Fasting serum α‑hydroxybutyrate and pyroglutamic acid as important metabolites for detecting isolated post-challenge diabetes based on organic acid profiles

Identification of potential serum biomarkers associated with HbA1c levels in Indian type 2 diabetic subjects using NMR-based metabolomics

BACKGROUND

The prevalence of type 2 diabetes mellitus (T2DM), a progressive metabolic disorder characterized by chronic hyperglycemia and the development of insulin resistance, has increased globally, with worrying statistics coming from children, adolescents, and young adults from developing countries like India. Here, we investigated unique circulating metabolic signatures associated with prediabetes and T2DM in an Indian cohort using NMR-based metabolomics.

MATERIALS AND METHODS

The study subjects included healthy volunteers (N = 101), prediabetic subjects (N = 75), and T2DM patients (N = 108). Serum metabolic profiling was performed using 1H NMR spectroscopy and major perturbed metabolites were identified by multivariate analysis and receiver operating characteristic (ROC) modules.

RESULTS

Of the 36 aqueous abundant metabolites, 24 showed a statistically significant difference between healthy volunteers, prediabetics, and established T2DM subjects. On performing multivariate ROC curve analysis with 5 commonly dysregulated metabolites (namely, glucose, pyroglutamate, o-phosphocholine, serine, and methionine) in prediabetes and T2DM, AUC values obtained were 0.96 (95 % confidence interval (CI) = 0.93, 0.98) for T2DM; and 0.88 (95 % CI = 0.81, 0.93) for prediabetic subjects, respectively.

CONCLUSION

We propose that the identified metabolite panel can be used in the future as a biomarker for clinical diagnosis, patient surveillance, and for predicting individuals at risk for developing diabetes.

Optimization of the quantitative protocol for organic acid in fecal samples using gas chromatography-mass spectrometry

Organic acids (OAs) play important roles in a variety of intracellular metabolic pathways, such as the tricarboxylic acid cycle, fatty acid oxidation, glycolysis. The accurate detection of OAs in fecal samples was crucial for comprehending the metabolic changes associated with various metabolic disease. However, the analytical protocol detecting OAs profiling in feces have received scant attention. In this work, an optimized protocol based on chromatography-mass spectrometry for simultaneous quantification of 23 OAs in rat feces was developed. The optimal conditions involved using a 40-mg fecal sample mixed with isopropyl alcohol, acetonitrile, and deionized water (3:2:2 vol ratio) with a total volume of 1500 μL, followed by ultrasonic extraction and a derivatization reaction with an 80 μL derivative agent. The protocol showed an acceptable linearity (R2 ≥ 0.9906), the satisfactory precision (RSD% ≤ 14.87%), the low limits of detection (0.001 to 1 μg/mL) and the limit of quantification (0.005 to 1.5 μg/mL). Moreover, the dried residues of the extracted solution showed the better stability of OAs at -20 °C, which was more suitable for a large-scale sample analysis. Finally, the developed protocol was successfully applied to compare the difference of OAs profiling in fecal samples harvested from normal and nonalcoholic fatty liver disease rats, which was beneficial to find out the metabolic change of OAs profiling and explain the related mechanism of the disease.

Serum metabolomics profiling by proton nuclear magnetic resonance spectrometry of the response to single oral macronutrient challenges in women with polycystic ovary syndrome (PCOS) compared with male and female controls

BACKGROUND

The polycystic ovary syndrome (PCOS) is associated with insulin resistance, obesity and cardiometabolic comorbidities. We here challenged the hypothesis, using state-of-the-art proton nuclear magnetic resonance spectrometry (1H-NMRS) metabolomics profiling, that androgen excess in women induces a certain masculinization of postprandial metabolism that is modulated by obesity.

MATERIALS AND METHODS

Participants were 53 Caucasian young adults, including 17 women with classic PCOS consisting of hyperandrogenism and ovulatory dysfunction, 17 non-hyperandrogenic women presenting with regular menses, and 19 healthy men, selected to be similar in terms of age and body mass index (BMI). Half of the subjects had obesity. Patients were submitted to isocaloric separate glucose, lipid and protein oral challenges in alternate days and fasting and postprandial serum samples were submitted to 1H-NMRS metabolomics profiling for quantification of 36 low-molecular-weight polar metabolites.

