A multi-analytical platform approach to the metabonomic analysis of plasma from normal and zucker (fa/fa) obese rats

Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Metabolomic Study of Aging in fa/fa Rats: Multiplatform Urine and Serum Analysis

Zucker fatty (fa/fa) rats represent a well-established and widely used model of genetic obesity. Because previous metabolomic studies have only been published for young fa/fa rats up to 20 weeks of age, which can be considered early maturity in male fa/fa rats, the aim of our work was to extend the metabolomic characterization to significantly older animals. Therefore, the urinary profiles of obese fa/fa rats and their lean controls were monitored using untargeted NMR metabolomics between 12 and 40 weeks of age. At the end of the experiment, the rats were also characterized by NMR and LC-MS serum analysis, which was supplemented by a targeted LC-MS analysis of serum bile acids and neurotransmitters. The urine analysis showed that most of the characteristic differences detected in young obese fa/fa rats persisted throughout the experiment, primarily through a decrease in microbial co-metabolite levels, the upregulation of the citrate cycle, and changes in nicotinamide metabolism compared with the age-related controls. The serum of 40-week-old obese rats showed a reduction in several bile acid conjugates and an increase in serotonin. Our study demonstrated that the fa/fa model of genetic obesity is stable up to 40 weeks of age and is therefore suitable for long-term experiments.

The metabolomics of a protein kinase C delta (PKCδ) knock-out mouse model

INTRODUCTION

PKCδ is ubiquitously expressed in mammalian cells and its dysregulation plays a key role in the onset of several incurable diseases and metabolic disorders. However, much remains unknown about the metabolic pathways and disturbances induced by PKC deficiency, as well as the metabolic mechanisms involved.

OBJECTIVES

This study aims to use metabolomics to further characterize the function of PKC from a metabolomics standpoint, by comparing the full serum metabolic profiles of PKC deficient mice to those of wild-type mice.

METHODS

The serum metabolomes of PKCδ knock-out mice were compared to that of a wild-type strain using a GCxGC-TOFMS metabolomics research approach and various univariate and multivariate statistical analyses.

RESULTS

Thirty-seven serum metabolite markers best describing the difference between PKCδ knock-out and wild-type mice were identified based on a PCA power value > 0.9, a t-test p-value < 0.05, or an effect size > 1. XERp prediction was also done to accurately select the metabolite markers within the 2 sample groups. Of the metabolite markers identified, 78.4% (29/37) were elevated and 48.65% of these markers were fatty acids (18/37). It is clear that a total loss of PKCδ functionality results in an inhibition of glycolysis, the TCA cycle, and steroid synthesis, accompanied by upregulation of the pentose phosphate pathway, fatty acids oxidation, cholesterol transport/storage, single carbon and sulphur-containing amino acid synthesis, branched-chain amino acids (BCAA), ketogenesis, and an increased cell signalling via N-acetylglucosamine.

CONCLUSION

The charaterization of the dysregulated serum metabolites in this study, may represent an additional tool for the early detection and screening of PKCδ-deficiencies or abnormalities.

Tryptophan-5-HT pathway disorder was uncovered in the olfactory bulb of a depression mice model by metabolomic analysis

Major depression (MD) is a severe mental illness that creates a heavy social burden, and the potential molecular mechanisms remain largely unknown. Lots of research demonstrate that the olfactory bulb is associated with MD. Recently, gas chromatography-mass spectrometry-based metabolomic studies on depressive rats indicated that metabolisms of purine and lipids were disordered in the olfactory bulb. With various physicochemical properties and extensive concentration ranges, a single analytical technique could not completely cover all metabolites, hence it is necessary to adopt another metabolomic technique to seek new biomarkers or molecular mechanisms for depression. Therefore, we adopted a liquid chromatography-mass spectrometry metabonomic technique in the chronic mild stress (CMS) model to investigate significant metabolic changes in the olfactory bulb of the mice. We discovered and identified 16 differential metabolites in the olfactory bulb of the CMS treatments. Metabolic pathway analysis by MetaboAnalyst 5.0 was generated according to the differential metabolites, which indicated that the tryptophan metabolism pathway was the core pathogenesis in the olfactory bulb of the CMS depression model. Further, the expressions of tryptophan hydroxylase (TpH) and aromatic amino acid decarboxylase (AAAD) were detected by western blotting and immunofluorescence staining. The expression of TpH was increased after CMS treatment, and the level of AAAD was unaltered. These results revealed that abnormal metabolism of the tryptophan pathway in the olfactory bulb mediated the occurrence of MD.

Urinary metabolite signature in bipolar disorder patients during depressive episode

The first few episodes of bipolar disorder (BD) are highly likely to be depressive. This phenomenon causes many BD patients to be misdiagnosed as having major depression. Therefore, it is very important to correctly diagnose BD patients during depressive episode. Here, we conducted this study to identify potential biomarkers for young and middle-aged BD patients during depressive episode. Both gas chromatography-mass spectroscopy (GC-MS) and nuclear magnetic resonance (NMR) spectroscopy were used to profile the urine samples from the recruited subjects. In total, 13 differential metabolites responsible for the discrimination between healthy controls (HCs) and patients were identified. Most differential metabolites had a close relationship with energy homeostasis. Meanwhile, a panel consisting of five differential metabolites was identified. This panel could effectively distinguish the patients from HCs with an AUC of 0.998 in the training set and 0.974 in the testing set. Our findings on one hand could be helpful in developing an objective diagnostic method for young and middle-aged BD patients during depressive episode; on the other hand could provide critical insight into the pathological mechanism of BD and the biological mechanisms responsible for the transformation of different episodes.

Dual Metabolomic Platforms Identified a Novel Urinary Metabolite Signature for Hepatitis B Virus-Infected Patients with Depression

OBJECTIVE

Depression could make the treatment outcome worse. However, up to now, no objective methods were developed to diagnose depression in hepatitis B virus (HBV)-infected patients. Therefore, the dual metabolomic platforms were used here to identify potential biomarkers for diagnosing HBV-infected patients with depression (dHB).

METHODS

Both gas chromatography-mass spectrometry-based and nuclear magnetic resonance-based metabolomic platforms were used to conduct urine metabolic profiling of dHB subjects and HBV-infected patients without depression (HB). Orthogonal partial least-squares discriminant analysis was used to identify the differential metabolites between dHB subjects and HB subjects, and the step-wise logistic regression analysis was used to identify potential biomarkers.

RESULTS

In total, 21 important metabolites responsible for distinguishing dHB subjects from HB subjects were identified. Meanwhile, seven potential biomarkers (α-ydroxyisobutyric acid, hippuric acid, azelaic acid, isobutyric acid, malonic acid, levulinic acid, and phenylacetylglycine) were viewed as potential biomarkers. The simplified biomarker panel consisting of these seven metabolites had an excellent diagnostic performance in discriminating dHB subjects from HB subjects. Moreover, this panel could yield a higher accuracy in separating dHB subjects from HB subjects than our previous panels (identified by single metabolomic platform) did.

CONCLUSION

These results suggested that the dual metabolomic platforms could yield a better urinary biomarker panel for dHB subjects than any single metabolomic platform did, and our results could be helpful for developing an objective method in future to diagnose depression in HBV-infected patients.

