Metabolic profiling in diabetes

Untargeted serum metabolic profiling of diabetes mellitus among Parkinson's disease patients

Type 2 diabetes mellitus (T2DM) is a common comorbidity among Parkinson's disease (PD) patients. Yet, little is known about dysregulated pathways that are unique in PD patients with T2DM. We applied high-resolution metabolomic profiling in serum samples of 636 PD and 253 non-PD participants recruited from Central California. We conducted an initial discovery metabolome-wide association and pathway enrichment analysis. After adjusting for multiple testing, in positive (or negative) ion mode, 30 (25) metabolic features were associated with T2DM in both PD and non-PD participants, 162 (108) only in PD participants, and 32 (7) only in non-PD participants. Pathway enrichment analysis identified 17 enriched pathways associated with T2DM in both the PD and non-PD participants, 26 pathways only in PD participants, and 5 pathways only in non-PD participants. Several amino acid, nucleic acids, and fatty acid metabolisms were associated with T2DM only in the PD patient group suggesting a possible link between PD and T2DM.

The role of exercise and hypoxia on glucose transport and regulation

Muscle glucose transport activity increases with an acute bout of exercise, a process that is accomplished by the translocation of glucose transporters to the plasma membrane. This process remains intact in the skeletal muscle of individuals with insulin resistance and type 2 diabetes mellitus (T2DM). Exercise training is, therefore, an important cornerstone in the management of individuals with T2DM. However, the acute systemic glucose responses to carbohydrate ingestion are often augmented during the early recovery period from exercise, despite increased glucose uptake into skeletal muscle. Accordingly, the first aim of this review is to summarize the knowledge associated with insulin action and glucose uptake in skeletal muscle and apply these to explain the disparate responses between systemic and localized glucose responses post-exercise. Herein, the importance of muscle glycogen depletion and the key glucoregulatory hormones will be discussed. Glucose uptake can also be stimulated independently by hypoxia; therefore, hypoxic training presents as an emerging method for enhancing the effects of exercise on glucose regulation. Thus, the second aim of this review is to discuss the potential for systemic hypoxia to enhance the effects of exercise on glucose regulation.

Metabolomics analysis of type 2 diabetes remission identifies 12 metabolites with predictive capacity: a CORDIOPREV clinical trial study

BACKGROUND

Type 2 diabetes mellitus (T2DM) is one of the most widely spread diseases, affecting around 90% of the patients with diabetes. Metabolomics has proven useful in diabetes research discovering new biomarkers to assist in therapeutical studies and elucidating pathways of interest. However, this technique has not yet been applied to a cohort of patients that have remitted from T2DM.

METHODS

All patients with a newly diagnosed T2DM at baseline (n = 190) were included. An untargeted metabolomics approach was employed to identify metabolic differences between individuals who remitted (RE), and those who did not (non-RE) from T2DM, during a 5-year study of dietary intervention. The biostatistical pipeline consisted of an orthogonal projection on the latent structure discriminant analysis (O-PLS DA), a generalized linear model (GLM), a receiver operating characteristic (ROC), a DeLong test, a Cox regression, and pathway analyses.

RESULTS

The model identified a significant increase in 12 metabolites in the non-RE group compared to the RE group. Cox proportional hazard models, calculated using these 12 metabolites, showed that patients in the high-score tercile had significantly (p-value < 0.001) higher remission probabilities (Hazard Ratio, HR, high versus low = 2.70) than those in the lowest tercile. The predictive power of these metabolites was further studied using GLMs and ROCs. The area under the curve (AUC) of the clinical variables alone is 0.61, but this increases up to 0.72 if the 12 metabolites are considered. A DeLong test shows that this difference is statistically significant (p-value = 0.01).

CONCLUSIONS

Our study identified 12 endogenous metabolites with the potential to predict T2DM remission following a dietary intervention. These metabolites, combined with clinical variables, can be used to provide, in clinical practice, a more precise therapy.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT00924937.

Branched-Chain and Aromatic Amino Acids in Relation to Fat Mass and Fat-Free Mass Changes among Adolescents: A School-Based Intervention

Plasma levels of branched-chain amino acids (BCAA) and aromatic amino acids (AAA) are considered early metabolic markers of obesity and insulin resistance (IR). This study aimed to assess changes in plasma concentrations of BCAA/AAA and HOMA-IR2 (homeostasis model assessment of IR) after intervention-induced modifications in fat mass (FM) and fat-free mass (FFM) among French Polynesian adolescents. FM, FFM, plasma levels of BCAA and AAA, HOMA-IR2 were recorded at baseline and post intervention among 226 adolescents during a 5-month school-based intervention on diet and physical activity. Participants were divided into two subgroups according to their college attendance status which determined their intervention adherence: externs/half-residents (n = 157) and residents (n = 69). Four ordinal categories of body composition changes post-intervention were created for the analysis (FMgain/FFMlost < FMgain/FFMgain < FMlost/FFMlost < FMlost/FFMgain). After 5 months, changes in BCAA (p−trend < 0.001) and AAA (p−trend = 0.007) concentrations were positively associated with ordinal categories of body composition. HOMA-IR2 significantly decreased with FMlost (−0.40; 95% CI, −0.60 to −0.20) and increased with FMgain (0.23; 95% CI, 0.11 to 0.36). Our results suggest that FM loss is associated with a decrease in concentrations of obesity and IR metabolic markers which is more substantial when FM loss is accompanied with FFM gain.

Circulating Palmitoyl Sphingomyelin Is Associated With Cardiovascular Disease in Individuals With Type 2 Diabetes: Findings From the China Da Qing Diabetes Study

OBJECTIVE

To investigate the association of potential cardiovascular disease (CVD) biomarkers in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

We enrolled 120 participants (aged 61.5-69.5 years) with type 2 diabetes and 60 (aged 62.5-73.5 years) with normal glucose tolerance in the discovery group from the original Da Qing Diabetes Study. Their diabetes status was confirmed in 1986; then, the participants were followed over 23 years to collect CVD outcome data. Untargeted and targeted metabolomics analyses based on ultra-high-performance liquid chromatography-tandem mass spectrometry were used to identify potential markers. Multivariable regression analysis was used to evaluate the association between metabolites and CVD outcomes. An independent group of 335 patients (aged 67.0-77.0 years) with diabetes was used for biomarker validation.