RESULTS

The largest postprandial changes were observed after glucose and protein intake, with lipid ingestion inducing smaller differences. Changes after glucose intake consisted of a marked increase in carbohydrates and byproducts of glycolysis, and an overall decrease in byproducts of proteolysis, lipolysis and ketogenesis. After the protein load, most amino acids and derivatives increased markedly, in parallel to an increase in pyruvate and a decrease in 3-hydroxybutyric acid and glycerol. Obesity increased β- and D-glucose and pyruvate levels, with this effect being observed mostly after glucose ingestion in women with PCOS. Regardless of the type of macronutrient, men presented increased lysine and decreased 3-hydroxybutyric acid. In addition, non-obese men showed increased postprandial β-glucose and decreased pyroglutamic acid, compared with non-obese control women. We observed a common pattern of postprandial changes in branched-chain and aromatic amino acids, where men showed greater amino acids increases after protein intake than control women and patients with PCOS but only within the non-obese participants. Conversely, this increase was blunted in obese men but not in obese women, who even presented a larger increase in some amino acids compared with their non-obese counterparts. Interestingly, regardless of the type of macronutrient, only obese women with PCOS showed increased leucine, lysine, phenylalanine and tryptophan levels compared with non-obese patients.

CONCLUSIONS

Serum 1H-NMRS metabolomics profiling indicated sexual dimorphism in the responses to oral macronutrient challenges, which were apparently driven by the central role of postprandial insulin effects with obesity, and to a lesser extent PCOS, exerting modifying roles derived from insulin resistance. Hence, obesity impaired metabolic flexibility in young adults, yet sex and sex hormones also influenced the regulation of postprandial metabolism.

Metabolomics Reveals Novel Serum Metabolic Signatures in Gastric Cancer by a Mass Spectrometry Platform

Gastric cancer (GAS) is one of the malignant tumors of the gastrointestinal system. Alterations in metabolite composition can reflect pathological processes of GAS and constitute a basis for diagnosis and treatment improvements. In this study, a total of 301 serum samples from 150 GAS patients at different tumor-node-metastasis (TNM) stages and 151 healthy controls were collected. Mass spectrometry platforms were performed to investigate the changes in GAS-related metabolites and explore the new potential serum biomarkers and the metabolic dysregulation associated with GAS progression. Twelve differential metabolites (ethyl 2,4-dimethyl-1,3-dioxolane-2-acetate, D-urobilinogen, 14-HDoHE, 13-hydroxy-9-methoxy-10-oxo-11-octadecenoic acid, 5,6-dihydroxyprostaglandin F1a, 9'-carboxy-gamma-tocotrienol, glutaric acid, alanine, tyrosine, C18:2(FFA), adipic acid, and suberic acid) were identified to establish the diagnosis model for GAS. The defined biomarker panel was also statistically significant for GAS progression with different TNM stages. KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment revealed the metabolic dysregulation associated with GAS progression. In conclusion, a diagnostic panel was established and validated, which could be used to further stage the early and advanced GAS patients from healthy controls. These findings may provide useful information for explaining the GAS metabolic alterations and try to facilitate the characterization of GAS patients in the early stage.

Non-targeted metabolomics and associations with per- and polyfluoroalkyl substances (PFAS) exposure in humans: A scoping review

OBJECTIVE

To summarize the application of non-targeted metabolomics in epidemiological studies that assessed metabolite and metabolic pathway alterations associated with per- and polyfluoroalkyl substances (PFAS) exposure.

