate for the first time whether plasma untargeted metabolomic profiles, established from a simple blood draw from healthy women, could contribute to predict the risk of developing breast cancer within the following decade and to better understand the aetiology of this complex disease.

Methods

A prospective nested case-control study was set up in the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, including 206 breast cancer cases diagnosed during a 13-year follow-up and 396 matched controls. Untargeted nuclear magnetic resonance (NMR) metabolomic profiles were established from baseline plasma samples. Multivariable conditional logistic regression models were computed for each individual NMR variable and for combinations of variables derived by principal component analysis.

Results

Several metabolomic variables from 1D NMR spectroscopy were associated with breast cancer risk. Women characterized by higher fasting plasma levels of valine, lysine, arginine, glutamine, creatine, creatinine and glucose, and lower plasma levels of lipoproteins, lipids, glycoproteins, acetone, glycerol-derived compounds and unsaturated lipids had a higher risk of developing breast cancer. P-values ranged from 0.00007 [odds ratio (OR)T3vsT1=0.37 (0.23-0.61) for glycerol-derived compounds] to 0.04 [ORT3vsT1=1.61 (1.02-2.55) for glutamine].

Conclusion

This study highlighted associations between baseline NMR plasma metabolomic signatures and long-term breast cancer risk. These results provide interesting insights to better understand complex mechanisms involved in breast carcinogenesis and evoke plasma metabolic disorders favourable for carcinogenesis initiation. This study may contribute to develop screening strategies for the identification of at-risk women for breast cancer well before symptoms appear.

Metabolic Profiling of Amino Acids Associated with Mortality in Patients with Acute Paraquat Poisoning

Background

Paraquat is a major cause of fatal poisoning after ingestion in many parts of Asia and the Pacific nations. However, optimal prognostic indicators to evaluate patient mortality have not been unequivocally established. Following acute paraquat poisoning, a number of amino acids (AA), are abnormally expressed in metabolic pathways. However, the alterations in AA metabolite levels after paraquat poisoning remain unknown in humans.

Material/Methods

In the present study, 40 patients were enrolled, of whom 16 survived and 24 died. A metabolomics approach was used to assess changes in AA metabolites in plasma and its potential prognostic value following paraquat poisoning. Mass spectrometry (MS) based on metabolite identification was conducted.

Results

Twenty-five AA levels in plasma were abnormally expressed in non-survivor patients. Among them, creatinine, indolelactate, and 3-(4-hydroxyphenyl)lactate were found to be highly correlated with paraquat death prediction. It was noted that the intensity levels of these 3 AA metabolites in the non-survivor group were substantially higher than in the survivor group. Furthermore, we examined receiver operating characteristic (ROC) curves for clinical validation. ROC results showed that 3-(4-hydroxyphenyl)lactate had the highest AUC of 0.84, while indolelactate and creatinine had AUCs of 0.75 and 0.83, respectively, suggesting that they can be used to predict the clinical outcome (although this methodology is expensive to implement).

Conclusions

Metabolic profiling of AA levels could be a reliable tool to identify effective indicators for the early high precision prognosis of paraquat poisoning.

KniMet: a pipeline for the processing of chromatography-mass spectrometry metabolomics data

INTRODUCTION

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

OBJECTIVES

Merge in the same platform the steps required for metabolomics data processing.

METHODS

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

RESULTS

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

CONCLUSION

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC-MS and LC-MS open profiling data.

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.

Metabolomics of aging assessed in individual parasitoid wasps

Metabolomics studies of low-biomass organisms, such as small insects, have previously relied on the pooling of biological samples to overcome detection limits, particularly using NMR. We show that the differentiation of metabolite profiles of individual 1 mg parasitoid wasps of different ages is possible when using a modified sample preparation and a combination of untargeted NMR and LC-MS based metabolomics. Changes were observed between newly emerged and older wasps in glycerolipids, amino acids and circulatory sugars. This advance in chemical profiling has important implications for the study of the behaviour and ecology of parasitoids and many other species of small organisms because predictions and observations are typically made at the level of the individual. Thus, the metabolomic state of low-biomass individuals can now be related to their behaviour and ecological performance. We discuss specifically the utility of age-related metabolomic profiling but our new approach can be applied to a wide range of biological research.