GC-MS-based metabolomics approach to diagnose depression in hepatitis B virus-infected patients with middle or old age

Depression is concomitantly presented in hepatitis B virus (HBV)-infected patients (HB). However, there is still no objective method to diagnose HBV-infected patients with depression (dHB). Therefore, in this study, a gas chromatography-mass spectrometry (GC-MS)-based metabolomic approach was employed to profile urine samples from 59 dHB and 52 HB (the training set) in order to identify urinary metabolite biomarkers for dHB. Then, 41 dHB and 35 HB (the testing set) were used to independently validate the diagnostic generalizability of these biomarkers. In total, 13 differential metabolites responsible for the discrimination between dHB and HB were identified. These differential urinary metabolites belonged mainly to Lipid metabolism and Amino acid metabolism. A panel consisting of six urinary metabolite biomarkers (ethanolamine, azelaic acid, histidine, threitol, 2,4-dihydroxypyrimidine and levulinic acid) was identified. This panel was capable of distinguishing dHB from HB with an area under the receiver operating characteristic curve (AUC) of 0.986 in the training set. Moreover, this panel could classify blinded samples from the testing set with an AUC of 0.933. These findings indicated that the GC-MS-based metabolomics approach could be a useful tool in the clinical diagnosis of dHB, and the identified biomarkers were helpful for future developing an objective diagnostic method for dHB.

Presoaking dried blood spot with water improves efficiency for small-molecule analysis

The current method of extracting small molecules from dried blood spots (DBSs) and liquid blood is similar. However, owing to their different physical characteristics, a modification of the extraction process for DBS is required. We propose a modified method involving presoaking in water that results in better extraction efficiency for small-molecule analysis than the conventional protein precipitation method. Using blood and DBSs from eight subjects, the similarities, recovery rates and extraction efficiencies of both methods were compared. Quantitative analysis showed that seven and six out of ten conditions for the modified method group exhibited almost 100% recovery and extraction efficiency rates, respectively, compared with the conventional method group. Taken together, the results suggest that a presoaking step is needed for efficient DBS analysis.

Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

INTRODUCTION

Obesity is a disorder characterized by a disproportionate increase in body weight in relation to height, mainly due to the accumulation of fat, and is considered a pandemic of the present century by many international health institutions. It is associated with several non-communicable chronic diseases, namely, metabolic syndrome, type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and cancer. Metabolomics is a useful tool to evaluate changes in metabolites due to being overweight and obesity at the body fluid and cellular levels and to ascertain metabolic changes in metabolically unhealthy overweight and obese individuals (MUHO) compared to metabolically healthy individuals (MHO).

OBJECTIVES

We aimed to conduct a systematic review (SR) of human studies focused on identifying metabolomic signatures in obese individuals and obesity-related metabolic alterations, such as inflammation or oxidative stress.

METHODS

We reviewed the literature to identify studies investigating the metabolomics profile of human obesity and that were published up to May 7th, 2019 in SCOPUS and PubMed through an SR. The quality of reporting was evaluated using an adapted of QUADOMICS.

RESULTS

Thirty-three articles were included and classified according to four types of approaches. (i) studying the metabolic signature of obesity, (ii) studying the differential responses of obese and non-obese subjects to dietary challenges (iii) studies that used metabolomics to predict weight loss and aimed to assess the effects of weight loss interventions on the metabolomics profiles of overweight or obese human subjects (iv) articles that studied the effects of specific dietary patterns or dietary compounds on obesity-related metabolic alterations in humans.

CONCLUSION

The present SR provides state-of-the-art information about the use of metabolomics as an approach to understanding the dynamics of metabolic processes involved in human obesity and emphasizes metabolic signatures related to obesity phenotypes.

Obesity and Cage Environment Modulate Metabolism in the Zucker Rat: A Multiple Biological Matrix Approach to Characterizing Metabolic Phenomena

Obesity and its comorbidities are increasing worldwide imposing a heavy socioeconomic burden. The effects of obesity on the metabolic profiles of tissues (liver, kidney, pancreas), urine, and the systemic circulation were investigated in the Zucker rat model using ¹H NMR spectroscopy coupled to multivariate statistical analysis. The metabolic profiles of the obese ( fa/ fa) animals were clearly differentiated from the two phenotypically lean phenotypes, ((+/+) and ( fa/+)) within each biological compartment studied, and across all matrices combined. No significant differences were observed between the metabolic profiles of the genotypically distinct lean strains. Obese Zucker rats were characterized by higher relative concentrations of blood lipid species, cross-compartmental amino acids (particularly BCAAs), urinary and liver metabolites relating to the TCA cycle and glucose metabolism; and lower amounts of urinary gut microbial-host cometabolites, and intermatrix metabolites associated with creatine metabolism. Further to this, the obese Zucker rat metabotype was defined by significant metabolic alterations relating to disruptions in the metabolism of choline across all compartments analyzed. The cage environment was found to have a significant effect on urinary metabolites related to gut-microbial metabolism, with additional cage-microenvironment trends also observed in liver, kidney, and pancreas. This study emphasizes the value in metabotyping multiple biological matrices simultaneously to gain a better understanding of systemic perturbations in metabolism, and also underscores the need for control or evaluation of cage environment when designing and interpreting data from metabonomic studies in animal models.

Serum metabolomics analysis of patients with polycystic ovary syndrome by mass spectrometry

Polycystic ovary syndrome (PCOS) is a set of symptoms caused by elevated androgens (male hormones) in females. PCOS is the most common endocrine disorder among women between 18 and 44 years. Currently, the pathogenesis of PCOS remains unclear. Liquid chromatography-mass spectrometry (LC/MS)-based metabolomics is becoming more and more useful for medical research, especially in revealing the mechanism of the disease. The aim of this study was to investigate the difference of serum metabolic profiles in patients with PCOS and healthy control to better understand the mechanism of this disease. Ten patients with PCOS and 10 healthy people were recruited for this study. The serum samples were collected for LC/MS analysis. Multivariate statistical analysis was performed to discover and identify the potential biomarkers. Six biomarkers were found and identified. The biomarkers belonged to different metabolic pathway including lipid metabolism, carnitine metabolism, androgen metabolism, and bile acid metabolism. Those biomarkers also played different roles in disease progression. Metabolomics is a powerful tool used in research of the mechanism involved in this disease to provide useful information for better understanding of PCOS.

Urinary biomarker panel for diagnosing patients with depression and anxiety disorders

Available data indicate that patients with depression and anxiety disorders are likely to be at greater risk for suicide. Therefore, it is important to correctly diagnose patients with depression and anxiety disorders. However, there are still no empirical laboratory methods to objectively diagnose these patients. In this study, the multiple metabolomics platforms were used to profile the urine samples from 32 healthy controls and 32 patients with depression and anxiety disorders for identifying differential metabolites and potential biomarkers. Then, 16 healthy controls and 16 patients with depression and anxiety disorders were used to independently validate the diagnostic performance of the identified biomarkers. Finally, a panel consisting of four biomarkers-N-methylnicotinamide, aminomalonic acid, azelaic acid and hippuric acid-was identified. This panel was capable of distinguishing patients with depression and anxiety disorders from healthy controls with an area under the receiver operating characteristic curve of 0.977 in the training set and 0.934 in the testing set. Meanwhile, we found that these identified differential metabolites were mainly involved in three metabolic pathways and five molecular and cellular functions. Our results could lay the groundwork for future developing a urine-based diagnostic method for patients with depression and anxiety disorders.