RESULTS

In the discovery group, untargeted metabolomics analysis found 16 lipids and fatty acids metabolites associated with CVD risk in patients with diabetes, with palmitoyl sphingomyelin (PSM) having the strongest association. Plasma PSM concentrations were significantly higher in cases of diabetes with CVD than without (41.68 ± 10.47 vs. 9.69 ± 1.47 μg/mL; P < 0.0001). The odds ratio (OR) of CVD for 1 µg/mL PSM change was 1.19 (95% CI 1.13-1.25) after adjustment of clinical confounders. The validation study confirmed that PSM was significantly associated with increased CVD risk in diabetes (OR 1.22 [95% CI 1.16-1.30]).

CONCLUSIONS

Changes in lipid and fatty acid content were significantly associated with CVD risk in the Chinese population with diabetes. PSM is a potential biomarker of increased CVD risk in diabetes.

Recent Developments in Biomarkers for Diagnosis and Screening of Type 2 Diabetes Mellitus

PURPOSE OF REVIEW

Diabetes mellitus is a complex, chronic illness characterized by elevated blood glucose levels that occurs when there is cellular resistance to insulin action, pancreatic β-cells do not produce sufficient insulin, or both. Diabetes prevalence has greatly increased in recent decades; consequently, it is considered one of the fastest-growing public health emergencies globally. Poor blood glucose control can result in long-term micro- and macrovascular complications such as nephropathy, retinopathy, neuropathy, and cardiovascular disease. Individuals with diabetes require continuous medical care, including pharmacological intervention as well as lifestyle and dietary changes.

RECENT FINDINGS

The most common form of diabetes mellitus, type 2 diabetes (T2DM), represents approximately 90% of all cases worldwide. T2DM occurs more often in middle-aged and elderly adults, and its cause is multifactorial. However, its incidence has increased in children and young adults due to obesity, sedentary lifestyle, and inadequate nutrition. This high incidence is also accompanied by an estimated underdiagnosis prevalence of more than 50% worldwide. Implementing successful and cost-effective strategies for systematic screening of diabetes mellitus is imperative to ensure early detection, lowering patients' risk of developing life-threatening disease complications. Therefore, identifying new biomarkers and assay methods for diabetes mellitus to develop robust, non-invasive, painless, highly-sensitive, and precise screening techniques is essential. This review focuses on the recent development of new clinically validated and novel biomarkers as well as the methods for their determination that represent cost-effective alternatives for screening and early diagnosis of T2DM.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

The role of metabolomics in personalized medicine for diabetes

Metabolomics is rapidly evolving omics technology in personalized medicine, it offers a new avenue for identification of multiple novel metabolic mediators of impaired glucose tolerance and dysglycemia. Liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy are most commonly used analytical methods in the field of metabolomics. Recent evidences showed that metabolomic profiles are link to the incidence of diabetes. In this review, an overview of metabolomics studies in diabetes revealed several diabetes-associated metabolites including 1,5-anhydroglycitol, branch chain amino acids, glucose, α-hydroxybutyric acid, 3-hydroundecanoyl-carnitine and phosphatidylcholine that could be potential biomarkers associated with diabetes. These identified metabolites can be used to develop personalized prognostics and diagnostic, and help in diabetes management.

Time-based investigation of urinary metabolic markers for Type 2 diabetes: Metabolomics approach for diabetes management

Diabetes is considered one of the most important health emergencies worldwide and Egypt has 8.2 million diabetic patients according to the International Diabetes Federation report in 2017. The objective of this study was to monitor the time-course variation in the metabolic profile of diabetic rats to detect urinary metabolic biomarkers using the metabolomics approach. Type 2 diabetes was induced in male Wistar albino rats using a single intraperitoneal injection of 40 mg/kg of streptozotocin following oral administration of 10% fructose in drinking water for 3 weeks. Then, urine was collected for 24 h from rats at three time points (0, 2, and 4 weeks after confirmation of diabetes), and were analyzed by nuclear magnetic resonance (H¹ -NMR), followed by multivariate data analysis. The results from H¹ -NMR pointed out that d-glucose, taurine, l-carnitine, l-fucose, 1,5-anhydrosorbitol, and d-galactose levels showed consistent significant variation (p < 0.05) between the positive (diabetic) and negative (normal) controls during the whole experimental period. Also, with the disease progression, myoinositol, and l-phenylalanine levels were significantly altered (p < 0.05) after 2 weeks and this alteration was maintained till the end of the 4-week experimental period in the positive control group. From the results of the present study, it could be concluded that we cannot depend only on glucose levels for prognostic purposes since there are other metabolic disturbances in diabetes which need to be tracked for better disease prognosis.

Plasma metabolomics analysis of the effects of drinking soda water on hyperglycemia mice using ultrahigh-pressure liquid chromatography-quadrupole-time of flight-mass spectrometry

The aim of this study was to evaluate the effects of a natural soda water [Shi Han Quan (SHQ)] on hyperglycemia and plasma metabolic profiling and explore the mechanism using metabolomics techniques. Kun-Ming mice weighing 26 ± 2 g were used for the hyperglycemia animal model with alloxan and divided into control, hyperglycemia (HG), and HG + SHQ soda water (SHQ) groups. The experiment lasted for 30 days. The plasma metabolomic profiling of mice was determined using ultrahigh-pressure liquid chromatography-quadrupole-time of flight-mass spectrometry. After the mice drank SHQ soda water, the levels of insulin and blood glucose were significantly lower in the SHQ group compared with the control group, and the level of insulin sensitivity [insulin sensitivity index (ISI)] was significantly higher in the SHQ group compared with the HG group. The mice in the different groups after SHQ intervention could be separated into distinct clusters, and nine major plasma metabolites with significant differences between groups were found closely associated with blood glucose and ISI. The metabolic pathway analysis of these metabolites involved abnormal fatty acid oxidation and phospholipid, acylcarnitine, and corticoid metabolism. The results suggested the metabolic changes and possible mechanism of SHQ improving the alloxan-induced HG, and the findings provided insights into the prevention and control of HG and diabetes.