RECENT FINDINGS

Eleven human studies published before April 1st, 2021 were identified through database searches (PubMed, Dimensions, Web of Science Core Collection, Embase, Scopus), and citation chaining (Citationchaser). The sample sizes of these studies ranged from 40 to 965, involving children and adolescents (n = 3), non-pregnant adults (n = 5), or pregnant women (n = 3). High-resolution liquid chromatography-mass spectrometry was the primary analytical platform to measure both PFAS and metabolome. PFAS were measured in either plasma (n = 6) or serum (n = 5), while metabolomic profiles were assessed using plasma (n = 6), serum (n = 4), or urine (n = 1). Four types of PFAS (perfluorooctane sulfonate(n = 11), perfluorooctanoic acid (n = 10), perfluorohexane sulfonate (n = 9), perfluorononanoic acid (n = 5)) and PFAS mixtures (n = 7) were the most studied. We found that alterations to tryptophan metabolism and the urea cycle were most reported PFAS-associated metabolomic signatures. Numerous lipid metabolites were also suggested to be associated with PFAS exposure, especially key metabolites in glycerophospholipid metabolism which is critical for biological membrane functions, and fatty acids and carnitines which are relevant to the energy supply pathway of fatty acid oxidation. Other important metabolome changes reported included the tricarboxylic acid (TCA) cycle regarding energy generation, and purine and pyrimidine metabolism in cellular energy systems.

CONCLUSIONS

There is growing interest in using non-targeted metabolomics to study the human physiological changes associated with PFAS exposure. Multiple PFAS were reported to be associated with alterations in amino acid and lipid metabolism, but these results are driven by one predominant type of pathway analysis thus require further confirmation. Standardizing research methods and reporting are recommended to facilitate result comparison. Future studies should consider potential differences in study methodology, use of prospective design, and influence from confounding bias and measurement errors.

Validation of a Microwave-Assisted Derivatization Gas Chromatography-Mass Spectrometry Method for the Quantification of 2-Hydroxybutyrate in Human Serum as an Early Marker of Diabetes Mellitus

Circulating levels of 2-hydroxybutyrate (2HB) are highly related to glycemic status in different metabolomic studies. According to recent evidence, 2HB is an early biomarker of the future development of dysglycemia and type 2 diabetes mellitus and may be causally related to the progression of normal subjects to impaired fasting glucose or insulin resistance. In the present study, we developed and validated a simple, specific and sensitive gas chromatography-mass spectrometry (GC-MS) method specifically intended to quantify serum levels of 2HB. Liquid–liquid extraction with ethyl acetate was followed by 2 min of microwave-assisted derivatization. The method presented acceptable accuracy, precision and recovery, and the limit of quantification was 5 µM. Levels of 2HB were found to be stable in serum after three freeze-thaw cycles, and at ambient temperature and at a temperature of 4 °C for up to 24 h. Extracts derivatized under microwave irradiation were stable for up to 96 h. No differences were found in 2HB concentrations measured in serum or plasma EDTA samples. In summary, the method is useful for a rapid, precise and accurate quantification of 2HB in serum samples assessed for the evaluation of dysglycemia and diabetes mellitus.

Antidiabetic effect of an engineered bacterium Lactobacillus plantarum-pMG36e -GLP-1 in monkey model

Glucagon-like peptide-1 (GLP-1) reduces postprandial hyperglycaemia, but its short half-life inhibits clinical application. The aim of the current study was to evaluate the treatment efforts of an engineered strain, Lactobacillus plantarum-pMG36e-GLP-1 (L. plantarum-pMG36e-GLP-1), that continuously expresses GLP-1 in spontaneous type 2 diabetes mellitus (T2DM) monkeys. After 7 weeks of oral supplementation with L. plantarum-pMG36e-GLP-1, the fasting blood glucose (FPG) of monkeys was significantly (p < 0.05) reduced to a normal level and only a small amount of weight was lost. The results of metagenomic sequencing showed that L. plantarum-pMG36e-GLP-1 caused a substantial (p < 0.05) reduction in the intestinal pathogen Prevotella and marked enhancement of butyrate-producing Alistipes genera. According to the functional analysis using Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways, 19 metabolism-related pathways were significantly enriched in T2DM monkeys after treatment with L. plantarum-pMG36e-GLP-1. LC-MS faecal metabolomics analysis found 41 significant differential metabolites (11 higher and 30 lower) in monkeys after treatment pathways linked to the metabolism of cofactors and vitamins were the most relevant. The present study suggests that L. plantarum-pMG36e-GLP-1 had an impact on the gut microbial composition and faecal metabolomic profile in spontaneous T2DM monkeys and may be a novel candidate for diabetes treatment.

