Serum Metabolomic Profiles Research Articles

A rational usage of combined using of herbal medicines and synthetic medicines: A time interval window research

The combined use of herbal and traditional medicine with synthetic drugs is a common clinical issue in China, as well as in other countries around the world. The improper combination of Traditional Chinese Medicine (TCM) with synthetic drugs may increase the risk of severe adverse drug reactions (ADRs). In China, the combination of Shuanghuanglian (SHL) and Ribavirin injections (RB) is one such example that has led to a higher risk of pseudo-allergic ADRs, observed during post-marketing safety monitoring. Such combination-related ADRs may be associated with concurrent drug exposure concentrations in vivo . Thus, it is important to administer both formulations at acceptable intervals to prevent ADRs. In this study, we propose an appropriate time interval of administration to reduce ADR risk in combination drug use. SHL and RB combination was then tested in rats. This combination was tested at different administration intervals. Ear weight was chosen as the quantitative index to indicate tissue edema and intensity of pseudo-allergic reactions. Compared to the control group, rats that were simultaneously administered with SHL and RB intravenously, showed different behaviors and symptoms such as ear edema and increase in weight, swelling of limbs and huddling, which were similar to those seen in pseudo-allergic reactions. By contrast, rats administered solely with either RB or SHL did not exhibit obvious pseudo-allergic reactions. With respect to the combination groups at different administration intervals, there were two different types of responses. One of them was similar to the concurrent administration of drug pairs, which occurred in groups at intervals of 0, 1.5, 2, 3.5 and 4 h. The other type was similar to the sole use of RB or SHL, which occurred in groups with intervals of 6, 8, 12 and 24 h. We also determined serum concentrations of complement C5 (C5) and thrombomodulin (THBD), proteins that mediate the complement system and coagulation cascade-depended pseudo-allergic reactions. Results showed that there was significant increase in these two indices in combination groups, with intervals below or equal to 4 h and non-significant increase in combination groups with intervals above or equal to 6 h. These results indicate that a “safe window” of administration interval exists when using the drug pair: SHL and RB, thus reducing the risk of pseudo-allergic reactions. Moreover, we tested serum metabolomic profiles of each group. The principal component analysis revealed that there was an obvious distinction between the rat groups, of both clusters: One cluster included the normal control group where either SHL or RB group was administered and the combination groups that were administered both SHL and RB at intervals of over or equal to 6 h. The other cluster involved combination groups that were administered both drugs at intervals below or equal to 4 h. Thus, the metabolomic results also elucidated a “safe window” administration interval, when using such drug pairs. We then screened out metabolites that might be used to distinguish these two clusters of rat groups. The results of ROC curves showed that compounds of m / z 154.0592, m / z 433.2357, and m / z 331.0528 could distinguish rats where SHL was used after and before 6 h. These metabolites were identified as L-2-Amino-3-(1-pyrazolyl) propanoic acid, [2-hydroxy-1-(6-hydroxy-1-oxo-1H-isochromen-3-yl) butoxy] sulfonic acid and LysoPA (0:0/18:2 (9Z, 12Z)) according to their MS/MS spectrum, respectively. This study verified the hypothesis of the “safe window” of administration interval in combination drug use, including TCM and synthetic drugs. Administered at appropriate intervals, it can reduce ADR risk and prevent safety-related issues from occurring. This study thus provides a new perspective for improving safety, when using Chinese herbal medicines in combination with synthetic medicine.

Serum metabolomic profile as a non-invasive adjunct tool for the diagnosis of endometriosis-related infertility

Serum metabolomic profile as a non-invasive adjunct tool for the diagnosis of endometriosis-related infertility

Ozone-induced changes in the serum metabolome: Role of the microbiome.

Ozone is an asthma trigger. In mice, the gut microbiome contributes to ozone-induced airway hyperresponsiveness, a defining feature of asthma, but the mechanistic basis for the role of the gut microbiome has not been established. Gut bacteria can affect the function of distal organs by generating metabolites that enter the blood and circulate systemically. We hypothesized that global metabolomic profiling of serum collected from ozone exposed mice could be used to identify metabolites contributing to the role of the microbiome in ozone-induced airway hyperresponsiveness. Mice were treated for two weeks with a cocktail of antibiotics (ampicillin, neomycin, metronidazole, and vancomycin) in the drinking water or with control water and then exposed to air or ozone (2 ppm for 3 hours). Twenty four hours later, blood was harvested and serum analyzed via liquid-chromatography or gas-chromatography coupled to mass spectrometry. Antibiotic treatment significantly affected 228 of the 562 biochemicals identified, including reductions in the known bacterially-derived metabolites, equol, indole propionate, 3-indoxyl sulfate, and 3-(4-hydroxyphenyl)propionate, confirming the efficacy of the antibiotic treatment. Ozone exposure caused significant changes in 334 metabolites. Importantly, ozone-induced changes in many of these metabolites were different in control and antibiotic-treated mice. For example, most medium and long chain fatty acids declined by 20–50% with ozone exposure in antibiotic-treated but not control mice. Most taurine-conjugated bile acids increased with ozone exposure in antibiotic-treated but not control mice. Ozone also caused marked (9-fold and 5-fold) increases in the polyamines, spermine and spermidine, respectively, in control but not antibiotic-treated mice. Each of these metabolites has the capacity to alter airway responsiveness and may account for the role of the microbiome in pulmonary responses to ozone.

Unraveling the Serum Metabolomic Profile of Post-partum Depression.

