GC–MS Based Plasma Metabolic Profiling of Type 2 Diabetes Mellitus

Running from depression: the antidepressant-like potential of prenatal and pre-pubertal exercise in adolescent FSL rats exposed to an early-life stressor

OBJECTIVE

We aimed to answer the questions of whether early-life (perinatal and/or juvenile) exercise can induce antidepressant-like effects in a validated rodent model of depression, and whether such early-life intervention could prevent or reverse the adverse effects of early-life stress in their offspring.

METHODS

Male and female Flinders sensitive line rats born to a dam that exercised during gestation, or not, were either maternally separated between PND02 and 16 and weaned on PND17 or not. Half of these animals then underwent a fourteen-day low-intensity exercise regimen from PND22. Baseline depressive-like behaviour was assessed on PND21 and then reassessed on PND36, whereafter hippocampal monoamine levels, redox state markers and metabolic markers relevant to mitochondrial function were measured.

RESULTS

Pre-pubertal exercise was identified as the largest contributing factor to the observed effects, where it decreased immobility time in the FST by 6%, increased time spent in the open arms of the EPM by 9%. Hippocampal serotonin and norepinephrine levels were also increased by 35% and 26%, respectively, whilst nicotinic acid was significantly decreased.

CONCLUSION

These findings suggest that pre-pubertal low-intensity exercise induces beneficial biological alterations that could translate into antidepressant behaviour in genetically susceptible individuals.

In Vitro, In Silico and Network Pharmacology Mechanistic Approach to Investigate the α-Glucosidase Inhibitors Identified by Q-ToF-LCMS from Phaleria macrocarpa Fruit Subcritical CO2 Extract

The fruit of Phaleria macrocarpa have been traditionally used as an antidiabetic remedy in Malaysia and neighbouring countries. Despite its potential for diabetes treatment, no scientific study has ever been conducted to predict the inhibitor interaction of the protein α-glucosidase identified in an extract prepared with a non-conventional extraction technique. Hence, the major aim of this research was to evaluate the in vitro antioxidant, the α-glucosidase inhibitors, and the molecular dynamic simulations of the α-glucosidase inhibitors identified by Quadrupole Time-of-Flight Liquid Chromatography Mass Spectrometry (Q-ToF-LCMS) analysis. Initially, dry fruit were processed using non-conventional and conventional extraction methods to obtain subcritical carbon dioxide extracts (SCE-1 and SCE-2) and heating under reflux extract (HRE), respectively. Subsequently, all extracts were evaluated for their in vitro antioxidative and α-glucosidase inhibitory potentials. Subsequently, the most bioactive extract (SCE-2) was subjected to Q-ToF-LCMS analysis to confirm the presence of α-glucosidase inhibitors, which were then analysed through molecular dynamic simulations and network pharmacology approaches to confirm their possible mechanism of action. The highest inhibitory effects of the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical and α-glucosidase on SCE-2 was found as 75.36 ± 0.82% and 81.79 ± 0.82%, respectively, compared to the SCE-1 and HRE samples. The Q-ToF-LCMS analysis tentatively identified 14 potent α-glucosidase inhibitors. Finally, five identified compounds, viz., lupenone, swertianolin, m-coumaric acid, pantothenic acid, and 8-C-glucopyranosyleriodictylol displayed significant stability, compactness, stronger protein-ligand interaction up to 100 ns further confirming their potential as α-glucosidase inhibitors. Consequently, it was concluded that the SCE-2 possesses a strong α-glucosidase inhibitory effect due to the presence of these compounds. The findings of this study might prove useful to develop these compounds as alternative safe α-glucosidase inhibitors to manage diabetes more effectively.

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.

Sustainable Enzymatic Synthesis of a Solketal Ester—Process Optimization and Evaluation of Its Antimicrobial Activity

The present study aims the enzymatic synthesis of solketal palmitate by esterification between solketal and palmitic acid using heptane as solvent. Lipases from Thermomyces lanuginosus (TLL), Candida rugosa type VII (CRL), and Pseudomonas fluorescens (PFL) were immobilized via interfacial activation on rice husk silica functionalized with triethoxy(octyl)silane (Octyl–SiO2) and used as biocatalysts. A loading of 20–22 mg of lipase/g of support was immobilized independently of the studied enzyme. TLL–Octyl–SiO2 was the most active biocatalyst in oil hydrolysis (656.0 ± 23.9 U/g) and ester synthesis (productivity of 6.8 mmol/min.gbiocat), and it has been chosen for further ester synthesis optimization. The effect of some important parameters such as biocatalyst concentration, reaction temperature and acid:alcohol molar ratio on the reaction has been evaluated using a central composite rotatable design at fixed mechanical stirring (240 rpm) and reaction time (15 min). Subsequently, the effect of reactants concentration and molecular sieve concentration has also been examined. Under optimal conditions (56 °C, acid:alcohol molar ratio of 1:3 with a palmitic acid concentration of 1 M, and 20% wt. of TLL–Octyl–SiO2 per volume of reaction mixture), 83% acid conversion was obtained after 150 min of reaction. The biocatalyst retained 87% of its initial activity after seven successive reaction batches. The product was identified by nuclear magnetic resonance analysis. Antimicrobial activity studies showed that the synthesized ester demonstrated antifungal activity against Candida albicans and Candida parapsilosis, with minimum inhibitory concentration (MIC) between 200 and 400 µg/mL, and bacteriostatic/fungistatic action—minimum microbicial concentration (MMC) > 400 µg/mL.