Effect of Metabolite Levels on Type 2 Diabetes Mellitus and Glycemic Traits: A Mendelian Randomization Study

OBJECTIVE

To assess the causal associations of plasma levels of metabolites with type 2 diabetes mellitus (T2DM) and glycemic traits.

METHODS

Two-sample mendelian randomization (MR) was conducted to assess the causal associations. Genetic variants strongly associated with metabolites at genome-wide significance level (P < 5 × 10-8) were selected from public genome-wide association studies, and single-nucleotide polymorphisms of outcomes were obtained from the Diabetes Genetics Replication and Meta-analysis consortium for T2DM and from the Meta-Analyses of Glucose and Insulin-related Traits Consortium for fasting glucose, insulin, and glycated hemoglobin (HbA1c). The Wald ratio and inverse-variance weighted methods were used for analyses, and MR-Egger was used for sensitivity analysis.

RESULTS

The β estimates per 1-SD increase of arachidonic acid (AA) level was 0.16 (95% CI, 0.078-0.242; P < 0.001). Genetic predisposition to higher plasma AA levels were associated with higher fasting glucose levels (β 0.10 [95% CI, 0.064-0.134], P < 0.001), higher HbA1c levels (β 0.04 [95% CI, 0.027-0.061]), and lower fasting insulin levels (β -0.025 [95% CI, -0.047 to -0.002], P = 0.033). Besides, 2-hydroxybutyric acid (2-HBA) might have a positive causal effect on glycemic traits.

CONCLUSIONS

Our findings suggest that AA and 2-HBA may have causal associations on T2DM and glycemic traits. This is beneficial for clarifying the pathogenesis of T2DM, which would be valuable for early identification and prevention for T2DM.

Development and application of sequential window acquisition of all theoretical mass spectra data acquisition modes on ultra-high-performance liquid chromatography triple-quadrupole/time-of-flight mass spectrometry for metabolic profiling of amino acids in human plasma

Accumulating evidence suggests that amino acids are important indicators of nutritional and metabolic status. A high-resolution mass spectrometry method based on sequential window acquisition of all theoretical mass spectra acquisition was developed for the simultaneous determination of 16 amino acids in human plasma. Sample preparation by protein precipitation using a mixture of acetonitrile and formic acid was followed by a BEH Amide column. The superiority of this method was investigated by comparing it to time-of-flight scan and multiple reaction monitoring modes. The limit of detection in sequential window acquisition of all theoretical mass spectra mode for threonine, methionine, histidine, citrulline, and tryptophan is 0.1 ng on the column; for lysine and asparagine is 0.2 ng; for alanine, pyroglutamic acid, leucine, ornithine, and aspartate is 0.5 ng, for arginine is 1.0 ng; for glutamate and serine is 2.0 ng; for glutamine is 10.0 ng. This method was linear in the range 0.8-40 μg/mL for arginine, citrulline, glutamate, histidine, leucine, methionine, pyroglutamic acid, threonine, tryptophan; 2-100 μg/mL for asparagine, aspartate, lysine, ornithine, serine; and 4-200 μg/mL for alanine, glutamine with good accuracy and precision. Significantly different levels in 11 amino acids were observed between childhood and adulthood, representing the growth and development of individuals relating to the level of amino acids.

Gas chromatography/mass spectrometry analysis of organic acid profiles in human serum: A protocol of direct ultrasound-assisted derivatization

RATIONALE

Low-molecular-weight organic acids that generally contain one to three carboxyl groups are involved in many important biological processes; therefore, it is important to develop a quantitative method for analyzing organic acids in serum in order to allow an evaluation of metabolic changes. In this study, we evaluated a protocol for detecting 26 organic acids in serum based on ultrasound-assisted derivatization by gas chromatography/mass spectrometry (GC/MS).

METHODS

Serum samples were prepared using ultrasound-assisted silane derivatization before GC/MS analysis to quantify concentrations of organic acids. Additionally, we investigated the variables affecting derivatization yields, including the extraction solvent, derivatization reagents, and derivatization conditions (reaction temperature, duration, and sonication parameters). The protocol was ultimately applied to detect organic acid profiles related to obesity.