Metabolomic Analysis of Biochemical Changes in the Serum and Urine of Freund's Adjuvant-Induced Arthritis in Rats after Treatment with Silkworm Excrement

Silkworm excrement (SE), is used as a traditional antirheumatic medicine in China. The present study was designed to investigate the therapeutic efficacy of water fraction of SE (ST) and ethanol fraction of SE (CT) at two different doses on adjuvant induced arthritis (AA) rats. Arthritis severity was evaluated by body weight, paw thickness, histological changes and index of paws oedema and spleen. Serum samples were collected for estimation of biochemical indicators and cytokines. In addition, a metabonomic method based on the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) had been established to investigate the holistic efficacy of SE by serum and urine. Multivariate statistical approaches, such as partial least-squares discriminant analysis (PLS-DA) and orthogonal projection to latent structures squares-discriminant analysis (OPLS-DA) were built to evaluate the therapeutic effects of SE and find potential biomarkers and metabolic pathways. Administration with SE significantly ameliorated the AA severity, including body weight loss, paw swelling, histological changes and the levels of biochemical index. 33 endogenous metabolites had been identified (10 in serum and 23 in urine) in the AA rats. Urinary and serum metabolic profiling revealed that the metabolites underpin the metabolic pathway including nicotinate and nicotinamide metabolism; pentose and glucuronate interconversions; TCA cycle; beta-Alanine metabolism; purine metabolism and glycolysis or gluconeogenesis. The altered metabolites could be regulated closer to normal level after SE intervention. The results suggested SE possesses substantial anti-arthritic activity and demonstrated that metabonomics is a powerful tool to gain insight in the mechanism of SE formula in therapy.

Protein kinase C-delta (PKCδ), a marker of inflammation and tuberculosis disease progression in humans, is important for optimal macrophage killing effector functions and survival in mice

We previously demonstrated that protein kinase C-δ (PKCδ) is critical for immunity against Listeria monocytogenes, Leishmania major, and Candida albicans infection in mice. However, the functional relevance of PKCδ during Mycobacterium tuberculosis (Mtb) infection is unknown. PKCδ was significantly upregulated in whole blood of patients with active tuberculosis (TB) disease. Lung proteomics further revealed that PKCδ was highly abundant in the necrotic and cavitory regions of TB granulomas in multidrug-resistant human participants. In murine Mtb infection studies, PKCδ^-/- mice were highly susceptible to tuberculosis with increased mortality, weight loss, exacerbated lung pathology, uncontrolled proinflammatory cytokine responses, and increased mycobacterial burdens. Moreover, these mice displayed a significant reduction in alveolar macrophages, dendritic cells, and decreased accumulation of lipid bodies (lungs and macrophages) and serum fatty acids. Furthermore, a peptide inhibitor of PKCδ in wild-type mice mirrored lung inflammation identical to infected PKCδ^-/- mice. Mechanistically, increased bacterial growth in macrophages from PKCδ^-/- mice was associated with a decline in killing effector functions independent of phagosome maturation and autophagy. Taken together, these data suggest that PKCδ is a marker of inflammation during active TB disease in humans and required for optimal macrophage killing effector functions and host protection during Mtb infection in mice.

Differential urinary metabolites related with the severity of major depressive disorder

Major depressive disorder (MDD) is a common mental disorder that affects a person's general health. However, there is still no objective laboratory test for diagnosing MDD. Here, an integrated analysis of data from our previous studies was performed to identify the differential metabolites in the urine of moderate and severe MDD patients. A dual platform approach (NMR spectroscopy and GC-MS) was used. Consequently, 14 and 22 differential metabolites responsible for separating moderate and severe MDD patients, respectively, from their respective healthy controls (HCs) were identified. Meanwhile, the moderate MDD-specific panel (N-Methylnicotinamide, Acetone, Choline, Citrate, vanillic acid and azelaic acid) and severe MDD-specific panel (indoxyl sulphate, Taurine, Citrate, 3-hydroxyphenylacetic acid, palmitic acid and Lactate) could discriminate moderate and severe MDD patients, respectively, from their respective HCs with high accuracy. Moreover, the differential metabolites in severe MDD were significantly involved in three metabolic pathways and some biofunctions. These results showed that there were divergent urinary metabolic phenotypes in moderate and severe MDD patients, and the identified potential urinary biomarkers might be useful for future developing objective diagnostic tests for MDD diagnosis. Our results could also be helpful for researchers to study the pathogenesis of MDD.

A Novel Urinary Metabolite Signature for Non-invasive Post-stroke Depression Diagnosis

Post-stroke depression (PSD) is the most common psychiatric complication in stroke survivors that has been associated with increased physical disability, distress, poor rehabilitation, and suicidal ideation. However, there are still no biomarkers available to support objective laboratory testing for this disorder. Here, a GC-MS-based urinary metabolomics approach was used to characterize the urinary metabolic profiling of PSD (stroke) subjects and non-PSD (health controls) subjects in order to identify and validate urinary metabolite biomarkers for PSD. Six metabolites, azelaic acid, glyceric acid, pseudouridine, 5-hydroxyhexanoic acid, tyrosine, and phenylalanine, were defined as biomarkers. A combined panel of these six urinary metabolites could effectively discriminate between PSD subjects and non-PSD subjects, achieving an area under the receiver-operating characteristic curve (AUC) of 0.961 in a training set (n = 72 PSD subjects and n = 146 non-PSD subjects). Moreover, this urinary biomarker panel was capable of discriminating blinded test samples (n = 58 PSD patients and n = 109 non-PSD subjects) with an AUC of 0.954. These findings suggest that a urine-based laboratory test using these biomarkers may be useful in the diagnosis of PSD.

Novel urinary metabolite signature for diagnosing postpartum depression

BACKGROUND

Postpartum depression (PPD) could affect ~10% of women and impair the quality of mother-infant interactions. Currently, there are no objective methods to diagnose PPD. Therefore, this study was conducted to identify potential biomarkers for diagnosing PPD.

MATERIALS AND METHODS

Morning urine samples of PPD subjects, postpartum women without depression (PPWD) and healthy controls (HCs) were collected. The gas chromatography-mass spectroscopy (GC-MS)-based urinary metabolomic approach was performed to characterize the urinary metabolic profiling. The orthogonal partial least-squares-discriminant analysis (OPLS-DA) was used to identify the differential metabolites. The logistic regression analysis and Bayesian information criterion rule were further used to identify the potential biomarker panel. The receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of the identified potential biomarker panel.

RESULTS

Totally, 73 PPD subjects, 73 PPWD and 74 HCs were included, and 68 metabolites were identified using GC-MS. The OPLS-DA model showed that there were 22 differential metabolites (14 upregulated and 8 downregulated) responsible for separating PPD subjects from HCs and PPWD. Meanwhile, a panel of five potential biomarkers - formate, succinate, 1-methylhistidine, α-glucose and dimethylamine - was identified. This panel could effectively distinguish PPD subjects from HCs and PPWD with an area under the curve (AUC) curve of 0.948 in the training set and 0.944 in the testing set.

CONCLUSION

These results demonstrated that the potential biomarker panel could aid in the future development of an objective diagnostic method for PPD.