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.

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

The aim of this study was to develop a method to detect serum organic acid profiles in patients with isolated post-challenge diabetes (IPD) and to compare the metabolites between IPD patients, type 2 diabetes mellitus (T2DM) and healthy controls. We developed a gas chromatography-mass spectrometry method to detect serum organic acids and validated it using serum from 40 patients with IPD, 47 with newly diagnosed T2DM, and 48 healthy controls. We then analyzed the organic acid profiles by multivariate analysis to identify potential metabolites. This method allowed the fast and accurate measurement of 27 organic acids in serum. Serum organic acid profiles differed significantly among IPD patients, T2DM patients, and healthy controls. IPD samples had significantly higher concentrations of α‑hydroxybutyrate and β‑hydroxybutyrate (P < 0.05) and lower pyroglutamic acid concentration (P < 0.05) compared with the healthy controls, and the area under the curve for the combination of α‑hydroxybutyrate and pyroglutamic acid was 0.863 for the IPD group. These results provide useful information regarding the changes in organic acid metabolism associated with IPD. Measurement of these metabolites in fasting serum from IPD patients may provide useful diagnostic and/or prognostic biomarkers, as well as helpful markers for the therapeutic monitoring of IPD patients.

Association of cultured myotubes and fasting plasma metabolite profiles with mitochondrial dysfunction in type 2 diabetes subjects

Accumulating evidence implicates mitochondrial dysfunction-induced insulin resistance in skeletal muscle as the root cause for the greatest hallmarks of type 2 diabetes (T2D). However, the identification of specific metabolite-based markers linked to mitochondrial dysfunction in T2D has not been adequately addressed. Therefore, we sought to identify the markers-based metabolomics for mitochondrial dysfunction associated with T2D. First, a cellular disease model was established using human myotubes treated with antimycin A, an oxidative phosphorylation inhibitor. Non-targeted metabolomic profiling of intracellular-defined metabolites on the cultured myotubes with mitochondrial dysfunction was then determined. Further, a targeted MS-based metabolic profiling of fasting blood plasma from normal (n = 32) and T2D (n = 37) subjects in a cross-sectional study was verified. Multinomial logical regression analyses for defining the top 5% of the metabolites within a 95% group were employed to determine the differentiating metabolites. The myotubes with mitochondrial dysfunction exhibited insulin resistance, oxidative stress and inflammation with impaired insulin signalling activities. Four metabolic pathways were found to be strongly associated with mitochondrial dysfunction in the cultured myotubes. Metabolites derived from these pathways were validated in an independent pilot investigation of the fasting blood plasma of healthy and diseased subjects. Targeted metabolic analysis of the fasting blood plasma with specific baseline adjustment revealed 245 significant features based on orthogonal partial least square discriminant analysis (PLS-DA) with a p-value < 0.05. Among these features, 20 significant metabolites comprised primarily of branched chain and aromatic amino acids, glutamine, aminobutyric acid, hydroxyisobutyric acid, pyroglutamic acid, acylcarnitine species (acetylcarnitine, propionylcarnitine, dodecenoylcarnitine, tetradecenoylcarnitine hexadecadienoylcarnitine and oleylcarnitine), free fatty acids (palmitate, arachidonate, stearate and linoleate) and sphingomyelin (d18:2/16:0) were identified as predictive markers for mitochondrial dysfunction in T2D subjects. The current study illustrates how cellular metabolites provide potential signatures associated with the biochemical changes in the dysregulated body metabolism of diseased subjects. Our finding yields additional insights into the identification of robust biomarkers for T2D associated with mitochondrial dysfunction in cultured myotubes.

Impact of statistical models on the prediction of type 2 diabetes using non-targeted metabolomics profiling

OBJECTIVE

Characterizing specific metabolites in sub-clinical phases preceding the onset of type 2 diabetes to enable efficient preventive and personalized interventions.