RESULTS

We used acetone as the extraction solvent and determined suitable derivatization conditions, as follows: BSTFA + 1% TMCS, 50°C, 10 min, and 100% ultrasound power. The protocol showed satisfactory linearity (r = 0.9958-0.9996), a low limit of detection (0.04-0.42 μmol/L), good reproducibility (coefficient of variation (CV) %: 0.32-13.76%), acceptable accuracy (recovery: 82.97-114.96%), and good stability within 5 days (CV%: 1.35-12.01% at room temperature, 1.24-14.09% at 4°C, and 1.01-11.67% at -20°C). Moreover, the protocol was successfully applied to obtain the organic acid profiles from obese and healthy control subjects.

CONCLUSIONS

We identified and validated a protocol for ultrasound-assisted derivatization prior to GC/MS analysis for detecting 26 kinds of organic acids in serum. The results suggest the efficacy of this protocol for clinical applications to determine metabolic changes related to fluctuations in organic acid profiles.

A novel ISM-SAM strategy, based on gas chromatography/mass spectrometry analysis, to compensate for matrix effects in the determination of pyruvic acid

RATIONALE

The matrix effect is tricky in gas chromatography/mass spectrometry (GC/MS) analyses. Although several methods have been proposed to solve this problem, the results were unsatisfactory. Even fewer studies have assessed the performance of corrective methods. Hence, our study focused on assessing several common corrective methods, and then proposed a new strategy to correct for the matrix effect in GC/MS analyses.

METHODS

In GC/MS analyses, the internal standard method (ISM) was employed to overcome the matrix effect during the detection of pyruvic acid (PA) in serum samples from a healthy adult female. The accuracy of the ISM was evaluated by comparing it with the standard addition method (SAM). To employ the ISM-SAM strategy, correction factors (CFs) were established by combining the ISM and the SAM based on different groups. The CFs were used to normalize data onto the results of subsequent analyses.

RESULTS

When using the ISM to detect levels of PA, a serious bias is observed, thereby affecting the conclusions reached. In contrast, more reliable data can be obtained after normalizing results by undertaking the ISM-SAM strategy. The feasibility of this strategy was verified by comparing it with the results of the SAM alone. The ISM-SAM strategy was successfully applied to quantify the PA levels in healthy people and nephrotic syndrome patients.

CONCLUSIONS

Our results indicated that a false outcome was presented when only the ISM was used to adjust the data, and important information would be missed if the correction strategy was not carried out. Therefore, ISM-SAM, as an available correction method, should be adapted to improve the reliability of research results.

Metabolomics analysis of serum in pediatric nephrotic syndrome based on targeted and non-targeted platforms

BACKGROUND AND AIMS

Nephrotic syndrome (NS) is a common pediatric urinary system disease. The aim in this work was to investigate the changes in pediatric NS-related metabolites through serum metabolomics, and explore the new potential metabolites and differential metabolic pathways.

METHODS

Serum samples from 40 pediatric patients with nephrotic syndrome and 40 healthy controls were collected. The targeted and non-targeted metabolomics analyses were performed to determine the metabolic changes in pediatric NS. Based on multivariate statistical analysis and the regression model, the serum potential metabolites were screened and different metabolic pathways were explored.

RESULTS

39 differential metabolites in pediatric NS were obtained based on the metabolomics analysis. 12 differential metabolites (serine, C18: 2 (EFA), C18: 2 (FFA), Isonuatigenin 3- [rhamnosyl- (1- > 2) -glucoside], C18: 4 (EFA), C18: 4 (FFA), caprylic acid, citric acid, methylmalonic acid, caproic acid, canavalioside and uroporphyrin were identified to establish the diagnostic model for pediatric NS. Five metabolic pathways including TCA cycle, amino acid metabolism, bile acid biosynthesis, linoleate metabolism and glyoxylate and dicarboxylate metabolism were the key differential metabolic pathways.

CONCLUSION

These data elucidated the metabolic alterations associated with pediatric NS and suggested a new diagnosis model for monitoring pediatric NS. The current study provides the useful information to bridge the gaps in our understanding of the metabolic alterations associated with pediatric NS and might facilitate the characterization of pediatric NS patients by performing serum metabolomics.