Metabolites involved in glycolysis and amino acid metabolism are altered in short children born small for gestational age

BACKGROUND

Later life metabolic dysfunction is a well-recognized consequence of being born small for gestational age (SGA). This study has applied metabolomics to identify whether there are changes in these pathways in prepubertal short SGA children and aimed to compare the intracellular and extracellular metabolome in fibroblasts derived from healthy children and SGA children with postnatal growth impairment.

METHODS

Skin fibroblast cell lines were established from eight SGA children (age 1.8-10.3 y) with failure of catch-up growth and from three healthy control children. Confluent cells were incubated in serum-free media and the spent growth medium (metabolic footprint), and intracellular metabolome (metabolic fingerprint) were analyzed by gas-chromatography mass spectrometry.

RESULTS

Nineteen metabolites were significantly altered between SGA and control cell lines. The greatest fold difference (FD) was seen for alanine (fingerprint FD, SGA: control 0.3, P = 0.01 and footprint FD = 0.19, P = 0.01), aspartic acid (fingerprint FD = 5.21, P = 0.01), and cystine (footprint FD = 1.66, P = 0.02). Network analysis of the differentially expressed metabolites predicted inhibition of insulin as well as growth (ERK) signaling in SGA cells.

CONCLUSION

This study indicates that changes in cellular metabolism associated with both growth failure and insulin insensitivity are present in prepubertal short children born SGA.

Metabolomics in diabetic complications

With a global prevalence of 9%, diabetes is the direct cause of millions of deaths each year and is quickly becoming a health crisis. Major long-term complications of diabetes arise from persistent oxidative stress and dysfunction in multiple metabolic pathways. The most serious complications involve vascular damage and include cardiovascular disease as well as microvascular disorders such as nephropathy, neuropathy, and retinopathy. Current clinical analyses like glycated hemoglobin and plasma glucose measurements hold some value as prognostic indicators of the severity of complications, but investigations into the underlying pathophysiology are still lacking. Advancements in biotechnology hold the key to uncovering new pathways and establishing therapeutic targets. Metabolomics, the study of small endogenous molecules, is a powerful toolset for studying pathophysiological processes and has been used to elucidate metabolic signatures of diabetes in various biological systems. Current challenges in the field involve correlating these biomarkers to specific complications to provide a better prediction of future risk and disease progression. This review will highlight the progress that has been made in the field of metabolomics including technological advancements, the identification of potential biomarkers, and metabolic pathways relevant to macro- and microvascular diabetic complications.

LC-MS-based metabolomics study of marine bacterial secondary metabolite and antibiotic production in Salinispora arenicola

An LC-MS-based metabolomics approach was used to characterise the variation in secondary metabolite production due to changes in the salt content of the growth media as well as across different growth periods (incubation times). We used metabolomics as a tool to investigate the production of rifamycins (antibiotics) and other secondary metabolites in the obligate marine actinobacterial species Salinispora arenicola, isolated from Great Barrier Reef (GBR) sponges, at two defined salt concentrations and over three different incubation periods. The results indicated that a 14 day incubation period is optimal for the maximum production of rifamycin B, whereas rifamycin S and W achieve their maximum concentration at 29 days. A "chemical profile" link between the days of incubation and the salt concentration of the growth medium was shown to exist and reliably represents a critical point for selection of growth medium and harvest time.

Metabolomics – the complementary field in systems biology: a review on obesity and type 2 diabetes

This paper highlights the metabolomic roles in systems biology towards the elucidation of metabolic mechanisms in obesity and type 2 diabetes.

Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study

BACKGROUND

Application of metabolite profiling could expand the etiological knowledge of type 2 diabetes mellitus (T2D). However, few prospective studies apply broad untargeted metabolite profiling to reveal the comprehensive metabolic alterations preceding the onset of T2D.

METHODS

We applied untargeted metabolite profiling in serum samples obtained from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort comprising 300 individuals who developed T2D after a median follow-up time of 6 years and 300 matched controls. For that purpose, we used ultraperformance LC-MS with a protocol specifically designed for large-scale metabolomics studies with regard to robustness and repeatability. After multivariate classification to select metabolites with the strongest contribution to disease classification, we applied multivariable-adjusted conditional logistic regression to assess the association of these metabolites with T2D.