Anti-ageing active ingredients from herbs and nutraceuticals used in traditional Chinese medicine: pharmacological mechanisms and implications for drug discovery

Ageing, an unanswered question in the medical field, is a multifactorial process that results in a progressive functional decline in cells, tissues and organisms. Although it is impossible to prevent ageing, slowing down the rate of ageing is entirely possible to achieve. Traditional Chinese medicine (TCM) is characterized by the nourishing of life and its role in anti-ageing is getting more and more attention. This article summarizes the work done on the natural products from TCM that are reported to have anti-ageing effects, in the past two decades. The effective anti-ageing ingredients identified can be generally divided into flavonoids, saponins, polysaccharides, alkaloids and others. Astragaloside, Cistanche tubulosa acteoside, icariin, tetrahydrocurcumin, quercetin, butein, berberine, catechin, curcumin, epigallocatechin gallate, gastrodin, 6-Gingerol, glaucarubinone, ginsenoside Rg1, luteolin, icarisid II, naringenin, resveratrol, theaflavin, carnosic acid, catalpol, chrysophanol, cycloastragenol, emodin, galangin, echinacoside, ferulic acid, huperzine, honokiol, isoliensinine, phycocyanin, proanthocyanidins, rosmarinic acid, oxymatrine, piceid, puerarin and salvianolic acid B are specified in this review. Simultaneously, chemical structures of the monomers with anti-ageing activities are listed, and their source, model, efficacy and mechanism are also described. The TCMs with anti-ageing function are classified according to their action pathways, including the telomere and telomerase, the sirtuins, the mammalian target of rapamycin, AMP-activated kinase and insulin/insulin-like growth factor-1 signalling pathway, free radicals scavenging and the resistance to DNA damage. Finally, Chinese compound prescription and extracts related to anti-ageing are introduced, which provides the basis and the direction for the further development of novel and potential drugs.

LINKED ARTICLES

This article is part of a themed section on Principles of Pharmacological Research of Nutraceuticals. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.11/issuetoc.

Lowered circulating aspartate is a metabolic feature of human breast cancer

Distinct metabolic transformation is essential for cancer cells to sustain a high rate of proliferation and resist cell death signals. Such a metabolic transformation results in unique cellular metabolic phenotypes that are often reflected by distinct metabolite signatures in tumor tissues as well as circulating blood. Using a metabolomics platform, we find that breast cancer is associated with significantly (p = 6.27E-13) lowered plasma aspartate levels in a training group comprising 35 breast cancer patients and 35 controls. The result was validated with 103 plasma samples and 183 serum samples of two groups of primary breast cancer patients. Such a lowered aspartate level is specific to breast cancer as it has shown 0% sensitivity in serum from gastric (n = 114) and colorectal (n = 101) cancer patients. There was a significantly higher level of aspartate in breast cancer tissues (n = 20) than in adjacent non-tumor tissues, and in MCF-7 breast cancer cell line than in MCF-10A cell lines, suggesting that the depleted level of aspartate in blood of breast cancer patients is due to increased tumor aspartate utilization. Together, these findings suggest that lowed circulating aspartate is a key metabolic feature of human breast cancer.

Discriminating poststroke depression from stroke by nuclear magnetic resonance spectroscopy-based metabonomic analysis

Poststroke depression (PSD), the most common psychiatric disease that stroke survivors face, is estimated to affect ~30% of poststroke patients. However, there are still no objective methods to diagnose PSD. In this study, to explore the differential metabolites in the urine of PSD subjects and to identify a potential biomarker panel for PSD diagnosis, the nuclear magnetic resonance-based metabonomic method was applied. Ten differential metabolites responsible for discriminating PSD subjects from healthy control (HC) and stroke subjects were found, and five of these metabolites were identified as potential biomarkers (lactate, α-hydroxybutyrate, phenylalanine, formate, and arabinitol). The panel consisting of these five metabolites provided excellent performance in discriminating PSD subjects from HC and stroke subjects, achieving an area under the receiver operating characteristic curve of 0.946 in the training set (43 HC, 45 stroke, and 62 PSD subjects). Moreover, this panel could classify the blinded samples from the test set (31 HC, 33 stroke, and 32 PSD subjects) with an area under the curve of 0.946. These results laid a foundation for the future development of urine-based objective methods for PSD diagnosis and investigation of PSD pathogenesis.

Pregnancy-Related Changes of Amino Acid and Acylcarnitine Concentrations: The Impact of Obesity

Objective Our primary objective was to assess the difference in amino and fatty acid biomarkers throughout pregnancy in women with and without obesity. Interactions between biomarkers and obesity status for associations with maternal and fetal metabolic measures were secondarily analyzed. Methods Overall 39 women (15 cases, 24 controls) were enrolled in this study during their 15- to 20-weeks' visit at the University of Iowa Hospitals and Clinics. We analyzed 32 amino acid and acylcarnitine concentrations with tandem mass spectrometry for differences throughout pregnancy as well as among women with and without obesity (body mass index [BMI] ≥ 35, BMI < 25). Results There were substantial changes in amino acids and acylcarnitine metabolites between the second and third trimesters (nonfasting state) of pregnancy that were significant after correcting for multiple testing (p < 0.002). Examining differences by maternal obesity, C8:1 (second trimester) and C2, C4-OH, C18:1 (third trimester) were higher in women with obesity compared with women without obesity. Several metabolites were marginally (0.002 < p < 0.05) correlated with birth weight, maternal glucose, and maternal weight gain stratified by obesity status and trimester. Conclusions Understanding maternal metabolism throughout pregnancy and the influence of obesity is a critical step in identifying potential mechanisms that may contribute to adverse outcomes in pregnancies complicated by obesity.

Associations between metabolomic-identified changes of biomarkers and arterial stiffness in subjects progressing to impaired fasting glucose

OBJECTIVE

We investigated correlations between age-related changes in circulating metabolites and arterial stiffness in impaired fasting glucose (IFG).

DESIGN, SUBJECTS, MEASUREMENT

This prospective cohort study included 602 healthy, normal fasting glucose (NFG) subjects (30-65 years old) who underwent triennial medical evaluation. After 3 years, 9·3% of subjects developed IFG (n = 56). Age, gender, BMI and fasting glucose were used to match the remaining NFG subjects (n = 546) that were included for the control group (NFG group, n = 80).

RESULTS

After 3 years, levels of fasting glucose, insulin and malondialdehyde, and brachial-ankle pulse wave velocity (baPWV) were significantly greater in the IFG group than in the NFG group after adjusting for baseline values. The IFG group had a greater increase in lactosylceramide (P = 0·001, q < 0·05) and a greater reduction in phosphatidylcholine (PC) (18:0/20:4) than the NFG group. Multiple linear regression analysis showed that the change in baPWV was independently and positively associated with changes in fasting glucose and lactosylceramide. In all subjects, lactosylceramide levels positively correlated with changes in baPWV and fasting glucose, while premenopausal women were not shown, and negatively correlated with changes in PC and LDL particle size.

CONCLUSIONS

This study indicates that age-related increase in circulating lactosylceramide is an independent predictor of increased arterial stiffness in subjects with impaired fasting glucose.