Secondary Metabolites in the Treatment of Diabetes Mellitus: A Paradigm Shift

Diabetes mellitus (DM) is a chronic, polygenic and non-infectious group of diseases that occurs due to insulin resistance or its low production by the pancreas and is also associated with lifelong damage, dysfunction and collapse of various organs. Management of diabetes is quite complex having many bodily and emotional complications and warrants efficient measures for prevention and control of the same. As per the estimates of the current and future diabetes prevalence, around 425 million people were diabetic in 2017 which is anticipated to rise up to 629 million by 2045. Various studies have vaguely proven the fact that several vitamins, minerals, botanicals and secondary metabolites demonstrate hypoglycemic activity in vivo as well as in vitro. Flavonoids, anthocyanin, catechin, lipoic acid, coumarin metabolites, etc. derived from herbs were found to elicit a significant influence on diabetes. However, the prescription of herbal compounds depend on various factors, including the degree of diabetes progression, comorbidities, feasibility, economics as well as their ADR profile. For instance, cinnamon could be a more favorable choice for diabetic hypertensive patients. Diabecon®, Glyoherb® and Diabeta Plus® are some of the herbal products that had been launched in the market for the favorable or adjuvant therapy of diabetes. Moreover, Aloe vera leaf gel extract demonstrates significant activity in diabetes. The goal of this review was to inscribe various classes of secondary metabolites, in particular those obtained from plants, and their role in the treatment of DM. Recent advancements in recognizing the markers which can be employed for identifying altered metabolic pathways, biomarker discovery, limitations, metabolic markers of drug potency and off-label effects are also reviewed.

Library-assisted nonlinear blind separation and annotation of pure components from a single 1H nuclear magnetic resonance mixture spectra

Due to its capability for high-throughput screening ¹H nuclear magnetic resonance (NMR) spectroscopy is commonly used for metabolite research. The key problem in ¹H NMR spectroscopy of multicomponent mixtures is overlapping of component signals and that is increasing with the number of components, their complexity and structural similarity. It makes metabolic profiling, that is carried out through matching acquired spectra with metabolites from the library, a hard problem. Here, we propose a method for nonlinear blind separation of highly correlated components spectra from a single ¹H NMR mixture spectra. The method transforms a single nonlinear mixture into multiple high-dimensional reproducible kernel Hilbert Spaces (mRKHSs). Therein, highly correlated components are separated by sparseness constrained nonnegative matrix factorization in each induced RKHS. Afterwards, metabolites are identified through comparison of separated components with the library comprised of 160 pure components. Thereby, a significant number of them are expected to be related with diabetes type 2. Conceptually similar methodology for nonlinear blind separation of correlated components from two or more mixtures is presented in the Supplementary material. Single-mixture blind source separation is exemplified on: (i) annotation of five components spectra separated from one ¹H NMR model mixture spectra; (ii) annotation of fifty five metabolites separated from one ¹H NMR mixture spectra of urine of subjects with and without diabetes type 2. Arguably, it is for the first time a method for blind separation of a large number of components from a single nonlinear mixture has been proposed. Moreover, the proposed method pinpoints urinary creatine, glutamic acid and 5-hydroxyindoleacetic acid as the most prominent metabolites in samples from subjects with diabetes type 2, when compared to healthy controls.

Metabolic signature of obesity-associated insulin resistance and type 2 diabetes

Background

Obesity is associated with an increased risk of insulin resistance and type 2 diabetes mellitus (T2DM). However, some obese individuals maintain their insulin sensitivity and exhibit a lower risk of associated comorbidities. The underlying metabolic pathways differentiating obese insulin sensitive (OIS) and obese insulin resistant (OIR) individuals remain unclear.

Methods

In this study, 107 subjects underwent untargeted metabolomics of serum samples using the Metabolon platform. Thirty-two subjects were lean controls whilst 75 subjects were obese including 20 OIS, 41 OIR, and 14 T2DM individuals.

Results

Our results showed that phospholipid metabolites including choline, glycerophosphoethanolamine and glycerophosphorylcholine were significantly altered from OIS when compared with OIR and T2DM individuals. Furthermore, our data confirmed changes in metabolic markers of liver disease, vascular disease and T2DM, such as 3-hydroxymyristate, dimethylarginine and 1,5-anhydroglucitol, respectively.

Conclusion

This pilot data has identified phospholipid metabolites as potential novel biomarkers of obesity-associated insulin sensitivity and confirmed the association of known metabolites with increased risk of obesity-associated insulin resistance, with possible diagnostic and therapeutic applications. Further studies are warranted to confirm these associations in prospective cohorts and to investigate their functionality.

Purine Metabolites and Carnitine Biosynthesis Intermediates Are Biomarkers for Incident Type 2 Diabetes

CONTEXT

Metabolomics has the potential to generate biomarkers that can facilitate understanding relevant pathways in the pathophysiology of type 2 diabetes (T2DM).

METHODS

Nontargeted metabolomics was performed, via liquid chromatography-mass spectrometry, in a discovery case-cohort study from the Malmö Preventive Project (MPP), which consisted of 698 metabolically healthy participants, of whom 202 developed T2DM within a follow-up time of 6.3 years. Metabolites that were significantly associated with T2DM were replicated in the population-based Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC) (N = 3423), of whom 402 participants developed T2DM within a follow-up time of 18.2 years.