RESULTS

Among several alterations in lipid metabolism, there was an inverse association with T2D for metabolites chemically annotated as lysophosphatidylcholine(dm16:0) and phosphatidylcholine(O-20:0/O-20:0). Hexose sugars were positively associated with T2D, whereas higher concentrations of a sugar alcohol and a deoxyhexose sugar reduced the odds of diabetes by approximately 60% and 70%, respectively. Furthermore, there was suggestive evidence for a positive association of the circulating purine nucleotide isopentenyladenosine-5'-monophosphate with incident T2D.

CONCLUSIONS

This study constitutes one of the largest metabolite profiling approaches of T2D biomarkers in a prospective study population. The findings might help generate new hypotheses about diabetes etiology and develop further targeted studies of a smaller number of potentially important metabolites.

Evidence that multiple defects in lipid regulation occur before hyperglycemia during the prodrome of type-2 diabetes

BACKGROUND

Blood-vessel dysfunction arises before overt hyperglycemia in type-2 diabetes (T2DM). We hypothesised that a metabolomic approach might identify metabolites/pathways perturbed in this pre-hyperglycemic phase. To test this hypothesis and for specific metabolite hypothesis generation, serum metabolic profiling was performed in young women at increased, intermediate and low risk of subsequent T2DM.

METHODS

Participants were stratified by glucose tolerance during a previous index pregnancy into three risk-groups: overt gestational diabetes (GDM; n = 18); those with glucose values in the upper quartile but below GDM levels (UQ group; n = 45); and controls (n = 43, below the median glucose values). Follow-up serum samples were collected at a mean 22 months postnatally. Samples were analysed in a random order using Ultra Performance Liquid Chromatography coupled to an electrospray hybrid LTQ-Orbitrap mass spectrometer. Statistical analysis included principal component (PCA) and multivariate methods.

FINDINGS

Significant between-group differences were observed at follow-up in waist circumference (86, 95%CI (79-91) vs 80 (76-84) cm for GDM vs controls, p<0.05), adiponectin (about 33% lower in GDM group, p = 0.004), fasting glucose, post-prandial glucose and HbA1c, but the latter 3 all remained within the 'normal' range. Substantial differences in metabolite profiles were apparent between the 2 'at-risk' groups and controls, particularly in concentrations of phospholipids (4 metabolites with p ≤ 0.01), acylcarnitines (3 with p ≤ 0.02), short- and long-chain fatty acids (3 with p<  = 0.03), and diglycerides (4 with p ≤ 0.05).

INTERPRETATION

Defects in adipocyte function from excess energy storage as relatively hypoxic visceral and hepatic fat, and impaired mitochondrial fatty acid oxidation may initiate the observed perturbations in lipid metabolism. Together with evidence from the failure of glucose-directed treatments to improve cardiovascular outcomes, these data and those of others indicate that a new, quite different definition of type-2 diabetes is required. This definition would incorporate disturbed lipid metabolism prior to hyperglycemia.

Potential of metabolomics in preclinical and clinical drug development

Metabolomics is an upcoming technology system which involves detailed experimental analysis of metabolic profiles. Due to its diverse applications in preclinical and clinical research, it became an useful tool for the drug discovery and drug development process. This review covers the brief outline about the instrumentation and interpretation of metabolic profiles. The applications of metabolomics have a considerable scope in the pharmaceutical industry, almost at each step from drug discovery to clinical development. These include finding drug target, potential safety and efficacy biomarkers and mechanisms of drug action, the validation of preclinical experimental models against human disease profiles, and the discovery of clinical safety and efficacy biomarkers. As we all know, nowadays the drug discovery and development process is a very expensive, and risky business. Failures at any stage of drug discovery and development process cost millions of dollars to the companies. Some of these failures or the associated risks could be prevented or minimized if there were better ways of drug screening, drug toxicity profiling and monitoring adverse drug reactions. Metabolomics potentially offers an effective route to address all the issues associated with the drug discovery and development.