RESEARCH DESIGN AND METHODS

We developed predictive models of type 2 diabetes using two strategies. One strategy focused on the probability of incidence only and was based on logistic regression (MRS1); the other strategy accounted for the age at diagnosis of diabetes and was based on Cox regression (MRS2). We assessed 293 metabolites using non-targeted metabolomics in fasting plasma samples of 1,044 participants (including 231 incident cases over 9 years) used as training population; and fasting serum samples of 128 participants (64 incident cases versus 64 controls) used as validation population. We applied a LASSO-based variable selection aiming at maximizing the out-of-sample area under the receiver operating characteristic curve (AROC) and integrated AROC.

RESULTS

Sixteen and 17 metabolites were selected for MRS1 and MRS2, respectively, with AROC = 90% and 73% in the training and validation populations, respectively for MRS1. MRS2 had a similar performance and was significantly associated with a younger age of onset of type 2 diabetes (β = -3.44 years per MRS2 SD in the training population, p = 1.56 × 10(-7); β = -4.73 years per MRS2 SD in the validation population, p = 4.04 × 10(-3)).

CONCLUSIONS

Overall, this study illustrates that metabolomics improves prediction of type 2 diabetes incidence of 4.5% on top of known clinical and biological markers, reaching 90% in total AROC, which is considered the threshold for clinical validity, suggesting it may be used in targeting interventions to prevent type 2 diabetes.

Application of GC-MS coupled with chemometrics for scanning serum metabolic biomarkers from renal fibrosis rat

Renal interstitial fibrosis closely relates to chronic kidney disease and is regarded as the final common pathway in most cases of end-stage renal disease. Metabolomic biomarkers can facilitate early diagnosis and allow better understanding of the pathogenesis underlying renal fibrosis. Gas chromatography-mass spectrometry (GC/MS) is one of the most promising techniques for identification of metabolites. However, the existence of the background, baseline offset, and overlapping peaks makes accurate identification of the metabolites unachievable. In this study, GC/MS coupled with chemometric methods was successfully developed to accurately identify and seek metabolic biomarkers for rats with renal fibrosis. By using these methods, seventy-six metabolites from rat serum were accurately identified and five metabolites (i.e., urea, ornithine, citric acid, galactose, and cholesterol) may be useful as potential biomarkers for renal fibrosis.

Milk consumption during pregnancy increases birth weight, a risk factor for the development of diseases of civilization

Antenatal dietary lifestyle intervention and nutrition during pregnancy and early postnatal life are important for appropriate lifelong metabolic programming. Epidemiological evidence underlines the crucial role of increased birth weight as a risk factor for the development of chronic diseases of civilization such as obesity, diabetes and cancer. Obstetricians and general practitioners usually recommend milk consumption during pregnancy as a nutrient enriched in valuable proteins and calcium for bone growth. However, milk is not just a simple nutrient, but has been recognized to function as an endocrine signaling system promoting anabolism and postnatal growth by activating the nutrient-sensitive kinase mTORC1. Moreover, pasteurized cow’s milk transfers biologically active exosomal microRNAs into the systemic circulation of the milk consumer apparently affecting more than 11 000 human genes including the mTORC1-signaling pathway. This review provides literature evidence and evidence derived from translational research that milk consumption during pregnancy increases gestational, placental, fetal and birth weight. Increased birth weight is a risk factor for the development of diseases of civilization thus involving key disciplines of medicine. With regard to the presented evidence we suggest that dietary recommendations promoting milk consumption during pregnancy have to be re-evaluated.

Chemoenzymatic Solvent-free Synthesis of 1-Monopalmitin Using a Microwave Reactor

An environmentally friendly method for the synthesis of 1-monopalmitin has been developed. The procedure consists of a two-step, solvent-free chemoenzymatic reaction. In the first step, palmitic acid is esterified with solketal (4-hydroxymethyl-2,2-dimethyl-1,3-dioxolane) using Novozym 435 by both conventional heating and microwave irradiation. The use of a microwave reactor allows the enzymatic synthesis of the intermediate compound with a similar yield as that achieved using conventional heating. In the second step, 1,2-acetonide-3-palmitoyl glycerol is cleaved to yield 1-monopalmitin by means of a cation-exchange resin and water or aliphatic alcohols as hydrolytic reagent in solvent-free conditions. The hydrolysis was accomplished in 15 min at 85°C. The best yield was obtained using 1-pentanol. We conclude that the yield achieved depends on the batch and nature of the cation-exchange resin used as catalyst.