RESULTS

Using nontargeted metabolomics, we observed alterations in nine metabolite classes to be related to incident T2DM, including 11 identified metabolites. N2,N2-dimethylguanosine (DMGU) (OR = 1.94; P = 4.9e-10; 95% CI, 1.57 to 2.39) was the metabolite most strongly associated with an increased risk, and beta-carotene (OR = 0.60; P = 1.8e-4; 95% CI, 0.45 to 0.78) was the metabolite most strongly associated with a decreased risk. Identified T2DM-associated metabolites were replicated in MDC-CC. Four metabolites were significantly associated with incident T2DM in both the MPP and the replication cohort MDC-CC, after adjustments for traditional diabetes risk factors. These included associations between three metabolites, DMGU, 7-methylguanine (7MG), and 3-hydroxytrimethyllysine (HTML), and incident T2DM.

CONCLUSIONS

We used nontargeted metabolomics in two Swedish prospective cohorts comprising >4000 study participants and identified independent, replicable associations between three metabolites, DMGU, 7MG, and HTML, and future risk of T2DM. These findings warrant additional studies to investigate a potential functional connection between these metabolites and the onset of T2DM.

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

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

Effects of Long-Term Storage at -80 °C on the Human Plasma Metabolome

High-quality biological samples are required for the favorable outcome of research studies, and valid data sets are crucial for successful biomarker identification. Prolonged storage of biospecimens may have an artificial effect on compound levels. In order to investigate the potential effects of long-term storage on the metabolome, human ethylenediaminetetraacetic acid (EDTA) plasma samples stored for up to 16 years were analyzed by gas and liquid chromatography-tandem mass spectrometry-based metabolomics. Only 2% of 231 tested plasma metabolites were altered in the first seven years of storage. However, upon longer storage periods of up to 16 years and more time differences of few years significantly affected up to 26% of the investigated metabolites when analyzed within subject age groups. Ontology classes that were most affected included complex lipids, fatty acids, energy metabolism molecules, and amino acids. In conclusion, the human plasma metabolome is adequately stable to long-term storage at −80 °C for up to seven years but significant changes occur upon longer storage. However, other biospecimens may display different sensitivities to long-term storage. Therefore, in retrospective studies on EDTA plasma samples, analysis is best performed within the first seven years of storage.

Dietary Supplementation of Vine Tea Ameliorates Glucose and Lipid Metabolic Disorder via Akt Signaling Pathway in Diabetic Rats

A traditional Chinese tea with many pharmacological effects, vine tea (VT) is considered a potential dietary supplement to improve type 2 diabetes (T2D). To investigate the effect and mechanism of VT on glucose and lipid metabolic disorders in T2D rats, Wistar rats fed a normal diet served as the normal control, while rats fed a high-fat diet combined with low-dose streptozotocin (STZ)-induced T2D were divided into three groups: The model group (MOD); the positive control group (MET, metformin at 200 mg/kg/d); and the VT-treated group (VT500, allowed to freely drink 500 mg/L VT). After four weeks of intervention, biochemical metrics indicated that VT significantly ameliorated hyperglycemia, hyperlipidemia and hyperinsulinemia in T2D rats. Metabolomics research indicated that VT regulated the levels of metabolites closely related to glucose and lipid metabolism and promoted glycogen synthesis. Furthermore, VT had a significant influence on the expression of key genes involved in the Akt signaling pathway, inhibited gluconeogenesis through the Akt/Foxo1/Pck2 signaling pathway, and reduced fatty acid synthesis via the SREBP1c/Fasn signaling pathways. In conclusion, VT has great potential as a dietary supplement to ameliorate glucose and lipid metabolic disorders via the Akt signaling pathway in T2D rats.

Metabolic and proteomic signatures of hypoglycaemia in type 2 diabetes

AIMS

To determine the biochemical changes that underlie hypoglycaemia in a healthy control group and in people with type 2 diabetes (T2D).

MATERIALS AND METHODS

We report a hypoglycaemic clamp study in seven healthy controls and 10 people with T2D. Blood was withdrawn at four time points: at baseline after an overnight fast; after clamping to euglycaemia at 5 mmol/L; after clamping to hypoglycaemia at 2.8 mmol/L; and 24 hours later, after overnight fast. Deep molecular phenotyping using non-targeted metabolomics and the SomaLogic aptamer-based proteomics platform was performed on collected samples.

RESULTS

A total of 955 metabolites and 1125 proteins were identified, with significant alterations in >90 molecules. A number of metabolites significantly increased during hypoglycaemia, but only cortisol, adenosine-3',5'-cyclic monophosphate (cyclic AMP), and pregnenolone sulphate, were independent of insulin. By contrast, identified protein changes were triggered by hypoglycaemia rather than insulin. The T2D group had significantly higher levels of fatty acids including 10-nonadecenoate, linolenate and dihomo-linoleate during hypoglycaemia compared with the control group. Molecules contributing to cardiovascular complications such as fatty-acid-binding protein-3 and pregnenolone sulphate were altered in the participants with T2D during hypoglycaemia. Almost all molecules returned to baseline at 24 hours.

CONCLUSIONS

The present study provides a comprehensive description of molecular events that are triggered by insulin-induced hypoglycaemia. We identified deregulated pathways in T2D that may play a role in the pathophysiology of hypoglycaemia-induced cardiovascular complications.

1H NMR based serum metabolic profiling reveals differentiating biomarkers in patients with diabetes and diabetes-related complication

BACKGROUND

Diabetes is among the most prevalent diseases worldwide, of all the affected individuals a significant proportion of the population remains undiagnosed due to lack of specific symptoms early in this disorder and inadequate diagnostics. Diabetes and its associated sequela, i.e., comorbidity are associated with microvascular and macrovascular complications. As diabetes is characterized by an altered metabolism of key metabolites and regulatory pathways. Metabolic phenotyping can provide us with a better understanding of the unique set of regulatory perturbations that predispose to diabetes and its associated complication/comorbidities.

METHODOLOGY

The present study utilizes the analytical platform NMR spectroscopy coupled with Random Forest statistical analysis to identify the discriminatory metabolites in diabetes (DB = 38) vs. diabetes-related complication (DC = 35) along with the healthy control (HC = 50) subjects. A combined and pairwise analysis was performed to identify the discriminatory metabolites responsible for class separation. The perturbed metabolites were further rigorously validated using t-test, AUROC analysis to examine the statistical significance of the identified metabolites.