Discovering the recondite secondary metabolome spectrum of Salinispora species: a study of inter-species diversity

Patterns of inter-species secondary metabolite production by bacteria can provide valuable information relating to species ecology and evolution. The complex nature of this chemical diversity has previously been probed via directed analyses of a small number of compounds, identified through targeted assays rather than more comprehensive biochemical profiling approaches such as metabolomics. Insights into ecological and evolutionary relationships within bacterial genera can be derived through comparative analysis of broader secondary metabolite patterns, and this can also eventually assist biodiscovery search strategies for new natural products. Here, we investigated the species-level chemical diversity of the two marine actinobacterial species Salinispora arenicola and Salinispora pacifica, isolated from sponges distributed across the Great Barrier Reef (GBR), via their secondary metabolite profiles using LC-MS-based metabolomics. The chemical profiles of these two species were obtained by UHPLC-QToF-MS based metabolic profiling. The resultant data were interrogated using multivariate data analysis methods to compare their (bio)chemical profiles. We found a high level of inter-species diversity in strains from these two bacterial species. We also found rifamycins and saliniketals were produced exclusively by S. arenicola species, as the main secondary metabolites differentiating the two species. Furthermore, the discovery of 57 candidate compounds greatly increases the small number of secondary metabolites previously known to be produced by these species. In addition, we report the production of rifamycin O and W, a key group of ansamycin compounds, in S. arenicola for the first time. Species of the marine actinobacteria harbour a much wider spectrum of secondary metabolites than suspected, and this knowledge may prove a rich field for biodiscovery as well as a database for understanding relationships between speciation, evolution and chemical ecology.

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

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

Metabolomics in the developing infant

PURPOSE OF REVIEW

The aim of this review is to update readers on the most recent publications concerning clinical metabolomics in developing infants.

RECENT FINDINGS

Only a limited number of neonatal and pediatric metabolomic studies have been published, in comparison to the adult. However, this number of pediatric and neonatal papers is constantly increasing. The latest papers are related to intrauterine growth restricted and small for gestational age neonates, prematurity, mode of delivery, hypoxic ischemic encephalopathy, persistent ductus arteriosus, respiratory syndrome and surfactant therapy, cytomegalovirus infection, nephrouropathy, inborn errors of metabolism, pharmametabolomics, and nutrimetabolomics (including study of maternal milk and formula). Also numerous papers have been presented in experimental neonatology. In particular, the fluids most frequently used were as follows: urine, cord blood plasma, but also milk and stools. Each condition or disease presents a specific discriminating set of metabolites, which can be considered like a 'bar code'.

SUMMARY

In the near future, improved tools for metabolomic analysis (like simplified 'dipsticks' for urine) and its integration with other 'omics' will make this technology available in the clinical setting, leading to better or easier clinical decision making. Urinary metabolomics will probably be one of the most used tools in pediatrics and the metabolome will be 'our world'.

Metabolomics in neonatology: fact or fiction?

The newest 'omics' science is metabolomics, the latest offspring of genomics, considered the most innovative of the 'omics' sciences. Metabolomics, also called the 'new clinical biochemistry', is an approach based on the systematic study of the complete set of metabolites in a biological sample. The metabolome is considered the most predictive phenotype and is capable of considering epigenetic differences. It is so close to the phenotype that it can be considered the phenotype itself. In the last three years about 5000 papers have been listed in PubMed on this topic, but few data are available in the newborn. The aim of this review, after a description of background and technical procedures, is to analyse the clinical applications of metabolomics in neonatology, covering the following points: gestational age, postnatal age, type of delivery, zygosity, perinatal asphyxia, intrauterine growth restriction, prenatal inflammation and brain injury, respiratory, cardiovascular renal, metabolic diseases; sepsis, necrotizing enterocolitis and antibiotic treatment; nutritional studies on maternal milk and formula, pharma-metabolomics, long-term diseases. Pros and cons of metabolomics are also discussed. All this comes about with the non-invasive collection of a few drops of urine (exceptionally important for the neonate, especially those of low birth weight). Only time and large-scale studies to validate initial results will place metabolomics within neonatology. In any case, it is important for perinatologists to learn and understand this new technology to offer their patients the utmost in diagnostic and therapeutic opportunities.