Serum metabolite signatures of epithelial ovarian cancer based on targeted metabolomics

BACKGROUND

Epithelial ovarian cancer (EOC) is a common gynecological cancer with high mortality rates. The main objective of this study was to investigate the serum amino acid and organic acid profiles to distinguish key metabolites for screening EOC patients.

METHODS

In total, 39 patients with EOC and 31 healthy controls were selected as the training set. Serum amino acid and organic acid profiles were determined using the targeted metabolomics approach. Metabolite profiles were processed via multivariate analysis to identify potential metabolites and construct a metabolic network. Finally, a test dataset derived from 29 patients and 28 healthy controls was constructed to validate the potential metabolites.

RESULTS

Distinct amino acid and organic acid profiles were obtained between EOC and healthy control groups. Methionine, glutamine, asparagine, glutamic acid and glycolic acid were identified as potential metabolites to distinguish EOC from control samples. The areas under the curve for methionine, glutamine, asparagine, glutamic acid and glycolic acid were 0.775, 0 778, 0.955, 0.874 and 0.897, respectively, in the validation study. Metabolic network analysis of the training set indicated key roles of alanine, aspartate and glutamate metabolism as well as D-glutamine and D-glutamate metabolism in the pathogenesis of EOC.

CONCLUSIONS

Amino acid and organic acid profiles may serve as potential screening tools for EOC. Data from this study provide useful information to bridge gaps in the understanding of the amino acid and organic acid alterations associated with epithelial ovarian cancer.

Prediction of Autoimmune Diseases by Targeted Metabolomic Assay of Urinary Organic Acids

Autoimmune diseases (ADs) are chronic disorders characterized by the loss of self-tolerance, and although being heterogeneous, they share common pathogenic mechanisms. Self-antigens and inflammation markers are established diagnostic tools; however, the metabolic imbalances that underlie ADs are poorly described. The study aimed to employ metabolomics for the detection of disease-related changes in autoimmune diseases that could have predictive value. Quantitative analysis of 28 urine organic acids was performed using Gas Chromatography-Mass Spectrometry in a group of 392 participants. Autoimmune thyroiditis, inflammatory bowel disease, psoriasis and rheumatoid arthritis were the most prevalent autoimmune diseases of the study. Statistically significant differences were observed in the tricarboxylate cycle metabolites, succinate, methylcitrate and malate, the pyroglutamate and 2-hydroxybutyrate from the glutathione cycle and the metabolites methylmalonate, 4-hydroxyphenylpyruvate, 2-hydroxyglutarate and 2-hydroxyisobutyrate between the AD group and the control. Artificial neural networks and Binary logistic regression resulted in the highest predictive accuracy scores (66.7% and 74.9%, respectively), while Methylmalonate, 2-Hydroxyglutarate and 2-hydroxybutyrate were proposed as potential biomarkers for autoimmune diseases. Urine organic acid levels related to the mechanisms of energy production and detoxification were associated with the presence of autoimmune diseases and could be an adjunct tool for early diagnosis and prediction.

Metabolite biomarkers of type 2 diabetes mellitus and pre-diabetes: a systematic review and meta-analysis

BACKGROUND

We aimed to explore metabolite biomarkers that could be used to identify pre-diabetes and type 2 diabetes mellitus (T2DM) using systematic review and meta-analysis.

METHODS

Four databases, the Cochrane Library, EMBASE, PubMed and Scopus were selected. A random effect model and a fixed effect model were applied to the results of forest plot analyses to determine the standardized mean difference (SMD) and 95% confidence interval (95% CI) for each metabolite. The SMD for every metabolite was then converted into an odds ratio to create an metabolite biomarker profile.