Divergent Urinary Metabolic Phenotypes between Major Depressive Disorder and Bipolar Disorder Identified by a Combined GC-MS and NMR Spectroscopic Metabonomic Approach

Bipolar disorder (BD) is a complex debilitating mental disorder that is often misdiagnosed as major depressive disorder (MDD). Therefore, a large percentage of BD subjects are incorrectly treated with antidepressants in clinical practice. To address this challenge, objective laboratory-based tests are needed to discriminate BD from MDD patients. Here, a combined gas chromatography-mass spectrometry (GC-MS)-based and nuclear magnetic resonance (NMR) spectroscopic-based metabonomic approach was performed to profile urine samples from 76 MDD and 43 BD subjects (training set) to identify the differential metabolites. Samples from 126 healthy controls were included as metabolic controls. A candidate biomarker panel was identified by further analyzing these differential metabolites. A testing set of, 50 MDD and 28 BD subjects was then used to independently validate the diagnostic efficacy of the identified panel using an area under the receiver operating characteristic curve (AUC). A total of 20 differential metabolites responsible for the discrimination between MDD and BD subjects were identified. A panel consisting of six candidate urinary metabolite biomarkers (propionate, formate, (R*,S*)2,3-dihydroxybutanoic acid, 2,4-dihydroxypyrimidine, phenylalanine, and β-alanine) was identified. This panel could distinguish BD from MDD subjects with an AUC of 0.913 and 0.896 in the training and testing sets, respectively. These results reveal divergent urinary metabolic phenotypes between MDD and BD. The identified urinary biomarkers can aid in the future development of an objective laboratory-based diagnostic test for distinguishing BD from MDD patients.

Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined (1)H NMR and GC-MS approach

Urine metabolomic profiling can identify biochemical alterations resulting from various injuries affecting the graft outcome after renal transplantation. Here, we aimed to describe in depth the metabolite content of urines of renal transplant patients and to link it with the major injury factors acting at critical stages following transplantation. Morning urine samples were prospectively collected from 38 kidney transplant patients at 7 days (D7), 3 months (M3) and 12 months (M12) after transplantation. Twenty-five patients were treated with tacrolimus (Tac) and thirteen patients with cyclosporine (CsA). (1)H-NMR (proton nuclear magnetic resonance) and gas chromatography-mass spectrometry (GC-MS) were used to examine the overall metabolomic signature of each sample. Multivariate analysis was performed to study the changes in the metabolic profile over time and their dependency on the type of calcineurin inhibitor (CNI) administered to patients. Biological pathways affected by transplantation were identified using a metabolomics pathway analysis (MetPA) web-tool. The metabolic profile of urine samples clearly varied with time. Markers of medullary injury, tubule cell oxidative metabolism and impaired tubular reabsorption or secretion were present at D7. Differences in metabolic profiles became less marked as time passed on, urine content being quite similar at M3 and M12. The metabolite profile tended to differ between patients receiving Tac and those receiving CsA but no clear discriminating profiles can be found. The combination of (1)H-NMR and GC-MS for the analysis of urine metabolomic profiles is a very useful method to study patho-physiological alterations in kidney transplant patients over time.

A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes

Obesity and diabetes arise from an intricate interplay between both genetic and environmental factors. It is well recognized that obesity plays an important role in the development of insulin resistance and diabetes. Yet, the exact mechanism of the connection between obesity and diabetes is still not completely understood. Metabolomics is an analytical approach that aims to detect and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics to the identification of disease biomarkers, with a number of well-known biomarkers identified. Metabolomics is a potent approach to unravel the intricate relationships between metabolism, obesity and progression to diabetes and, at the same time, has potential as a clinical tool for risk evaluation and monitoring of disease. Moreover, metabolomics applications have revealed alterations in the levels of metabolites related to obesity-associated diabetes. This review focuses on the part that metabolomics has played in elucidating the roles of metabolites in the regulation of systemic metabolism relevant to obesity and diabetes. It also explains the possible metabolic relation and association between the two diseases. The metabolites with altered profiles in individual disorders and those that are specifically and similarly altered in both disorders are classified, categorized and summarized.

Research of Herb-Partitioned Moxibustion for Primary Dysmenorrhea Patients Based on the LC-MS Metabonomics

Objective. To explore the efficacy and mechanism of primary dysmenorrhea patients were treated with herb-partitioned moxibustion through metabonomics. Methods. 20 patients with primary dysmenorrhea were randomized into two groups, separately treated with herb-partitioned moxibustion at CV8 (shenque) and acupuncture at SP6 (sanyinjiao). After three menstrual cycles' treatment, the intensity of menstrual pain using VAS and the changes of metabolites of plasma using LC-MS were observed. Results. The VAS of two groups decreased with different descending range. Herb-partitioned moxibustion upregulated 20α-dihydroprogesterone, pregnenolone, prostaglandin E₂ and γ-aminobutyric acid and downregulated the content of estrone and prostaglandin H₂, while acupuncture upregulated pregnenolone and 20α-dihydroprogesterone and downregulated 2-methoxyestradiol-3-methylether, 15-hydroxyeicosatrienoic acid and 6-keto-prostaglandin. Discussion. It was effective in relieving the abdominal pain by these two therapies. Herb-partitioned moxibustion is superior to acupuncture for primary dysmenorrhea, which could be related to regulating the endocrine hormone.

Association between arterial stiffness and serum L-octanoylcarnitine and lactosylceramide in overweight middle-aged subjects: 3-year follow-up study

Existing data on the association between being overweight and cardiovascular morbidity and mortality risk in adults are inconsistent. We prospectively and longitudinally investigated the effects of weight on arterial stiffness and plasma metabolites in middle-aged subjects (aged 40–55 years). A group of 59 individuals who remained within the range of overweight during repeated measurements over a 3-year period was compared with a control group of 59 normal weight subjects who were matched for age and gender. Changes in metabolites by UPLC-LTQ-Orbitrap mass spectrometry and changes in brachial-ankle pulse wave velocity (ba-PWV) were examined. At baseline, the overweight group showed higher BMI, waist circumference, triglyceride, free fatty acid (FFA), glucose, insulin, and hs-CRP, and lower HDL-cholesterol than controls. After 3 years, the changes in waist circumference, diastolic and systolic blood pressure (DBP and SBP), triglyceride, FFA, glucose, insulin, hs-CRP, and ba-PWV observed in the overweight group were significantly different from those in the control group after adjusting for baseline levels. Furthermore, the overweight group showed greater increases in L-octanoylcarnitine (q=0.006) and decanoylcarnitine (q=0.007), and higher peak intensities of L-leucine, L-octanoylcarnitine, and decanoylcarnitine. Multiple linear regression analysis showed that the change in ba-PWV was independently and positively associated with changes in L-octanoylcarnitine, lactosylceramide, and SBP, and with baseline BMI. Our results indicate that the duration of overweight is an important aggravating factor for arterial stiffness, especially during middle age. Additionally, an age-related increase in plasma L-octanoylcarnitine, lactosylceramide, SBP, and baseline BMI are independent predictors of increased arterial stiffness in middle-aged individuals.

A gas chromatography-mass spectrometry based study on serum metabolomics in rats chronically poisoned with hydrogen sulfide

Hydrogen sulfide poisoning is a common occupational hazard, whose mortality and incidence rates are first and second, respectively, among occupational poisoning incidents in China. The main target organs of its toxicity are in the central nervous system and respiratory system. However, there are currently no specific direct tests that can be used to diagnose poisoned patients. In this study, we developed a serum metabonomic method using orthogonal partial least squares-discriminate analysis (OPLS-DA), based on gas chromatography-mass spectrometry (GC/MS) to evaluate the effect of chronic poisoning by hydrogen sulfide in rats. The OPLS-DA data demonstrated that the model group (n = 60) differed significantly from the control group (n = 30), suggesting that the metabolic profiles of the two groups are markedly different. Alterations in the levels of some metabolites such as citrate, galactose, lactate, mannose, inositol, urea, phosphate, alanine and valine were detected by OPLS-DA analysis. We observed changes in metabolic pathways including lipid metabolism, energy metabolism and amino metabolism in the model group. Our results indicate that GC/MS-based metabonomic methods may provide novel detection means for chronic hydrogen sulfide poisoning.