RESULTS

The DB and DC patients were well discriminated from HC. However, 15 metabolites were found to be significantly perturbed in DC patients compared to DB, the identified panel of metabolites are TCA cycle (succinate, citrate), methylamine metabolism (trimethylamine, methylamine, betaine), -intermediates; energy metabolites (glucose, lactate, pyruvate); and amino acids (valine, arginine, glutamate, methionine, proline, and threonine).

CONCLUSION

The ¹H NMR metabolomics may prove a promising technique to differentiate and predict diabetes and its complication on their onset or progression by determining the altered levels of the metabolites in serum.

Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.

Balancing metabolome coverage and reproducibility for untargeted NMR-based metabolic profiling in tissue samples through mixture design methods

Untargeted metabolomics attempts to acquire a comprehensive and reproducible set of small-molecule metabolites in biological systems. However, metabolite extraction method significantly affects the quality of metabolomics data. In the present study, we calculated the number of peaks (NP) and coefficient of variation (CV) to reflect metabolome coverage and reproducibility in untargeted NMR-based metabolic profiling of tissue samples in rats under different methanol/chloroform/water (MCW) extraction conditions. Different MCW extractions expectedly generated diverse characteristics of metabolome. Moreover, the classic MCW method revealed tissue-specific differences in the NP and CV values. To obtain high-quality metabolomics data, therefore, we used mixture design methods to optimize the MCW extraction strategy by maximizing the NP value and minimizing the CV value in each tissue sample. Results show that the optimal formulations of MCW extraction were 2:2:8 (ml/mg tissue) for brain sample, 2:4:6 (ml/mg tissue) for heart sample, 1.3:2:8.7 (ml/mg tissue) for liver sample, 4:2:6 (ml/mg tissue) for kidney sample, 2:3:7 (ml/mg tissue) for muscle sample, and 2:4:6 (ml/mg tissue) for pancreas sample. Therefore, these findings demonstrate that different tissue samples need a specific optimal extraction condition for balancing metabolome coverage and reproducibility in the untargeted metabolomics study. Mixture design method is an effective tool to optimize metabolite extraction strategy for tissue samples. Graphical abstract ᅟ.

Online Liquid Chromatography-Sheath-Flow Surface Enhanced Raman Detection of Phosphorylated Carbohydrates

Online detection and quantification of three phosphorylated carbohydrate molecules: glucose 1-phosphate, glucose 6-phosphate, and fructose 6-phosphate was achieved by coupling sheath-flow surface enhanced Raman spectroscopy (SERS) to liquid chromatography. The presence of an alkanethiol (hexanethiol) self-assembled monolayer adsorbed to a silver SERS-active substrate helps retain and concentrate the analytes of interest at the SERS substrate to improve the detection sensitivity significantly. Mixtures of 2 μM of phosphorylated carbohydrates in pure water as well as in cell culture media were successfully separated by HPLC, with identification using the sheath-flow SERS detector. The quantification of each analyte was achieved using partial least-squares (PLS) regression analysis and acetonitrile in the mobile phases as an internal standard. These results illustrate the utility of sheath-flow SERS for molecular specific detection in complex biological samples appropriate for metabolomics and other applications.

The potential of metabolic profiling for vaccine development

Recent technological advances have provided deeper insights into the role of small molecules in biological processes. Metabolic profiling has thus entered the arena of -omics studies and rapidly proven its value both as stand-alone and as complement to other more advanced approaches, notably transcriptomics. Here we describe the potential of metabolic profiling for vaccinology embedded in the context of infection and immunity. This discussion is preceded by a description of the relevant technical and analytical tools for biological interpretation of metabolic data. Although not as widely applied as other -omics technologies, we believe that metabolic profiling can make important contributions to the better understanding of mechanisms underlying vaccine-induced responses and their effects on the prevention of infection or disease.

Analysis of sequential hair segments reflects changes in the metabolome across the trimesters of pregnancy

The hair metabolome has been recognized as a valuable source of information in pregnancy research, as it provides stable metabolite information that could assist with studying biomarkers or metabolic mechanisms of pregnancy and its complications. We tested the hypothesis that hair segments could be used to reflect a metabolite profile containing information from both endogenous and exogenous compounds accumulated during the nine months of pregnancy. Segments of hair samples corresponding to the trimesters were collected from 175 pregnant women in New Zealand. The hair samples were analysed using gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. In healthy pregnancies, 56 hair metabolites were significantly different between the first and second trimesters, while 62 metabolites were different between the first and third trimesters (p < 0.05). Additionally, three metabolites in the second trimester hair samples were significantly different between healthy controls and women who delivered small-for-gestational-age infants (p < 0.05), and ten metabolites in third trimester hair were significantly different between healthy controls and women with gestational diabetes mellitus (p < 0.01). The findings from this pilot study provide improved insight into the changes of the hair metabolome during pregnancy, as well as highlight the potential of the maternal hair metabolome to differentiate pregnancy complications from healthy pregnancies.

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

AIMS/HYPOTHESIS

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

METHODS

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

RESULTS

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

CONCLUSIONS/INTERPRETATION

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

Long-Term Stability of Human Plasma Metabolites during Storage at -80 °C

Prolonged storage of biospecimen can lead to artificially altered metabolite concentrations and thus bias data analysis in metabolomics experiments. To elucidate the potential impact of long-term storage on the metabolite profile, a pooled human plasma sample was aliquoted and stored at -80 °C. During a time period of five years, 1012 of the aliquots were measured with the Biocrates AbsoluteIDQ p180 targeted-metabolomics assay at 193 time points. Modeling the concentration courses over time revealed that 55 out of 111 metabolites remained stable. The statistically significantly changed metabolites showed on average an increase or decrease of +13.7% or -14.5%, respectively. In detail, increased concentration levels were observed for amino acids (mean: + 15.4%), the sum of hexoses (+7.9%), butyrylcarnitine (+9.4%), and some phospholipids mostly with chain lengths exceeding 40 carbon atoms (mean: +18.0%). Lipids tended to exhibit decreased concentration levels with the following mean concentration changes: acylcarnitines, -12.1%; lysophosphatidylcholines, -15.1%; diacyl-phosphatidylcholines, -17.0%; acyl-alkyl-phosphatidylcholines, -13.3%; sphingomyelins, -14.8%. We conclude that storage of plasma samples at -80 °C for up to five years can lead to altered concentration levels of amino acids, acylcarnitines, glycerophospholipids, sphingomyelins, and the sum of hexoses. These alterations must be considered when analyzing metabolomics data from long-term epidemiological studies.