RESULTS

Twenty-four independent studies reported data from 14,131 healthy individuals and 3499 patients with T2DM, and 14 included studies reported 4844 healthy controls and a total of 2139 pre-diabetes patients. In the serum and plasma of patients with T2DM, compared with the healthy participants, the concentrations of valine, leucine, isoleucine, proline, tyrosine, lysine and glutamate were higher and that of glycine was lower. The concentrations of isoleucine, alanine, proline, glutamate, palmitic acid, 2-aminoadipic acid and lysine were higher and those of glycine, serine, and citrulline were lower in prediabetic patients. Metabolite biomarkers of T2DM and pre-diabetes revealed that the levels of alanine, glutamate and palmitic acid (C16:0) were significantly different in T2DM and pre-diabetes.

CONCLUSIONS

Quantified multiple metabolite biomarkers may reflect the different status of pre-diabetes and T2DM, and could provide an important reference for clinical diagnosis and treatment of pre-diabetes and T2DM.

Metabolomics of Interstitial Fluid, Plasma and Urine in Patients with Arterial Hypertension: New Insights into the Underlying Mechanisms

There is growing evidence that lymphatic system plays a pivotal role in the pathogenesis of hypertension. Here, for the first time, the metabolome of interstitial fluid is analyzed in patients with arterial hypertension. Due to ethical issues to obtain human interstitial fluid samples, this study included only oncological patients after axillary lymph node dissection (ALND). These patients were matched into hypertensive (n = 29) and normotensive (n = 35) groups with similar oncological status. Simultaneous evaluation of interstitial fluid, plasma, and urine was obtained by combining high-resolution proton nuclear magnetic resonance (¹H NMR) spectroscopy with chemometric analysis. Orthogonal partial least squares discriminant analysis (OPLS-DA) provided a clear differentiation between the hypertension and normotensive group, with the discrimination visible in each biofluid. In interstitial fluid nine potential metabolomic biomarkers for hypertension could be identified (creatinine, proline, pyroglutamine, glycine, alanine, 1-methylhistidine, the lysyl group of albumin, threonine, lipids), seven distinct markers in plasma (creatinine, mannose, isobutyrate, glycine, alanine, lactate, acetate, ornithine), and seven respectively in urine (methylmalonate, citrulline, phenylacetylglycine, fumarate, citrate, 1-methylnicotinamide, trans-aconitate). Biomarkers in plasma and urine allowed for the identification of specific biochemical pathways involved in hypertension, as previously suggested. Analysis of the interstitial fluid metabolome provided additional biomarkers compared to plasma or urine. Those biomarkers reflected primarily alterations in the metabolism of lipids and amino acids, and indicated increased levels of oxidative stress/inflammation in patients with hypertension.

Branched-chain amino acids, history of gestational diabetes, and breastfeeding: The Bogalusa Heart Study

BACKGROUND AND AIMS

To examine the associations between history of gestational diabetes mellitus (GDM) and breastfeeding with branched-chain amino acids (BCAA) and their metabolites in later life.

METHODS AND RESULTS

638 women (mean age 48.0 y) who had participated in the Bogalusa Heart Study and substudies of pregnancy history had untargeted, ultrahigh performance liquid chromatography-tandem mass spectroscopy conducted by Metabolon© on serum samples. Metabolites were identified that were BCAA or associated with BCAA metabolic pathways. History of GDM at any pregnancy (self-reported, confirmed with medical records when possible) as well as breastfeeding were examined as predictors of BCAA using linear models, controlling for age, race, BMI, waist circumference, and menopausal status. None of the BCAA differed statistically by history of either GDM or breastfeeding, although absolute levels of each of the BCAA were higher with GDM and lower with breastfeeding. Of the 27 metabolites on the leucine, isoleucine and valine metabolism subpathway, 1-carboxyethylleucine, 1-carboxyethyvaline, and 3-hydroxy-2-ethylpropionate were higher in women with a history of GDM, but lower in women in women with a history of breastfeeding. Similar results were found for alpha-hydroxyisocaproate, 1-carboxyethylisoleucine, and N-acetylleucine.

CONCLUSIONS

GDM and breastfeeding are associated in opposite directions with several metabolites on the BCAA metabolic pathway.