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

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

An NMR-based metabolomic approach to investigate the effects of supplementation with glutamic acid in piglets challenged with deoxynivalenol

Deoxynivalenol (DON) has various toxicological effects in humans and pigs that result from the ingestion of contaminated cereal products. This study was conducted to investigate the protective effects of dietary supplementation with glutamic acid on piglets challenged with DON. A total of 20 piglets weaned at 28 d of age were randomly assigned to receive 1 of 4 treatments (5 piglets/treatment): 1) basal diet, negative control (NC); 2) basal diet +4 mg/kg DON (DON); 3) basal diet +2% (g/g) glutamic acid (GLU); 4) basal diet +4 mg/kg DON +2% glutamic acid (DG). A 7-d adaptation period was followed by 30 days of treatment. A metabolite analysis using nuclear magnetic resonance spectroscopy (1H-NMR)-based metabolomic technology and the determination of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities for plasma, as well as the activity of Caspase-3 and the proliferation of epithelial cells were conducted. The results showed that contents of low-density lipoprotein, alanine, arginine, acetate, glycoprotein, trimethylamine-N-oxide (TMAO), glycine, lactate, and urea, as well as the glutamate/creatinine ratio were higher but high-density lipoprotein, proline, citrate, choline, unsaturated lipids and fumarate were lower in piglets of DON treatment than that of NC treatment (P<0.05). Compared with DON treatment, dietary supplementation with glutamic acid increased the plasma concentrations of proline, citrate, creatinine, unsaturated lipids, and fumarate, and decreased the concentrations of alanine, glycoprotein, TMAO, glycine, and lactate, as well as the glutamate/creatinine ratio (P<0.05). Addition glutamic acid to DON treatment increased the plasma activities of SOD and GSH-Px and the proliferating cell nuclear antigen (PCNA) labeling indexes for the jejunum and ileum (P<0.05). These novel findings indicate that glutamic acid has the potential to repair the injuries associated with oxidative stress as well as the disturbances of energy and amino acid metabolism induced by DON.

Plasma metabolite biomarkers for the detection of pancreatic cancer

Patients with pancreatic cancer (PC) are usually diagnosed at late stages, when the disease is nearly incurable. Sensitive and specific markers are critical for supporting diagnostic and therapeutic strategies. The aim of this study was to use a metabonomics approach to identify potential plasma biomarkers that can be further developed for early detection of PC. In this study, plasma metabolites of newly diagnosed PC patients (n = 100) and age- and gender-matched controls (n = 100) from Connecticut (CT), USA, and the same number of cases and controls from Shanghai (SH), China, were profiled using combined gas and liquid chromatography mass spectrometry. The metabolites consistently expressed in both CT and SH samples were used to identify potential markers, and the diagnostic performance of the candidate markers was tested in two sample sets. A diagnostic model was constructed using a panel of five metabolites including glutamate, choline, 1,5-anhydro-d-glucitol, betaine, and methylguanidine, which robustly distinguished PC patients in CT from controls with high sensitivity (97.7%) and specificity (83.1%) (area under the receiver operating characteristic curve [AUC] = 0.943, 95% confidence interval [CI] = 0.908–0.977). This panel of metabolites was then tested with the SH data set, yielding satisfactory accuracy (AUC = 0.835; 95% CI = 0.777–0.893), with a sensitivity of 77.4% and specificity of 75.8%. This model achieved a sensitivity of 84.8% in the PC patients at stages 0, 1, and 2 in CT and 77.4% in the PC patients at stages 1 and 2 in SH. Plasma metabolic signatures show promise as biomarkers for early detection of PC.

Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a South Indian Asian population using an NMR-based metabolomic approach

It is well known that obesity/high body mass index (BMI) plays a key role in the evolution of insulin resistance and type-2 diabetes mellitus (T2DM). However, the exact mechanism underlying its contribution is still not fully understood. This work focuses on an NMR-based metabolomic investigation of the serum profiles of diabetic, obese South Indian Asian subjects. (1)H 1D and 2D NMR experiments were performed to profile the altered metabolic patterns of obese diabetic subjects and multivariate statistical methods were used to identify metabolites that contributed significantly to the differences in the samples of four different subject groups: diabetic and non-diabetic with low and high BMIs. Our analysis revealed that the T2DM-high BMI group has higher concentrations of saturated fatty acids, certain amino acids (leucine, isoleucine, lysine, proline, threonine, valine, glutamine, phenylalanine, histidine), lactic acid, 3-hydroxybutyric acid, choline, 3,7-dimethyluric acid, pantothenic acid, myoinositol, sorbitol, glycerol, and glucose, as compared to the non-diabetic-low BMI (control) group. Of these 19 identified significant metabolites, the levels of saturated fatty acids, lactate, valine, isoleucine, and phenylalanine are also higher in obese non-diabetic subjects as compared to control subjects, implying that this set of metabolites could be identified as potential biomarkers for the onset of diabetes in subjects with a high BMI. Our work validates the utility of NMR-based metabolomics in conjunction with multivariate statistical analysis to provide insights into the underlying metabolic pathways that are perturbed in diabetic subjects with a high BMI.

Carriage of the V279F homozygous genotype, a rare allele, within the gene encoding Lp-PLA2 leads to changes in circulating intermediate metabolites in individuals without metabolic syndrome

AIM

Identifying differences in plasma metabolic profiling between Lp-PLA2 279VV and 279FF in individuals without metabolic syndrome (MetS) can be used to elucidate the roles of novel Lp-PLA2 activities in normal physiological processes.

METHODS

Non-MetS individuals with 279FF (n=36) and age-, sex- and BMI-matched VV subjects (n=36) were included in this analysis.

RESULTS

The FF subjects exhibited no appreciable enzyme activity. No significant differences were observed between the VV and FF subjects in the serum lipid profiles or hs-CRP, plasma ox-LDL, MDA or urinary 8-epi-PGF2α levels. The FF subjects also showed lower activities of lyso-phosphatidylcholine (lysoPC) (16:0) (p=0.003) and oleamide (p＜0.001) and a higher activity of L-tryptophan (p=0.016) than the VV subjects. In addition, the Lp-PLA2 activity positively correlated with the lysoPC (16:0) and lysoPC (18:0) activities and negatively correlated with the PC (16:0/22:6) and L-tryptophan activities in the VV subjects. Furthermore, in the VV subjects, the lysoPC (16:0) and lysoPC (18:0) activities negatively correlated with the presence of PCs containing 14:0/20:2, 14:0/22:4 and 16:0/22:6, while the oleamide activity exhibited a strong positive correlation with lysoPCs and a negative correlation with PCs, whereas the relationship between oleamide and lysoPCs and PCs was weaker in the FF subjects.

CONCLUSIONS

The present results indicate that the natural absence of the plasma Lp-PLA2 activity due to carriage of the Lp-PLA2 279FF genotype may reduce the generation of lysoPC (16:0) and oleamide and thereby enhance the activity of plasma tryptophan in normal physiological processes.