Type 2 diabetes associated changes in the plasma non-esterified fatty acids, oxylipins and endocannabinoids

Type 2 diabetes has profound effects on metabolism that can be detected in plasma. While increases in circulating non-esterified fatty acids (NEFA) are well-described in diabetes, effects on signaling lipids have received little attention. Oxylipins and endocannabinoids are classes of bioactive fatty acid metabolites with many structural members that influence insulin signaling, adipose function and inflammation through autocrine, paracrine and endocrine mechanisms. To link diabetes-associated changes in plasma NEFA and signaling lipids, we quantitatively targeted >150 plasma lipidome components in age- and body mass index-matched, overweight to obese, non-diabetic (n = 12) and type 2 diabetic (n = 43) African-American women. Diabetes related NEFA patterns indicated ∼60% increase in steroyl-CoA desaturase activity and ∼40% decrease in very long chain polyunsaturated fatty acid chain shortening, patterns previously associated with the development of nonalcoholic fatty liver disease. Further, epoxides and ketones of eighteen carbon polyunsaturated fatty acids were elevated >80% in diabetes and strongly correlated with changes in NEFA, consistent with their liberation during adipose lipolysis. Endocannabinoid behavior differed by class with diabetes increasing an array of N-acylethanolamides which were positively correlated with pro-inflammatory 5-lipooxygenase-derived metabolites, while monoacylglycerols were negatively correlated with body mass. These results clearly show that diabetes not only results in an increase in plasma NEFA, but shifts the plasma lipidomic profiles in ways that reflect the biochemical and physiological changes of this pathological state which are independent of obesity associated changes.

A metabolic profiling strategy for biomarker screening by GC-MS combined with multivariate resolution method and Monte Carlo

A GC-MS based metabolic profiling method via a multivariate resolution method and Monte Carlo PLS-DA is proposed for screening potential biomarkers, and applied to Type 2 diabetes mellitus. The metabolic profiles of plasma samples from healthy control and Type 2 diabetes mellitus patient groups were obtained by GC-MS, and 25 compounds considered as endogenous metabolites excluding glucose were identified. With the help of a multivariate resolution method, qualitative and quantitative results of the metabolic profiles were extracted for subsequent multivariate statistical analysis. In order to select potential biomarkers, responsible for the classification of the two groups, Monte Carlo PLS-DA was introduced. The distribution of the regression coefficients of PLS-DA models corresponding to the metabolites was obtained. The levels of metabolites with all positive coefficients were considered as decreased from healthy controls to patients, and all negative coefficients were considered as increased. Univariate t-test was employed to check for metabolites whose levels changed significantly. Metabolites identified as potential biomarkers of Type 2 diabetes mellitus were ten in total, namely lactate, alanine, α-hydroxyisobutyric acid, phosphate, serine, pyroglutamic acid, palmitic acid, stearic acid, 1-monopalmitin and cholesterol. Finally, canonical correlation analysis was used to explore the correlation between the selected ten metabolites and blood glucose, which was considered to be a routine parameter reflecting the disease state. The results showed that the ten selected metabolites correlated well with blood glucose (r = 0.81, p = 0.03), and may be considered as possible biomarkers of Type 2 diabetes mellitus. The results demonstrated that the proposed method may be a useful tool to discover potential biomarkers of diseases.

Plasma metabolic fingerprinting of childhood obesity by GC/MS in conjunction with multivariate statistical analysis

Metabolic fingerprinting is a powerful tool for exploring systemic metabolic perturbations and potential biomarkers, thus may shed light on the pathophysiological mechanism of diseases. In this work, a new strategy of metabolic fingerprinting was proposed to exploit the disturbances of metabolic patterns and biomarker candidates of childhood obesity. Plasma samples from children with normal weight, overweight and obesity were first profiled by GC/MS. ULDA (uncorrelated linear discriminant analysis) then revealed that the metabolic patterns of the three groups were different. Furthermore, several metabolites, say isoleucine, glyceric acid, serine, 2,3,4-trihydroxybutyric acid and phenylalanine were screened as potential biomarkers of childhood obesity by both ULDA and CCA (canonical correlation analysis). CCA also shows satisfactory correlation between the metabolic patterns and clinical parameters, and the results further suggest that WHR (waist-hip ratio) together with TG (total triglycerides), TC (total cholesterol), HDL (high density lipoprotein) and LDL (low density lipoprotein) were the most important parameters which are associated closely with the metabolic perturbations of childhood obesity, so as to be paid more attention for dealing with metabolic disturbances of childhood obesity in clinical practice rather than regularly monitored BMI (body-mass index). The results have demonstrated that the proposed metabolic fingerprinting approach may be a useful tool for discovering metabolic abnormalities and possible biomarkers for childhood obesity.