Metabolomics and ischaemic heart disease

Ischaemic heart disease accounts for nearly half of the global cardiovascular disease burden. Aetiologies relating to heart disease are complex, but dyslipidaemia, oxidative stress and inflammation are cardinal features. Despite preventative measures and advancements in treatment regimens with lipid-lowering agents, the high prevalence of heart disease and the residual risk of recurrent events continue to be a significant burden to the health sector and to the affected individuals and their families. The development of improved risk models for the early detection and prevention of cardiovascular events in addition to new therapeutic strategies to address this residual risk are required if we are to continue to make inroads into this most prevalent of diseases. Metabolomics and lipidomics are modern disciplines that characterize the metabolite and lipid complement respectively, of a given system. Their application to ischaemic heart disease has demonstrated utilities in population profiling, identification of multivariate biomarkers and in monitoring of therapeutic response, as well as in basic mechanistic studies. Although advances in magnetic resonance and mass spectrometry technologies have given rise to the fields of metabolomics and lipidomics, the plethora of data generated presents challenges requiring specific statistical and bioinformatics applications, together with appropriate study designs. Nonetheless, the predictive and re-classification capacity of individuals with various degrees of risk by the plasma lipidome has recently been demonstrated. In the present review, we summarize evidence derived exclusively by metabolomic and lipidomic studies in the context of ischaemic heart disease. We consider the potential role of plasma lipid profiling in assessing heart disease risk and therapeutic responses, and explore the potential mechanisms. Finally, we highlight where metabolomic studies together with complementary -omic disciplines may make further inroads into the understanding, detection and treatment of ischaemic heart disease.

Application of metabolomics approaches to the study of respiratory diseases

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system, under a given set of conditions. These methods offer the potential for a holistic approach to clinical medicine, as well as improving disease diagnosis and understanding of pathological mechanisms. Respiratory diseases including asthma and chronic obstructive pulmonary disorder are increasing globally, with the latter predicted to become the third leading cause of global mortality by 2020. The root causes for disease onset remain poorly understood and no cures are available. This review presents an overview of metabolomics followed by in-depth discussion of its application to the study of respiratory diseases, including the design of metabolomics experiments, choice of clinical material collected and potentially confounding experimental factors. Particular challenges in the field are presented and placed within the context of the future of the applications of metabolomics approaches to the study of respiratory diseases.

Metabolic changes in urine during and after pregnancy in a large, multiethnic population-based cohort study of gestational diabetes

This study aims to identify novel markers for gestational diabetes (GDM) in the biochemical profile of maternal urine using NMR metabolomics. It also catalogs the general effects of pregnancy and delivery on the urine profile. Urine samples were collected at three time points (visit V1: gestational week 8-20; V2: week 28±2; V3 10-16 weeks post partum) from participants in the STORK Groruddalen program, a prospective, multiethnic cohort study of 823 healthy, pregnant women in Oslo, Norway, and analyzed using (1)H-NMR spectroscopy. Metabolites were identified and quantified where possible. PCA, PLS-DA and univariate statistics were applied and found substantial differences between the time points, dominated by a steady increase of urinary lactose concentrations, and an increase during pregnancy and subsequent dramatic reduction of several unidentified NMR signals between 0.5 and 1.1 ppm. Multivariate methods could not reliably identify GDM cases based on the WHO or graded criteria based on IADPSG definitions, indicating that the pattern of urinary metabolites above micromolar concentrations is not influenced strongly and consistently enough by the disease. However, univariate analysis suggests elevated mean citrate concentrations with increasing hyperglycemia. Multivariate classification with respect to ethnic background produced weak but statistically significant models. These results suggest that although NMR-based metabolomics can monitor changes in the urinary excretion profile of pregnant women, it may not be a prudent choice for the study of GDM.

Quantitative metabolomic and lipidomic profiling reveals aberrant amino acid metabolism in type 2 diabetes

Type 2 diabetes (T2DM) is a multi-factorial disease with a complex pathogenic mechanism; however a complete understanding of precise biochemical alterations accompanying the onset and progression of T2DM is lacking. Using a combination of untargeted and targeted metabolomic profiling approach we were able to delineate significantly altered metabolites in the diabetic (T2DM) group. Our results indicate significant perturbations in amino acid metabolism, TCA cycle and glycerol-phospholipid metabolism possibly impacting the overall glucose homeostasis in T2DM. A systems approach offers promise towards identification of clinically relevant markers of T2DM and novel molecular targets to foster drug discovery for effective therapeutic development for diabetes.

Global metabolic profiling of animal and human tissues via UPLC-MS

Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ~48 h: 1 h for system setup, 8-10 h for sample preparation, 34 h for UPLC-MS analysis and 2-3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.