Combined application of NMR- and GC-MS-based metabonomics yields a superior urinary biomarker panel for bipolar disorder

Bipolar disorder (BD) is a debilitating mental disorder that cannot be diagnosed by objective laboratory-based modalities. Our previous studies have independently used nuclear magnetic resonance (NMR)-based and gas chromatography-mass spectrometry (GC-MS)-based metabonomic methods to characterize the urinary metabolic profiles of BD subjects and healthy controls (HC). However, the combined application of NMR spectroscopy and GC-MS may identify a more comprehensive metabolite panel than any single metabonomic platform alone. Therefore, here we applied a dual platform (NMR spectroscopy and GC-MS) that generated a panel of five metabolite biomarkers for BD-four GC-MS-derived metabolites and one NMR-derived metabolite. This composite biomarker panel could effectively discriminate BD subjects from HC, achieving an area under receiver operating characteristic curve (AUC) values of 0.974 in a training set and 0.964 in a test set. Moreover, the diagnostic performance of this panel was significantly superior to the previous single platform-derived metabolite panels. Thus, the urinary biomarker panel identified here shows promise as an effective diagnostic tool for BD. These findings also demonstrate the complementary nature of NMR spectroscopy and GC-MS for metabonomic analysis, suggesting that the combination of NMR spectroscopy and GC-MS can identify a more comprehensive metabolite panel than applying each platform in isolation.

Distinct signatures of host-microbial meta-metabolome and gut microbiome in two C57BL/6 strains under high-fat diet

A combinatory approach using metabolomics and gut microbiome analysis techniques was performed to unravel the nature and specificity of metabolic profiles related to gut ecology in obesity. This study focused on gut and liver metabolomics of two different mouse strains, the C57BL/6J (C57J) and the C57BL/6N (C57N) fed with high-fat diet (HFD) for 3 weeks, causing diet-induced obesity in C57N, but not in C57J mice. Furthermore, a 16S-ribosomal RNA comparative sequence analysis using 454 pyrosequencing detected significant differences between the microbiome of the two strains on phylum level for Firmicutes, Deferribacteres and Proteobacteria that propose an essential role of the microbiome in obesity susceptibility. Gut microbial and liver metabolomics were followed by a combinatory approach using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and ultra performance liquid chromatography time of tlight MS/MS with subsequent multivariate statistical analysis, revealing distinctive host and microbial metabolome patterns between the C57J and the C57N strain. Many taurine-conjugated bile acids (TBAs) were significantly elevated in the cecum and decreased in liver samples from the C57J phenotype likely displaying different energy utilization behavior by the bacterial community and the host. Furthermore, several metabolite groups could specifically be associated with the C57N phenotype involving fatty acids, eicosanoids and urobilinoids. The mass differences based metabolite network approach enabled to extend the range of known metabolites to important bile acids (BAs) and novel taurine conjugates specific for both strains. In summary, our study showed clear alterations of the metabolome in the gastrointestinal tract and liver within a HFD-induced obesity mouse model in relation to the host-microbial nutritional adaptation.

Prehypertension-associated elevation in circulating lysophosphatidlycholines, Lp-PLA2 activity, and oxidative stress

Prehypertension is a risk factor for atherosclerosis. We investigated alterations in plasma metabolites that are associated with prehypertension. A group of 53 individuals was identified who remained within the range of prehypertension during repeated measurements in a 3-year period. This group was compared with the control group of 53 normotensive subjects who were matched for age and gender. Metabolomic profiles were analyzed with UPLC-LTQ-Orbitrap mass spectrometry. The prehypertensive group showed higher levels of lysophosphatidylcholines (lysoPCs) containing C14:0, C16:1, C16:0, C18:2, C18:1, C18:0, C20:5, C20:4, C20:3, and C22:6, higher circulating Lp-PLA₂ activity, oxidized LDL (ox-LDL), interleukin 6 (IL-6), urinary 8-epi-PGF_2α, and higher brachial-ankle pulse wave velocity (ba-PWV), before and after adjusting for BMI, WHR, smoking, alcohol consumption, serum lipid profiles, glucose, and insulin. LysoPC (16:0) was the most important plasma metabolite for evaluating the difference between control and prehypertensive groups, with a variable important in the projection (VIP) value of 17.173, and it showed a positive and independent association with DBP and SBP. In the prehypertensive group, the levels of lysoPC (16:0) positively and significantly correlated with ox-LDL, Lp-PLA₂ activity, 8-epi-PGF_2α, ba-PWV, and IL-6 before and after adjusting for confounding variables. Prehypertension-associated elevations in lysoPCs, Lp-PLA₂ activity, ox-LDL, urinary 8-epi-PGF_2α, IL-6, and ba-PWV could indicate increased oxidative stress from Lp-PLA₂-catalyzed PC hydrolysis during increased LDL oxidation, thereby enhancing proinflammation and arterial stiffness.

2,4-Dihydroxypyrimidine is a potential urinary metabolite biomarker for diagnosing bipolar disorder

Bipolar disorder (BD) is a common and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. Here, we used a gas chromatography-mass spectrometry (GC-MS) based metabonomic method to characterize the urinary metabolic profiling of BD subjects and healthy controls to identify and validate urinary metabolite biomarkers for BD. Multivariate statistical analysis was used to visualize group discrimination and identify differentially expressed urinary metabolites in BD subjects relative to the healthy controls. Multivariate statistical analysis showed that the BD group was significantly distinguishable from the healthy control. Totally, 37 urinary metabolites responsible for discriminating BD subjects from healthy controls were identified. Interestingly, of 37 differential metabolites, 2,4-dihydroxypyrimidine was identified as an effective diagnostic biomarker for BD, yielding an area under the receiver operating characteristic curve (AUC) of 0.889 in the training samples (45 BD subjects and 61 healthy controls) and 0.805 in the test samples (26 BD subjects and 33 healthy controls). Our findings suggest that 2,4-dihydroxypyrimidine is a promising candidate urinary biomarker for BD, which may facilitate development of a urine-based diagnostic test for BD.

A novel urinary metabolite signature for diagnosing major depressive disorder

Major depressive disorder (MDD) is a prevalent and debilitating mental disorder. Yet, there are no objective biomarkers available to support diagnostic laboratory testing for this disease. Here, gas chromatography-mass spectrometry was applied to urine metabolic profiling of 126 MDD and 134 control subjects. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to identify the differential metabolites in MDD subjects relative to healthy controls. The OPLS-DA analysis of data from training samples (82 first-episode, drug-naïve MDD subjects and 82 well-matched healthy controls) showed that the depressed group was significantly distinguishable from the control group. Totally, 23 differential urinary metabolites responsible for the discrimination between the two groups were identified. Postanalysis, 6 of the 23 metabolites (sorbitol, uric acid, azelaic acid, quinolinic acid, hippuric acid, and tyrosine) were defined as candidate diagnostic biomarkers for MDD. Receiver operating characteristic analysis of combined levels of these six biomarkers yielded an area under the receiver operating characteristic curve (AUC) of 0.905 in distinguishing training samples; this simplified metabolite signature classified blinded test samples (44 MDD subjects and 52 healthy controls) with an AUC of 0.837. Furthermore, a composite panel by the addition of previously identified urine biomarker (N-methylnicotinamide) to this biomarker panel achieved a more satisfactory accuracy, yielding an AUC of 0.909 in the training samples and 0.917 in the test samples. Taken together, these results suggest this composite urinary metabolite signature should facilitate development of a urine-based diagnostic test for MDD.

