Key elements of metabolomics in the study of biomarkers of diabetes

Identifying critical age and gender-based metabolomic shifts in a Japanese population of the Tohoku Medical Megabank cohort

Understanding the physiological changes associated with aging and the associated disease risks is essential to establish biomarkers as indicators of biological aging. This study used the NMR-measured plasma metabolome to calculate age-specific metabolite indices. In doing so, the scope of the study was deliberately simplified to capture general trends and insights into age-related changes in metabolic patterns. In addition, changes in metabolite concentrations with age were examined in detail, with the period from 55-59 to 60-64 years being a period of significant metabolic change, particularly in men, and from 45-49 to 50-54 years in females. These results illustrate the different variations in metabolite concentrations by sex and provide new insights into the relationship between age and metabolic diseases.

Egg white-derived peptides reduced blood glucose in high-fat-diet and low-dose streptozotocin-induced type 2 diabetic mice via regulating the hepatic gluconeogenic signaling and metabolic profile

Food proteins are considered an ideal source for the identification of bioactive peptides with the potential to intervene in nutrition-related chronic diseases such as cardiovascular disease, obesity, and diabetes. Egg white-derived peptides (EWPs) have been shown to improve glucose tolerance in insulin-resistant rats. However, underlying mechanisms are to be elucidated. Therefore, we hypothesized that EWP exerts a hypoglycemic effect by regulating hepatic glucose homeostasis. Our results showed that 7 weeks of EWP treatment reduced the fasting blood glucose in T2DM mice and the inhibition of the liver gluconeogenic pathway was involved in the mechanisms of actions. Using the untargeted metabolomics technique, we found that EWP treatment also altered the hepatic metabolic profile in T2DM mice, in which, the role of fatty acid esters of hydroxy fatty acids in mediating the hypoglycemic effect of EWPs might be pivotal.

Effect of DHA-Enriched Phospholipids from Fish Roe on Rat Fecal Metabolites: Untargeted Metabolomic Analysis

Lipid metabolism disorder has become an important hidden danger threatening human health, and various supplements to treat lipid metabolism disorder have been studied. Our previous studies have shown that DHA-enriched phospholipids from large yellow croaker (Larimichthys Crocea) roe (LYCRPLs) have lipid-regulating effects. To better explain the effect of LYCRPLs on lipid regulation in rats, the fecal metabolites of rats were analyzed from the level of metabolomics in this study, and GC/MS metabolomics measurements were performed to figure out the effect of LYCRPLs on fecal metabolites in rats. Compared with the control (K) group, 101 metabolites were identified in the model (M) group. There were 54, 47, and 57 metabolites in the low-dose (GA), medium-dose (GB), and high-dose (GC) groups that were significantly different from that of group M, respectively. Eighteen potential biomarkers closely related to lipid metabolism were screened after intervention with different doses of LYCRPLs on rats, which were classified into several metabolic pathways in rats, including pyrimidine metabolism, the citric acid cycle (TCA cycle), the metabolism of L-cysteine, carnitine synthesis, pantothenate and CoA biosynthesis, glycolysis, and bile secretion. L-cysteine was speculated to be a useful biomarker of LYCRPLs acting on rat fecal metabolites. Our findings indicated that LYCRPLs may regulate lipid metabolism disorders in SD rats by activating these metabolic pathways.

Metabolomics profiling distinctively identified end-stage renal disease patients from chronic kidney disease patients

Chronic kidney disease (CKD) is a serious public health problem characterized by progressive kidney function loss leading to end-stage renal disease (ESRD) that demands dialysis or kidney transplantation. Early detection can prevent or delay progression to ESRD. The study aimed to gain new insights into the perturbed biochemical reactions and to identify novel distinct biomarkers between ESRD and CKD. Serum samples of 32 patients with ESRD (n = 13) and CKD (n = 19) were analyzed using chemical isotope labeling liquid chromatography-mass spectrometry metabolomics approach. A total of 193 metabolites were significantly altered in ESRD compared to CKD and were mainly involved in aminoacyl-tRNA biosynthesis, branched-chain amino acid (BCAA) biosynthesis, taurine metabolism, and tryptophan metabolism. Three kynurenine derivatives, namely, 2-aminobenzoic acid, xanthurenic acid, and hydroxypicolinic acid were upregulated in ESRD compared to CKD due to the significant decrease in glomerular filtration rate with the progression of CKD to ESRD. N-Hydroxy-isoleucine, 2-aminobenzoic acid, and picolinic acid yielded AUC > 0.99 when analyzed using Receiver Operating Characteristic (ROC) analysis. Our findings suggest that inhibiting the kynurenine pathway might be a promising target to delay CKD progression and that metabolites with high discriminative ability might serve as potential prognostic biomarkers to monitor the progression of CKD to ESRD or used in combination with current markers to indicate the status of kidney damage better.