High-resolution metabolomics determines the mode of onset of type 2 diabetes in a 3-year prospective cohort study

Type 2 diabetes mellitus (DM) is a progressive disease and the rate of progression from non-diabetes to DM varies considerably between individuals, ranging from a few months to many years. It is important to understand the mechanisms underlying the progression of diabetes. In the present study, a high-resolution metabolomics (HRM) analysis was performed to detect potential biomarkers and pathways regulating the mode of onset by comparing subjects who developed and did not develop type 2 DM at the second year in a 3-year prospective cohort study. Metabolic profiles correlated with progression to DM were examined. The subjects (n=98) were classified into four groups: Control (did not develop DM for 3 years), DM (diagnosed with DM at the start of the study), DM onset at the third year and DM onset at the second year. The focus was on the comparison of serum samples of the DM groups with onset at the second and third year from the first year, where these two groups had not developed DM, yet. Analyses involved sample examination using liquid chromatography-mass spectrometry-based HRM and multivariate statistical analysis of the data. Metabolic differences were identified across all analyses with the affected pathways involved in metabolism associated with steroid biosynthesis and bile acid biosynthesis. In the first year, higher levels of cholesterol {mass-to charge ratio (m/z) 369.35, (M+H-H2O)+}, 25-hydroxycholesterol [m/z 403.36, (M+H)+], 3α,7α-dihydroxy-5β-cholestane [m/z 443.33, (M+K)+], 4α-methylzymosterol-4-carboxylate [m/z 425.34, (M+H‑H2O)+], and lower levels of 24,25-dihydrolanosterol [m/z 429.40, (M+H)+] were evident in the group with DM onset at the second year compared with those in the group with DM onset at the third year. These results, with a focus on the cholesterol biosynthesis pathway, point to important aspects in the development of DM and may aid in the development of more effective means of treatment and prevention.

Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies

Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.

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.

Systemic Perturbations of Key Metabolites in Type 2 Diabetic Rats Treated by Polyphenol Extracts from Litchi chinensis Seeds

Our previous research obtained Litchi chinensis Sonn. seeds extract (LSE) which showed hypoglycaemic effects on type 2 diabetes (T2D) rats. In order to understand the detailed pathogenesis of diabetes intervened by LSE, the metabonomics strategy was used. As a result, LSE decreased the insulin resistance index and the levels of glucose in urine through elevating the mRNA level of insulin, while decreasing the expression of glucagon to enhance the function of the pancreas. Meanwhile, LSE regulated the glucose and fatty acid metabolisms via increasing the expression of glucose transporter (Glu) 2, Glu4, insulin receptor (IR), and IR substrate-2 (IRS2). LSE effectively restored the impairment of the IRS2/PI3K/Akt/mTOR insulin signaling in the livers. All in all, LSE played a pivotal role in the treatment of T2D through regulation of broad-spectrum metabolic changes and inhibition of the glycogenesis, proteolysis, and lipogenesis in T2D rats.

Multi-parameter comparison of a standardized mixed meal tolerance test in healthy and type 2 diabetic subjects: the PhenFlex challenge

BACKGROUND

A key feature of metabolic health is the ability to adapt upon dietary perturbations. Recently, it was shown that metabolic challenge tests in combination with the new generation biomarkers allow the simultaneous quantification of major metabolic health processes. Currently, applied challenge tests are largely non-standardized. A systematic review defined an optimal nutritional challenge test, the "PhenFlex test" (PFT). This study aimed to prove that PFT modulates all relevant processes governing metabolic health thereby allowing to distinguish subjects with different metabolic health status. Therefore, 20 healthy and 20 type 2 diabetic (T2D) male subjects were challenged both by PFT and oral glucose tolerance test (OGTT). During the 8-h response time course, 132 parameters were quantified that report on 26 metabolic processes distributed over 7 organs (gut, liver, adipose, pancreas, vasculature, muscle, kidney) and systemic stress.

RESULTS

In healthy subjects, 110 of the 132 parameters showed a time course response. Patients with T2D showed 18 parameters to be significantly different after overnight fasting compared to healthy subjects, while 58 parameters were different in the post-challenge time course after the PFT. This demonstrates the added value of PFT in distinguishing subjects with different health status. The OGTT and PFT response was highly comparable for glucose metabolism as identical amounts of glucose were present in both challenge tests. Yet the PFT reports on additional processes, including vasculature, systemic stress, and metabolic flexibility.

CONCLUSION

The PFT enables the quantification of all relevant metabolic processes involved in maintaining or regaining homeostasis of metabolic health. Studying both healthy subjects and subjects with impaired metabolic health showed that the PFT revealed new processes laying underneath health. This study provides the first evidence towards adopting the PFT as gold standard in nutrition research.

Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis

Recent metabolomics studies of Rheumatoid Arthritis (RA) reported few metabolites that were associated with the disease, either due to small cohort sizes or limited coverage of metabolic pathways. Our objective is to identify metabolites associated with RA and its cofounders using a new untargeted metabolomics platform. Moreover, to investigate the pathomechanism of RA by identifying correlations between RA-associated metabolites. 132 RA patients and 104 controls were analyzed for 927 metabolites. Metabolites were tested for association with RA using linear regression. OPLS-DA was used to discriminate RA patients from controls. Gaussian Graphical Models (GGMs) were used to identify correlated metabolites. 32 metabolites are identified as significantly (Bonferroni) associated with RA, including the previously reported metabolites as DHEAS, cortisol and androstenedione and extending that to a larger set of metabolites in the steroid pathway. RA classification using metabolic profiles shows a sensitivity of 91% and specificity of 88%. Steroid levels show variation among the RA patients according to the corticosteroid treatment; lowest in those taking the treatment at the time of the study, higher in those who never took the treatment, and highest in those who took it in the past. Finally, the GGM reflects metabolite relations from the steroidogenesis pathway.

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

Background

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

Methods

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

Results

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

Conclusion

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

Genetic variants including markers from the exome chip and metabolite traits of type 2 diabetes

Diabetes-associated metabolites may aid the identification of new risk variants for type 2 diabetes. Using targeted metabolomics within a subsample of the German EPIC-Potsdam study (n = 2500), we tested previously published SNPs for their association with diabetes-associated metabolites and conducted an additional exploratory analysis using data from the exome chip including replication within 2,692 individuals from the German KORA F4 study. We identified a total of 16 loci associated with diabetes-related metabolite traits, including one novel association between rs499974 (MOGAT2) and a diacyl-phosphatidylcholine ratio (PC aa C40:5/PC aa C38:5). Gene-based tests on all exome chip variants revealed associations between GFRAL and PC aa C42:1/PC aa C42:0, BIN1 and SM (OH) C22:2/SM C18:0 and TFRC and SM (OH) C22:2/SM C16:1). Selecting variants for gene-based tests based on functional annotation identified one additional association between OR51Q1 and hexoses. Among single genetic variants consistently associated with diabetes-related metabolites, two (rs174550 (FADS1), rs3204953 (REV3L)) were significantly associated with type 2 diabetes in large-scale meta-analysis for type 2 diabetes. In conclusion, we identified a novel metabolite locus in single variant analyses and four genes within gene-based tests and confirmed two previously known mGWAS loci which might be relevant for the risk of type 2 diabetes.

Nutrimetabolomics: An Update on Analytical Approaches to Investigate the Role of Plant-Based Foods and Their Bioactive Compounds in Non-Communicable Chronic Diseases

Metabolomics is the study of low-weight molecules present in biological samples such as biofluids, tissue/cellular extracts, and culture media. Metabolomics research is increasing, and at the moment, it has several applications in the food science and nutrition fields. In the present review, we provide an update about the most frequently used methodologies and metabolomic platforms in these areas. Also, we discuss different metabolomic strategies regarding the discovery of new bioactive compounds (BACs) in plant-based foods. Furthermore, we review the existing literature related to the use of metabolomics to investigate the potential protective role of BACs in the prevention and treatment of non-communicable chronic diseases, namely cardiovascular disease, diabetes, and cancer.

Key elements of metabolomics in the study of biomarkers of diabetes

Metabolomics is instrumental in the analysis of disease mechanisms and biomarkers of disease. The human metabolome is influenced by genetics and environmental interactions and reveals characteristic signatures of disease. Population studies with metabolomics require special study designs and care needs to be taken with pre-analytics. Gas chromatography coupled to mass spectrometry, liquid chromatography coupled to mass spectrometry or NMR are popular techniques used for metabolomic analyses in human cohorts. Metabolomics has been successfully used in the biomarker search for disease prediction and progression, for analyses of drug action and for the development of companion diagnostics. Several metabolites or metabolite classes identified by metabolomics have gained much attention in the field of diabetes research in the search for early disease detection, differentiation of progressor types and compliance with medication. This review summarises a presentation given at the 'New approaches beyond genetics' symposium at the 2015 annual meeting of the EASD. It is accompanied by another review from this symposium by Bernd Mayer (DOI: 10.1007/s00125-016-4032-2 ) and an overview by the Session Chair, Leif Groop (DOI: 10.1007/s00125-016-4014-4 ).

An Untargeted Metabolomics Approach to Characterize Short-Term and Long-Term Metabolic Changes after Bariatric Surgery

Bariatric surgery is currently one of the most effective treatments for obesity and leads to significant weight reduction, improved cardiovascular risk factors and overall survival in treated patients. To date, most studies focused on short-term effects of bariatric surgery on the metabolic profile and found high variation in the individual responses to surgery. The aim of this study was to identify relevant metabolic changes not only shortly after bariatric surgery (Roux-en-Y gastric bypass) but also up to one year after the intervention by using untargeted metabolomics. 132 serum samples taken from 44 patients before surgery, after hospital discharge (1-3 weeks after surgery) and at a 1-year follow-up during a prospective study (NCT01271062) performed at two study centers (Austria and Switzerland). The samples included 24 patients with type 2 diabetes at baseline, thereof 9 with diabetes remission after one year. The samples were analyzed by using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS, HILIC-QExactive). Raw data was processed with XCMS and drift-corrected through quantile regression based on quality controls. 177 relevant metabolic features were selected through Random Forests and univariate testing and 36 metabolites were identified. Identified metabolites included trimethylamine-N-oxide, alanine, phenylalanine and indoxyl-sulfate which are known markers for cardiovascular risk. In addition we found a significant decrease in alanine after one year in the group of patients with diabetes remission relative to non-remission. Our analysis highlights the importance of assessing multiple points in time in subjects undergoing bariatric surgery to enable the identification of biomarkers for treatment response, cardiovascular benefit and diabetes remission. Key-findings include different trend pattern over time for various metabolites and demonstrated that short term changes should not necessarily be used to identify important long term effects of bariatric surgery.