Metabolomics approaches for characterizing metabolic interactions between host and its commensal microbes

It is increasingly evident that the gut microbiota is involved in the regulation of multiple mammalian metabolic pathways through a series of interactive host-microbiota metabolic, signaling, and immune-inflammatory axes that physiologically connect the gut, liver, brain, and other organs. Correlation of the metabotypes with the gut microbial profiles derived from culture-independent molecular techniques is increasingly useful for deciphering inherent and intimate host-microbe relationships. Real-time analysis of the small molecule metabolites derived from gut microbial-host co-metabolism is essential for understanding the metabolic functions of the gut microbiome and has tremendous implications for personalized healthcare strategies. Metabolomics, an array of analytical techniques that includes high resolution NMR spectroscopy and chromatography-MS in conjunction with chemometrics and bioinformatics tools, enables characterization of the metabolic footprints of mammalian hosts that correlate with the microbial community in the intestinal tract. The metabolomics approach provides important information of a complete spectrum of metabolites produced from the gut microbial-mammalian co-metabolism and is improving our understanding of the molecular mechanisms underlying multilevel host-microbe interactions. In this review, the interactions of gut microbiota with their host are discussed and some examples of NMR- or MS-based metabolomics applications for characterizing the metabolic footprints of gut microbial-host co-metabolism are described. Advances in the metabolomic analysis of bile acids, short-chain fatty acids, and choline metabolism are also summarized.

Association of age-related changes in circulating intermediary lipid metabolites, inflammatory and oxidative stress markers, and arterial stiffness in middle-aged men

The relationships between age-related changes in circulating endogenous metabolites, inflammatory and oxidative stress markers, and arterial stiffness in 57 middle-aged (34-55 years), nonobese men were studied over the course of 3 years. Arterial stiffness was measured using brachial-ankle pulse wave velocities (ba-PWV). Plasma metabolomic profiling was performed using ultra-performance liquid chromatography and quadrupole time-of-flight mass spectrometry. After 3 years, decreased HDL cholesterol and increased malondialdehyde (MDA) and ox-LDL levels were observed. Among 15 identified lipids, lysoPCs (C16:0, C18:0, C18:2, C20:4, and C20:5) and linoleyl carnitine were the major plasma metabolites that contributed to the age-related differences. LysoPC16:0 (variable importance in the projection value, 6.2029) was found as the most important plasma metabolite for evaluating these changes. Changes in lysoPC16:0 levels positively correlated with the changes in 8-epi-PGF2α (r = 0.608), MDA (r = 0.413), high-sensitivity C-reactive protein (r = 0.509), IL-6 (r = 0.497), and ba-PWV (r = 0.283) levels. ba-PWV levels positively correlated with the changes in waist-to-hip ratios (WHR), inflammatory and oxidative stress markers. In a subgroup analysis of subjects with decreased ba-PWVs vs. increased ba-PWVs, changes in WHR and levels of lysoPC16:0, ba-PWV, IL-6, 8-epi-PGF2α, MDA, and P-selectin were significantly different. Our results suggest that age-related increases in lysoPC16:0 may contribute to lipid peroxidation, thereby activating proinflammatory phenotypes and arterial stiffness.

Proteomic and metabolomic approaches to the study of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is considered a complex multifactorial disorder resulting from the interaction of genetic, environmental, and lifestyle influences. Nontargeted proteomics and metabolomics have been used in the past years with the aim of identifying molecules potentially involved in the pathophysiology of this frequent disorder. The biomolecules identified so far participate in many metabolic pathways, including energy metabolism (glucose and lipid metabolism), protein metabolic processes and protein folding, cytoskeleton structure, immune response, inflammation and iron metabolism, fibrinolysis and thrombosis, oxidative stress and intracellular calcium metabolism. These molecules provide key information about molecular functions altered in PCOS and raise questions concerning their precise role in the pathogenesis of this syndrome. The biomolecules identified by nontargeted proteomic and metabolomic approaches should be considered as candidates in future studies aiming to define specific molecular phenotypes of PCOS.

Metabonomics study of essential hypertension and its chinese medicine subtypes by using gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy

A metabonomic study was performed to investigate the metabolic mechanism of essential hypertension and its Chinese medicine subtypes, including “Yin-deficiency and Yang-hyperactivity syndrome” (YDYHS) and “Yin-Yang deficiency syndrome” (YYDS). Plasma samples from 22 healthy volunteers, 31 hypertensive patients with YDYHS, and 29 hypertensive patients with YYDS were analyzed by ¹H-NMR spectroscopy and gas chromatography coupled with mass spectrometry (GC-MS). The three groups were distinctly classified by principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA). According to identified biomarkers and their related pathways, abnormal glucose metabolism might be the main common pathway from YDYHS to YYDS, and sympathetic nervous system activation would play an important role in the pathogenesis of YDYHS, while a low metabolic rate usually occurred in YYDS.

Metabolomics in diabetes research

Diabetes represents one of the most important global health problems because it is associated with a large economic burden on the health systems of many countries. Whereas the diagnosis and treatment of manifest diabetes have been well investigated, the identification of novel pathways or early biomarkers indicative of metabolic alterations or insulin resistance related to the development of diabetes is still in progress. Over half of the type 2 diabetes patients show manifestations of diabetes-related diseases, which highlight the need for early screening markers of diabetes. During the last decade, the rapidly growing research field of metabolomics has introduced new insights into the pathology of diabetes as well as methods to predict disease onset and has revealed new biomarkers. Recent epidemiological studies first used metabolism to predict incident diabetes and revealed branched-chain and aromatic amino acids including isoleucine, leucine, valine, tyrosine and phenylalanine as highly significant predictors of future diabetes. This review summarises the current findings of metabolic research regarding diabetes in animal models and human investigations.

Application of metabolomics in traditional chinese medicine differentiation of deficiency and excess syndromes in patients with diabetes mellitus

Metabolic profiling is widely used as a probe in diagnosing diseases. In this study, the metabolic profiling of urinary carbohydrates was investigated using gas chromatography/mass spectrometry (GC/MS) and multivariate statistical analysis. The kernel-based orthogonal projections to latent structures (K-OPLS) model were established and validated to distinguish between subjects with and without diabetes mellitus (DM). The model was combined with subwindow permutation analysis (SPA) in order to extract novel biomarker information. Furthermore, the K-OPLS model visually represented the alterations in urinary carbohydrate profiles of excess and deficiency syndromes in patients with diabetes. The combination of GC/MS and K-OPLS/SPA analysis allowed the urinary carbohydrate metabolic characterization of DM patients with different traditional Chinese medicine (TCM) syndromes, including biomarkers different from non-DM patients. The method presented in this study might be a complement or an alternative to TCM syndrome research.

Investigating potential mechanisms of obesity by metabolomics

Obesity is a serious health problem with an increased risk of several common diseases including diabetes, cardiovascular disease, and cancer. Metabolomics is an emerging analytical technique for systemic determination of metabolite profiles, which is useful for understanding the biochemical changes in obesity or related diseases both in individual organs and at the organism level. Increasingly, this technology has been applied to the study of obesity, complementing transcriptomics and/or proteomics analyses. Indeed, the alterations of metabolites in biofluids/tissues are direct indicators of variations in physiology or pathology. In this paper, we will examine the obesity-related alterations in significant metabolites that have been identified by metabolomics as well as their metabolic pathway associations. Issues concerning the screening of biologically significant metabolites related to obesity will also be discussed.