Changes in Plasma Metabolomic Profile Following Bariatric Surgery, Lifestyle Intervention or Diet Restriction-Insights from Human and Rat Studies

Although bariatric surgery is known to change the metabolome, it is unclear if this is specific for the intervention or a consequence of the induced bodyweight loss. As the weight loss after Roux-en-Y Gastric Bypass (RYGB) can hardly be mimicked with an evenly effective diet in humans, translational research efforts might be helpful. A group of 188 plasma metabolites of 46 patients from the randomized controlled Würzburg Adipositas Study (WAS) and from RYGB-treated rats (n = 6) as well as body-weight-matched controls (n = 7) were measured using liquid chromatography tandem mass spectrometry. WAS participants were randomized into intensive lifestyle modification (LS, n = 24) or RYGB (OP, n = 22). In patients in the WAS cohort, only bariatric surgery achieved a sustained weight loss (BMI -34.3% (OP) vs. -1.2% (LS), p ≤ 0.01). An explicit shift in the metabolomic profile was found in 57 metabolites in the human cohort and in 62 metabolites in the rodent model. Significantly higher levels of sphingolipids and lecithins were detected in both surgical groups but not in the conservatively treated human and animal groups. RYGB leads to a characteristic metabolomic profile, which differs distinctly from that following non-surgical intervention. Analysis of the human and rat data revealed that RYGB induces specific changes in the metabolome independent of weight loss.

Deep Metabolic Profiling Assessment of Tissue Extraction Protocols for Three Model Organisms

Metabolic profiling harbors the potential to better understand various disease entities such as cancer, diabetes, Alzheimer’s, Parkinson’s disease or COVID-19. To better understand such diseases and their intricate metabolic pathways in human studies, model animals are regularly used. There, standardized rearing conditions and uniform sampling strategies are prerequisites towards a successful metabolomic study that can be achieved through model organisms. Although metabolomic approaches have been employed on model organisms before, no systematic assessment of different conditions to optimize metabolite extraction across several organisms and sample types has been conducted. We address this issue using a highly standardized metabolic profiling assay analyzing 630 metabolites across three commonly used model organisms (Drosophila, mouse, and zebrafish) to find an optimal extraction protocol for various matrices. Focusing on parameters such as metabolite coverage, concentration and variance between replicates we compared seven extraction protocols. We found that the application of a combination of 75% ethanol and methyl tertiary-butyl ether (MTBE), while not producing the broadest coverage and highest concentrations, was the most reproducible extraction protocol. We were able to determine up to 530 metabolites in mouse kidney samples, 509 in mouse liver, 422 in zebrafish and 388 in Drosophila and discovered a core overlap of 261 metabolites in these four matrices. To enable other scientists to search for the most suitable extraction protocol in their experimental context and interact with this comprehensive data, we have integrated our data set in the open-source shiny app “MetaboExtract”. Hereby, scientists can search for metabolites or compound classes of interest, compare them across the different tested extraction protocols and sample types as well as find reference concentration values.

Investigation of energy metabolic dynamism in hyperthermia-resistant ovarian and uterine cancer cells under heat stress

Despite progress in the use of hyperthermia in clinical practice, the thermosensitivity of cancer cells is poorly understood. In a previous study, we found that sensitivity to hyperthermia varied between ovarian and uterine cancer cell lines. Upon hyperthermia, glycolytic enzymes decreased in hyperthermia-resistant SKOV3 cells. However, the mechanisms of glycolysis inhibition and their relationship with thermoresistance remain to be explored. In this study, metabolomic analysis indicated the downregulation of glycolytic metabolites in SKOV3 cells after hyperthermia. Proteomic and pathway analyses predicted that the ubiquitin pathway was explicitly activated in resistant SKOV3 cells, compared with hyperthermia-sensitive A2780 cells, and STUB1, a ubiquitin ligase, potentially targeted PKM, a glycolytic rate-limiting enzyme. PKM is degraded via ubiquitination upon hyperthermia. Although glycolysis is inactivated by hyperthermia, ATP production is maintained. We observed that oxygen consumption and mitochondrial membrane potential were activated in SKOV3 cells but suppressed in A2780 cells. The activation of mitochondria could compensate for the loss of ATP production due to the suppression of glycolysis by hyperthermia. Although the physiological significance has not yet been elucidated, our results demonstrated that metabolomic adaptation from the Warburg effect to mitochondrial oxidative phosphorylation could contribute to thermoresistance in ovarian and uterine cancer cells.