Novel insights into development of diabetic bladder disorder provided by metabolomic analysis of the rat nondiabetic and diabetic detrusor and urothelial layer

There are at present no published studies providing a global overview of changes in bladder metabolism resulting from diabetes. Such studies have the potential to provide mechanistic insight into the development of diabetic bladder disorder (DBD). In the present study, we compared the metabolome of detrusor and urothelial layer in a 1-mo streptozotocin-induced rat model of type 1 diabetes with nondiabetic controls. Our studies revealed that diabetes caused both common and differential changes in the detrusor and urothelial layer's metabolome. Diabetes resulted in similar changes in the levels of previously described diabetic markers in both tissues, such as glucose, lactate, 2-hydroxybutyrate, branched-chain amino acid degradation products, bile acids, and 1,5-anhydroglucitol, as well as markers of oxidative stress. In the detrusor (but not the urothelial layer), diabetes caused activation of the pentose-phosphate and polyol pathways, concomitant with a reduction in the TCA cycle and β-oxidation. Changes in detrusor energy-generating pathways resulted in an accumulation of sorbitol that, through generation of advanced glycation end products, is likely to play a central role in the development of DBD. In the diabetic urothelial layer there was decreased flux of glucose via glycolysis and changes in lipid metabolism, particularly prostaglandin synthesis, which also potentially contributes to detrusor dysfunction.

5-Aminoimidazole-4-carboxamide ribonucleoside-mediated adenosine monophosphate-activated protein kinase activation induces protective innate responses in bacterial endophthalmitis

The retina is considered to be the most metabolically active tissue in the body. However, the link between energy metabolism and retinal inflammation, as incited by microbial infection such as endophthalmitis, remains unexplored. In this study, using a mouse model of Staphylococcus aureus (SA) endophthalmitis, we demonstrate that the activity (phosphorylation) of 5' adenosine monophosphate-activated protein kinase alpha (AMPKα), a cellular energy sensor and its endogenous substrate; acetyl-CoA carboxylase is down-regulated in the SA-infected retina. Intravitreal administration of an AMPK activator, 5-aminoimidazole-4-carboxamide ribonucleoside (AICAR), restored AMPKα and acetyl-CoA carboxylase phosphorylation. AICAR treatment reduced both the bacterial burden and intraocular inflammation in SA-infected eyes by inhibiting NF-kB and MAP kinases (p38 and JNK) signalling. The anti-inflammatory effects of AICAR were diminished in eyes pretreated with AMPK inhibitor, Compound C. The bioenergetics (Seahorse) analysis of SA-infected microglia and bone marrow-derived macrophages revealed an increase in glycolysis, which was reinstated by AICAR treatment. AICAR also reduced the expression of SA-induced glycolytic genes, including hexokinase 2 and glucose transporter 1 in microglia, bone marrow-derived macrophages and the mouse retina. Interestingly, AICAR treatment enhanced the bacterial phagocytic and intracellular killing activities of cultured microglia, macrophages and neutrophils. Furthermore, AMPKα1 global knockout mice exhibited increased susceptibility towards SA endophthalmitis, as evidenced by increased inflammatory mediators and bacterial burden and reduced retinal function. Together, these findings provide the first evidence that AMPK activation promotes retinal innate defence in endophthalmitis by modulating energy metabolism and that it can be targeted therapeutically to treat ocular infections.

Sheath flow SERS for chemical profiling in urine

The molecular specificity and sensitivity of surface enhanced Raman scattering (SERS) makes it an attractive method for biomedical diagnostics. Here we present results demonstrating the utility and complications for SERS characterization in urine. The chemical fingerprint characteristics of Raman spectra suggest its use as a label free diagnostic; however, the complex composition of biological fluids presents a tremendous challenge. In particular, the limited number of surface sites and competing absorption tend to mask the presence of analytes in solution, particularly when the solution contains multiple analytes. To address these problems and characterize biological fluids we have demonstrated a sheath-flow interface for SERS detection. This sheath-flow SERS interface uses hydrodynamic focusing to confine analyte molecules eluting out of a column onto a planar SERS substrate where the molecules are detected by their intrinsic SERS signal. In this report we compare the direct detection of benzoylecgonine in urine using DSERS with chemical profiling by capillary zone electrophoresis and sheath-flow SERS detection. The SERS spectrum from the observed migration peaks can identify benzoylecgonine and other distinct spectra are also observed, suggesting improved chemical diagnostics in urine. With over 2000 reported compounds in urine, identification of each of the detected species is an enormous task. Nonetheless, these samples provide a benchmark to establish the potential clinical utility of sheath-flow SERS detection.

Plasma amino acid and metabolite signatures tracking diabetes progression in the UCD-T2DM rat model

Elevations of plasma concentrations of branched-chain amino acids (BCAAs) are observed in human insulin resistance and type 2 diabetes mellitus (T2DM); however, there has been some controversy with respect to the passive or causative nature of the BCAA phenotype. Using untargeted metabolomics, plasma BCAA and other metabolites were assessed in lean control Sprague-Dawley rats (LC) and temporally during diabetes development in the UCD-T2DM rat model, i.e., prediabetic (PD) and 2 wk (D2W), 3 mo (D3M), and 6 mo (D6M) post-onset of diabetes. Plasma leucine, isoleucine, and valine concentrations were elevated only in D6M rats compared with D2W rats (by 28, 29, and 30%, respectively). This was in contrast to decreased plasma concentrations of several other amino acids in D3M and/or D6M relative to LC rats (Ala, Arg, Glu, Gln, Met, Ser, Thr, and Trp). BCAAs were positively correlated with fasting glucose and negatively correlated with plasma insulin, total body weight, total adipose tissue weight, and gastrocnemius muscle weight in the D3M and D6M groups. Multivariate analysis revealed that D3M and D6M UCD-T2DM rats had lower concentrations of amino acids, amino acid derivatives, 1,5-anhydroglucitol, and conduritol-β-opoxide and higher concentrations of uronic acids, pantothenic acids, aconitate, benzoic acid, lactate, and monopalmitin-2-glyceride relative to PD and D2W UCD-T2DM rats. The UCD-T2DM rat does not display elevated plasma BCAA concentrations until 6 mo post-onset of diabetes. With the acknowledgement that this is a rodent model of T2DM, the results indicate that elevated plasma BCAA concentrations are not necessary or sufficient to elicit an insulin resistance or T2DM onset.