Dupuytren's disease metabolite analyses reveals alterations following initial short-term fibroblast culturing

Dupuytren's disease (DD) is an ill-defined fibroproliferative disorder affecting the palm of the hand, resulting in progressive and irreversible digital contracture. In view of the abnormal gene dysregulation found in DD, and its potential effect on metabolites at a functional level, we chose to examine the metabolic profile involved in DD. Using Fourier transform infrared (FT-IR) spectroscopy to generate metabolic fingerprints of cultured cells, we compared the profiles of DD cords and nodules (1) against the unaffected transverse palmar fascia (internal control), (2) against carpal ligamentous fascia (external control), and (3) against fibroblasts from fat surrounding the nodule and skin overlying the nodule (environmental control). We also determined the effects of serial passaging of the cells on DD fingerprints. Subsequently, gas chromatography-mass spectrometry (GC-MS) was employed for metabolic profiling in order to identify metabolites characteristic of the DD tissue phenotypes. We developed a robust metabolomic analysis procedure of DD using cultured fibroblasts derived from DD tissues. Our carefully controlled culture conditions, combined with assessment of metabolic phenotypes by FT-IR and GC-MS, enabled us to demonstrate metabolic differences between DD and unaffected transverse palmar fascia and between DD and healthy control tissue. In early passage (0-3) the metabolic differences were clear, but cells from subsequent passages (4-6) started to lose this distinction between diseased and non-diseased origin. The dysregulated metabolites we identified were leucine, phenylalanine, lysine, cysteine, aspartic acid, glycerol-3-phosphate and the vitamin precursor to coenzyme A. Early passage DD cells exhibit a clear metabolic profile, in which central metabolic pathways appear to be involved. Experimental conditions have been identified in which these DD data are reproducible. The experimental reproducibility will be useful in DD diagnostics and for DD systems biology.

NMR metabolic profiling of serum identifies amino acid disturbances in chronic fatigue syndrome

Chronic fatigue syndrome (CFS) is a debilitating multisystem disorder characterised by long-term fatigue with a variety of other symptoms including cognitive dysfunction, unrefreshing sleep, muscle pain, and post-exertional malaise. It is a poorly understood condition that occurs in ~5 in every 1000 individuals. We present here a preliminary study on the analysis of blood samples from 11 CFS and 10 control subjects through NMR metabolic profiling. Identified metabolites that were found to be significantly altered between the groups were subjected to correlation analysis to potentially elucidate disturbed metabolic pathways. Our results showed a significant reduction of glutamine (P=0.002) and ornithine (P<0.05) in the blood of the CFS samples. Correlation analysis of glutamine and ornithine with other metabolites in the CFS sera showed relationships with glucogenic amino acids and metabolites that participate in the urea cycle. This indicates a possible disturbance to amino acid and nitrogen metabolism. It would be beneficial to identify any potential biomarkers of CFS for accurate diagnosis of the disorder.

Metabolomic analysis of rat serum in streptozotocin-induced diabetes and after treatment with oral triethylenetetramine (TETA)

Background

The prevalence, and associated healthcare burden, of diabetes mellitus is increasing worldwide. Mortality and morbidity are associated with diabetic complications in multiple organs and tissues, including the eye, kidney and cardiovascular system, and new therapeutics to treat these complications are required urgently. Triethylenetetramine (TETA) is one such experimental therapeutic that acts to chelate excess copper (II) in diabetic tissues and reduce oxidative stress and cellular damage.

Methods

Here we have performed two independent metabolomic studies of serum to assess the suitability of the streptozotocin (STZ)-induced rat model for studying diabetes and to define metabolite-related changes associated with TETA treatment. Ultraperformance liquid chromatography-mass spectrometry studies of serum from non-diabetic/untreated, non-diabetic/TETA-treated, STZ-induced diabetic/untreated and STZ-induced diabetic/TETA-treated rats were performed followed by univariate and multivariate analysis of data.

Results

Multiple metabolic changes related to STZ-induced diabetes, some of which have been reported previously in other animal and human studies, were observed, including changes in amino acid, fatty acid, glycerophospholipid and bile acid metabolism. Correlation analysis suggested that treatment with TETA led to a reversal of diabetes-associated changes in bile acid, fatty acid, steroid, sphingolipid and glycerophospholipid metabolism and proteolysis.

Conclusions

Metabolomic studies have shown that the STZ-induced rat model of diabetes is an appropriate model system to undertake research into diabetes and potential therapies as several metabolic changes observed in humans and other animal models were also observed in this study. Metabolomics has also identified several biological processes and metabolic pathways implicated in diabetic complications and reversed following treatment with the experimental therapeutic TETA.