Distinctive Metabolomics Patterns Associated With Insulin Resistance and Type 2 Diabetes Mellitus

Obesity is associated with an increased risk of insulin resistance (IR) and type 2 diabetes mellitus (T2DM) which is a multi-factorial disease associated with a dysregulated metabolism and can be prevented in pre-diabetic individuals with impaired glucose tolerance. A metabolomic approach emphasizing metabolic pathways is critical to our understanding of this heterogeneous disease. This study aimed to characterize the serum metabolomic fingerprint and multi-metabolite signatures associated with IR and T2DM. Here, we have used untargeted high-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) to identify candidate biomarkers of IR and T2DM in sera from 30 adults of normal weight, 26 obese adults, and 16 adults newly diagnosed with T2DM. Among the 3633 peak pairs detected, 62% were either identified or matched. A group of 78 metabolites were up-regulated and 111 metabolites were down-regulated comparing obese to lean group while 459 metabolites were up-regulated and 166 metabolites were down-regulated comparing T2DM to obese groups. Several metabolites were identified as IR potential biomarkers, including amino acids (Asn, Gln, and His), methionine (Met) sulfoxide, 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, serotonin, L-2-amino-3-oxobutanoic acid, and 4,6-dihydroxyquinoline. T2DM was associated with dysregulation of 42 metabolites, including amino acids, amino acids metabolites, and dipeptides. In conclusion, these pilot data have identified IR and T2DM metabolomics panels as potential novel biomarkers of IR and identified metabolites associated with T2DM, with possible diagnostic and therapeutic applications. Further studies to confirm these associations in prospective cohorts are warranted.

Metabolomics in Sleep, Insomnia and Sleep Apnea

Sleep-wake disorders are highly prevalent disorders, which can lead to negative effects on cognitive, emotional and interpersonal functioning, and can cause maladaptive metabolic changes. Recent studies support the notion that metabolic processes correlate with sleep. The study of metabolite biomarkers (metabolomics) in a large-scale manner offers unique opportunities to provide insights into the pathology of diseases by revealing alterations in metabolic pathways. This review aims to summarize the status of metabolomic analyses-based knowledge on sleep disorders and to present knowledge in understanding the metabolic role of sleep in psychiatric disorders. Overall, findings suggest that sleep-wake disorders lead to pronounced alterations in specific metabolic pathways, which might contribute to the association of sleep disorders with other psychiatric disorders and medical conditions. These alterations are mainly related to changes in the metabolism of branched-chain amino acids, as well as glucose and lipid metabolism. In insomnia, alterations in branched-chain amino acid and glucose metabolism were shown among studies. In obstructive sleep apnea, biomarkers related to lipid metabolism seem to be of special importance. Future studies are needed to examine severity, subtypes and treatment of sleep-wake disorders in the context of metabolite levels.

Metabolomics Identifies a Biomarker Revealing In Vivo Loss of Functional β-Cell Mass Before Diabetes Onset

Identification of individuals with decreased functional β-cell mass is essential for the prevention of diabetes. However, in vivo detection of early asymptomatic β-cell defect remains unsuccessful. Metabolomics has emerged as a powerful tool in providing readouts of early disease states before clinical manifestation. We aimed at identifying novel plasma biomarkers for loss of functional β-cell mass in the asymptomatic prediabetes stage. Nontargeted and targeted metabolomics were applied in both lean β-Phb2^-/- (β-cell-specific prohibitin-2 knockout) mice and obese db/db (leptin receptor mutant) mice, two distinct mouse models requiring neither chemical nor dietary treatments to induce spontaneous decline of functional β-cell mass promoting progressive diabetes development. Nontargeted metabolomics on β-Phb2^-/- mice identified 48 and 82 significantly affected metabolites in liver and plasma, respectively. Machine learning analysis pointed to deoxyhexose sugars consistently reduced at the asymptomatic prediabetes stage, including in db/db mice, showing strong correlation with the gradual loss of β-cells. Further targeted metabolomics by gas chromatography-mass spectrometry uncovered the identity of the deoxyhexose, with 1,5-anhydroglucitol displaying the most substantial changes. In conclusion, this study identified 1,5-anhydroglucitol as associated with the loss of functional β-cell mass and uncovered metabolic similarities between liver and plasma, providing insights into the systemic effects caused by early decline in β-cells.