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

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

Glucose challenge metabolomics implicates the change of organic acid profiles in hyperlipidemic subjects

Hyperlipidemia (HLP) is a major risk factor of diabetes and cardiovascular disease. Here, we applied gas chromatography-mass spectrometry to study differences in postprandial organic acid profiles in healthy and HLP subjects. In fasting status, six intermediates of the tricarboxylic acid cycle showed significant differences in HLP and healthy controls (P < 0.05). The percentage changes of 17 metabolites including three intermediates of the tricarboxylic acid cycle were significantly different during the oral glucose tolerance test. Postprandial changes in ethylmalonic acid and pimelic acid were negatively associated with HOMA-IR (homeostasis model assessment of insulin resistance; all P < 0.05) in the HLP group. Postprandial metabolism of organic acid profiles revealed energy metabolism perturbations in HLP. Our findings provide new insights into the complex physiological regulation of HLP postprandial metabolism.

Metabolomic profile of diabetic retinopathy: a GC-TOFMS-based approach using vitreous and aqueous humor

AIM

To identify the potential metabolite markers in diabetic retinopathy (DR) by using gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS).

METHODS

GC-TOFMS spectra were acquired from vitreous and aqueous humor (AH) samples of patients with DR and non-diabetic participants. Comparative analysis was used to elucidate the distinct metabolites of DR. Metabolic pathway was employed to explicate the metabolic reprogramming pathways involved in DR. Logistic regression and receiver-operating characteristic analyses were carried out to select and validate the biomarker metabolites and establish a therapeutic model.

RESULTS

Comparative analysis showed a clear separation between disease and control groups. Eight differentiating metabolites from AH and 15 differentiating metabolites from vitreous were highlighted. Out of these 23 metabolites, 11 novel metabolites have not been detected previously. Pathway analysis identified nine pathways (three in AH and six in vitreous) as the major disturbed pathways associated with DR. The abnormal of gluconeogenesis, ascorbate-aldarate metabolism, valine-leucine-isoleucine biosynthesis, and arginine-proline metabolism might weigh the most in the development of DR. The AUC of the logistic regression model established by D-2,3-Dihydroxypropanoic acid, isocitric acid, fructose 6-phosphate, and L-Lactic acid in AH was 0.965. The AUC established by pyroglutamic acid and pyruvic acid in vitreous was 0.951.

CONCLUSIONS

These findings have expanded our understanding of identified metabolites and revealed for the first time some novel metabolites in DR. These results may provide useful information to explore the mechanism and may eventually allow the development of metabolic biomarkers for prognosis and novel therapeutic strategies for the management of DR.

Homology modeling and site-directed mutagenesis identify amino acid residues underlying the substrate selection mechanism of human monocarboxylate transporters 1 (hMCT1) and 4 (hMCT4)

Human monocarboxylate transporters (hMCTs/SLC16As) mediate the transport of monocarboxylic compounds across plasma membranes. Among the hMCTs, hMCT1 and hMCT4 are expressed in various tissues, and transport substrates involved in energy metabolism. Both transporters mediate L-lactate transport, but, although hMCT1 also transports L-5-oxoproline (L-OPro), this compound is minimally transported by hMCT4. Thus, we were interested in the molecular mechanism responsible for the difference in substrate specificity between hMCT1 and hMCT4. Therefore, we generated 3D structure models of hMCT1 and hMCT4 to identify amino acid residues involved in the substrate specificity of these transporters. We found that the substrate specificity of hMCT1 was regulated by residues involved in turnover number (M69) and substrate affinity (F367), and these residues were responsible for recognizing (directly or indirectly) the -NH- moiety of L-OPro. Furthermore, our homology model of hMCT1 predicted that M69 and F367 participate in hydrophobic interactions with another region of hMCT1, emphasizing its potentially important role in the binding and translocation cycle of L-OPro. Mutagenesis experiments supported this model, showing that efficient L-OPro transport required a hydrophobic, long linear structure at position 69 and a hydrophobic, γ-branched structure at position 367. Our work demonstrated that the amino acid residues, M69 and F367, are key molecular elements for the transport of L-OPro by hMCT1. These two residues may be involved in substrate recognition and/or substrate-induced conformational changes.

Postprandial metabolomics: GC-MS analysis reveals differences in organic acid profiles of impaired fasting glucose individuals in response to highland barley loads

The HB load producing low postprandial glucose and insulin responses brings about several alterations in organic acids.