Metabolomics and Lipidomics Profiling Reveals Hypocholesterolemic and Hypolipidemic Effects of Arabinoxylan on Type 2 Diabetic Rats

Type 2 diabetes (T2D) is a pandemic disease chiefly characterized by hyperglycemia. In this study, the combination of serum lipidomic and metabolomic approach was employed to investigate the effect of arabinoxylan on type 2 diabetic rats and identify the critical biomarkers of T2D. Metabolomics analysis revealed that branched-chain amino acids, 12α-hydroxylated bile acids, ketone bodies, and several short- and long-chain acylcarnitines were significantly increased in T2D, whereas lysophosphatidylcholines (LPCs) were significantly decreased. Lipidomics analysis indicated T2D-related dyslipidemia was mainly associated with the increased levels of acetylcarnitine, free fatty acids (FFA), diacylglycerols, triacylglycerols, and cholesteryl esters and the decreased levels of some unsaturated phosphatidylcholines (less than 22 carbons). These variations indicated the disturbed amino acid and lipid metabolism in T2D, and the accumulation of incompletely oxidized lipid species might eventually contribute to impaired insulin action and glucose homeostasis. Arabinoxylan treatment decreased the concentrations of 12α-hydroxylated bile acids, carnitines, and FFAs and increased the levels of LPCs. The improved bile acid and lipid metabolism by arabinoxylan might be involved in the alleviation of hypercholesterolemia and hyperlipidemia in T2D.

Metabolomics study in severe extracranial carotid artery stenosis

BACKGROUND

Significant genetic association has been found in patients with severe carotid artery stenosis (CAS). The present study wished to investigate if metabolites may also act as biomarkers for CAS.

METHODS

Consecutive patients with at least one carotid artery stenosis > = 60% on cerebral angiography were prospectively recruited from May 2007 to January 2016. Normal controls were recruited from outpatient clinic who had no stroke and coronary artery disease (CAD) history, and the brain magnetic resonance or computed tomographic angiography showed bilateral CAS < 30%. Risk factor profile, clinical characteristics, age, and clinical features were recorded. All subjects were male, and none had diabetes. ¹H-NMR spectroscopy-based metabolomics analysis was carried out for plasma samples.

RESULTS

Totally, 130 male subjects were recruited. Age had no significant difference between the controls and CAS group (60.2 ± 5.9 vs. 63.3 ± 6.0, p = 0.050). The CAS group had significantly higher frequency of CAD, hypertension, smoking and alcohol but lower body mass index than the controls (p < 0.05). The laboratory tests showed CAS group had significantly higher level of homocysteine but lower levels of cholesterol, high-density lipoprotein and hemoglobin than the controls (p < 0.05). The ¹H-NMR based plasma metabolomics analysis indicated that choline was significantly lower in CAS patients. The VIP values of lipids were greater than 1.0, which were considered significantly different.

CONCLUSIONS

Our results suggest homocysteine, choline and lipids in association with traditional risk factors may be involved in the pathogenesis of CAS. Diet adjustment to control homocysteine, choline and lipids may be helpful for the prevention of CAS.

Linking of metabolomic biomarkers with cardiometabolic health in Chinese population

Due to rapid nutrition transitions, the prevalence of cardiometabolic diseases, such as metabolic syndrome, type 2 diabetes, and cardiovascular diseases, has been increasing at an alarming rate in the Chinese population. Moreover, Asians, including Chinese, have been hypothesized to have a higher susceptibility to cardiometabolic diseases than Caucasians. Early prediction and prevention are key to controlling this epidemic trend; to this end, the identification of novel biomarkers is critical to reflect environmental exposure, as well as to reveal endogenous metabolic and pathophysiologic mechanisms. The emerging "omics" technologies, especially metabolomics, offer a unique opportunity to provide novel signatures or fingerprints to understand the effects of genetic and non-genetic factors on cardiometabolic health. During the past two decades, metabolomic approaches have been increasingly used in various epidemiological studies, primarily in Western populations. Although the field is still in its early stages, some studies have tried to identify novel compounds or confirm their metabolites and associations with cardiometabolic diseases in Chinese populations, including amino acids, fatty acids, acylcarnitines and other metabolites. Despite major efforts to discover novel biomarkers for disease prediction or intervention, the limits in current study design, analytical platforms, and data processing approaches are challenges in metabolomic research worldwide. Therefore, future research with more advanced technologies, rigorous study designs, standardized detection and analytic approaches, and integrated data from multiomics approaches are essential to evaluate the feasibility of using metabolomics in clinical settings. Finally, the functional roles and underlying biological mechanisms of metabolomic biomarkers should be elucidated by future mechanistic research.

Human White Adipose Tissue Metabolome: Current Perspective

OBJECTIVE

Interest in metabolites produced by adipose tissue has increased substantially in the past several decades. Previously regarded as an inert energy storage depot, adipose tissue is now viewed as a complex metabolically active organ with considerable impact on human health. The emerging field of mass spectrometry-based metabolomics presents a powerful tool for the study of processes in complex biological matrices including adipose tissue.

RESULTS

A large number of structurally distinct metabolites can be analyzed to facilitate the investigation of differences between physiological and pathophysiological metabolic profiles associated with adipose tissue. Understanding the molecular basis of adipose tissue regulation can thereby provide insight into the monitoring of obesity-related metabolic disorders and lead to the development of novel diagnostic and prognostic biomarkers.

CONCLUSIONS

This review provides the current state of knowledge, recent progress, and critical evaluation of metabolomics approaches in the context of white adipose tissue and obesity. An overview of basic principles and resources describing individual groups of metabolites analyzed in white adipose tissue and biological fluids is given. The focus is on metabolites that can serve as reliable biomarkers indicative of metabolic alterations associated with obesity.

Activation of Nrf2 signaling by natural products-can it alleviate diabetes?

Type 2 diabetes mellitus (DM) has reached pandemic proportions and effective prevention strategies are wanted. Its onset is accompanied by cellular distress, the nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription factor boosting cytoprotective responses, and many phytochemicals activate Nrf2 signaling. Thus, Nrf2 activation by natural products could presumably alleviate DM. We summarize function, regulation and exogenous activation of Nrf2, as well as diabetes-linked and Nrf2-susceptible forms of cellular stress. The reported amelioration of insulin resistance, β-cell dysfunction and diabetic complications by activated Nrf2 as well as the status quo of Nrf2 in precision medicine for DM are reviewed.

Metabolic Clustering Analysis as a Strategy for Compound Selection in the Drug Discovery Pipeline for Leishmaniasis

A lack of viable hits, increasing resistance, and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimize prioritization and accelerate the discovery process, a strategy to cluster compounds based on more than chemical structure is required. We show the power of metabolomics in comparing effects on metabolism of 28 different candidate treatments for Leishmaniasis (25 from the GSK Leishmania box, two analogues of Leishmania box series, and amphotericin B as a gold standard treatment), tested in the axenic amastigote form of Leishmania donovani. Capillary electrophoresis-mass spectrometry was applied to identify the metabolic profile of Leishmania donovani, and principal components analysis was used to cluster compounds on potential mode of action, offering a medium throughput screening approach in drug selection/prioritization. The comprehensive and sensitive nature of the data has also made detailed effects of each compound obtainable, providing a resource to assist in further mechanistic studies and prioritization of these compounds for the development of new antileishmanial drugs.

Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: A cross-sectional study of PREDIMED trial participants

AIM

To characterize the urinary metabolomic fingerprint and multi-metabolite signature associated with type 2 diabetes (T2D), and to classify the population into metabotypes related to T2D.

METHODS

A metabolomics analysis using the ¹H-NMR-based, non-targeted metabolomic approach was conducted to determine the urinary metabolomic fingerprint of T2D compared with non-T2D participants in the PREDIMED trial. The discriminant metabolite fingerprint was subjected to logistic regression analysis and ROC analyses to establish and to assess the multi-metabolite signature of T2D prevalence, respectively. Metabotypes associated with T2D were identified using the k-means algorithm.

RESULTS

A total of 33 metabolites were significantly different (P<0.05) between T2D and non-T2D participants. The multi-metabolite signature of T2D comprised high levels of methylsuccinate, alanine, dimethylglycine and guanidoacetate, and reduced levels of glutamine, methylguanidine, 3-hydroxymandelate and hippurate, and had a 96.4% AUC, which was higher than the metabolites on their own and glucose. Amino-acid and carbohydrate metabolism were the main metabolic alterations in T2D, and various metabotypes were identified in the studied population. Among T2D participants, those with a metabotype of higher levels of phenylalanine, phenylacetylglutamine, p-cresol and acetoacetate had significantly higher levels of plasma glucose.

CONCLUSION

The multi-metabolite signature of T2D highlights the altered metabolic fingerprint associated mainly with amino-acid, carbohydrate and microbiota metabolism. Metabotypes identified in this patient population could be related to higher risk of long-term cardiovascular events and therefore require further studies. Metabolomics is a useful tool for elucidating the metabolic complexity and interindividual variation in T2D towards the development of stratified precision nutrition and medicine. Trial registration at www.controlled-trials.com: ISRCTN35739639.

High-throughput extraction and quantification method for targeted metabolomics in murine tissues

INTRODUCTION

Global metabolomics analyses using body fluids provide valuable results for the understanding and prediction of diseases. However, the mechanism of a disease is often tissue-based and it is advantageous to analyze metabolomic changes directly in the tissue. Metabolomics from tissue samples faces many challenges like tissue collection, homogenization, and metabolite extraction.

OBJECTIVES

We aimed to establish a metabolite extraction protocol optimized for tissue metabolite quantification by the targeted metabolomics AbsoluteIDQ™ p180 Kit (Biocrates). The extraction method should be non-selective, applicable to different kinds and amounts of tissues, monophasic, reproducible, and amenable to high throughput.

METHODS

We quantified metabolites in samples of eleven murine tissues after extraction with three solvents (methanol, phosphate buffer, ethanol/phosphate buffer mixture) in two tissue to solvent ratios and analyzed the extraction yield, ionization efficiency, and reproducibility.

RESULTS

We found methanol and ethanol/phosphate buffer to be superior to phosphate buffer in regard to extraction yield, reproducibility, and ionization efficiency for all metabolites measured. Phosphate buffer, however, outperformed both organic solvents for amino acids and biogenic amines but yielded unsatisfactory results for lipids. The observed matrix effects of tissue extracts were smaller or in a similar range compared to those of human plasma.

CONCLUSION

We provide for each murine tissue type an optimized high-throughput metabolite extraction protocol, which yields the best results for extraction, reproducibility, and quantification of metabolites in the p180 kit. Although the performance of the extraction protocol was monitored by the p180 kit, the protocol can be applicable to other targeted metabolomics assays.

Metabolomics applied to diabetes-lessons from human population studies

The 'classical' distribution of type 2 diabetes (T2D) across the globe is rapidly changing and it is no longer predominantly a disease of middle-aged/elderly adults of western countries, but it is becoming more common through Asia and the Middle East, as well as increasingly found in younger individuals. This global altered incidence of T2D is most likely associated with the spread of western diets and sedentary lifestyles, although there is still much debate as to whether the increased incidence rates are due to an overconsumption of fats, sugars or more generally high-calorie foods. In this context, understanding the interactions between genes of risk and diet and how they influence the incidence of T2D will help define the causative pathways of the disease. This review focuses on the use of metabolomics in large cohort studies to follow the incidence of type 2 diabetes in different populations. Such approaches have been used to identify new biomarkers of pre-diabetes, such as branch chain amino acids, and associate metabolomic profiles with genes of known risk in T2D from large scale GWAS studies. As the field develops, there are also examples of meta-analysis across metabolomics cohort studies and cross-comparisons with different populations to allow us to understand how genes and diet contribute to disease risk. Such approaches demonstrate that insulin resistance and T2D have far reaching metabolic effects beyond raised blood glucose and how the disease impacts systemic metabolism.

Endocrinology Meets Metabolomics: Achievements, Pitfalls, and Challenges

The metabolome, although very dynamic, is sufficiently stable to provide specific quantitative traits related to health and disease. Metabolomics requires balanced use of state-of-the-art study design, chemical analytics, biostatistics, and bioinformatics to deliver meaningful answers to contemporary questions in human disease research. The technology is now frequently employed for biomarker discovery and for elucidating the mechanisms underlying endocrine-related diseases. Metabolomics has also enriched genome-wide association studies (GWAS) in this area by providing functional data. The contributions of rare genetic variants to metabolome variance and to the human phenotype have been underestimated until now.