Post-partum depression (PPD) is a severe psychiatric disorder affecting ∼15% of young mothers. Early life stressful conditions in periconceptual, fetal and early infant periods or exposure to maternal psychiatric disorders, have been linked to adverse childhood outcomes interfering with physiological, cognitive and emotional development. The molecular mechanisms of PPD are not yet fully understood. Unraveling the molecular underpinnings of PPD will allow timely detection and establishment of effective therapeutic approaches. To investigate the underlying molecular correlates of PPD in peripheral material, we compared the serum metabolomes of an in detail characterized group of mothers suffering from PPD and a control group of mothers, all from Heraklion, Crete in Greece. Serum samples were analyzed by a mass spectrometry platform for targeted metabolomics, based on selected reaction monitoring (SRM), which measures the levels of up to 300 metabolites. In the PPD group, we observed increased levels of glutathione-disulfide, adenylosuccinate, and ATP, which associate with oxidative stress, nucleotide biosynthesis and energy production pathways. We also followed up the metabolomic findings in a validation cohort of PPD mothers and controls. To the very best of our knowledge, this is the first metabolomic serum analysis in PPD. Our data show that molecular changes related to PPD are detectable in peripheral material, thus paving the way for additional studies in order to shed light on the molecular correlates of PPD.

ASIC3-dependent metabolomics profiling of serum and urine in a mouse model of fibromyalgia

Fibromyalgia (FM) is characterized by chronic widespread pain. The pathogenesis of FM remains unclear. No specific biomarkers are available. Animal models of FM may provide an opportunity to explore potential biomarkers in a relative homogenous disease condition. Here, we probed the metabolomics profiles of serum and urine in a mouse model of FM induced by intermittent cold stress (ICS). We focused on the role of acid-sensing ion channel 3 (ASIC3) in the metabolomics profiling because ICS treatment induced chronic widespread muscle pain lasting for 1 month in wild-type (Asic3+/+) but not Asic3-knockout (Asic3−/−) mice. Serum and urine samples were collected from both genotypes at different ICS stages, including before ICS (basal level) and post-ICS at days 10 (middle phase, P10) and 40 (recovery phase, P40). Control naïve mice and ICS-induced FM mice differed in 1H-NMR- and LC-MS-based metabolomics profiling. On pathway analysis, the leading regulated pathways in Asic3+/+ mice were taurine and hypotaurine, cysteine and methionine, glycerophospholipid, and ascorbate and aldarate metabolisms, and the major pathways in Asic3−/− mice involved amino acid-related metabolism. Finally, we developed an algorithm for the impactful metabolites in the FM model including cis-aconitate, kynurenate, taurine, pyroglutamic acid, pyrrolidonecarboxylic acid, and 4-methoxyphenylacetic acid in urine as well as carnitine, deoxycholic acid, lysoPC(16:0), lysoPC(20:3), oleoyl-L-carnitine, and trimethylamine N-oxide in serum. Asic3−/− mice were impaired in only muscle allodynia development but not other pain symptoms in the ICS model, so the ASIC3-dependent metabolomics changes could be useful for developing diagnostic biomarkers specific to chronic widespread muscle pain, the core symptom of FM. Further pharmacological validations are needed to validate these metabolomics changes as potential biomarkers for FM diagnosis and/or treatment responses.

Beyond the antibodies: serum metabolomic profiling of myasthenia gravis.

Myasthenia gravis (MG) is a chronic, potentially debilitating autoimmune disease characterized by weakness and rapid fatigue of the voluntary muscles that worsens on exertion. Left untreated, MG symptoms may cause significant morbidity or even death. To date, no robust biological marker is available to follow the course of the disease. Therefore, new diagnostic approaches and biological markers are essential not only for improved diagnosis of the disease but for improved outcomes. The present study applied a two-control, multi-label metabolomics profiling approach as a potential strategy for the identification of biomarkers unique to myasthenia gravis (MG). Metabolic analyses using acid- and dansyl-labelled serum from seropositive MG (n = 46), rheumatoid arthritis (RA) (n = 23) and healthy controls (HC) (n = 49) were performed on samples from adult patients presenting to the University of Alberta Hospital neuromuscular and rheumatology clinics. Comparisons between patients with MG vs. HC, and RA vs. HC were made using univariate and multivariate statistics. Serum biomarker patterns were statistically significantly different between groups. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models exhibited considerable distinction between all groups. Metabolites were then filtered to remove peak pairs common to both disease cohorts. Combined metabolite panels revealed clear separation between MG and HC for both library-matched (AUROC: 0.92 ± 0.03) and highest AUC patients (AUROC: 0.94 ± 0.05). In patients presenting to the clinic with seropositive MG, metabolomic profiling is capable of distinguishing patients with disease from those without. These results provide an important first step towards a potential biomarker for improving MG identification.

Serum metabolome and targeted bile acid profiling reveals potential novel biomarkers for drug-induced liver injury.

This study aims to determine the non-invasive, reliable and sensitive biochemical parameters for the diagnosis of drug-induced liver injury (DILI).Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) and selected reaction monitoring (SRM) were used to profile the serum metabolome and quantify 15 targeted bile acid metabolites, respectively, in samples obtained from 38 DILI patients and 30 healthy controls.A comparison of the resulting serum metabolome profiles of the study participants revealed significant differences between DILI patients and healthy controls. Specifically, serum palmitic acid, taurochenodeoxycholic acid, glycocholic acid (GCA), and tauroursodeoxycholic acid (TUDCA) levels were significantly higher, and serum lysophosphatidylethanolamine levels were significantly lower in DILI patients vs healthy controls (P < .001). Furthermore, the SRM assay of bile acids revealed that the increase in GCA, taurocholic acid (TCA), TUDCA, glycochenodeoxycholic acid (GCDCA), glycochenodeoxycholic sulfate (GCDCS), and taurodeoxycholic acid (TDCA) corresponded to a higher degree of liver damage. These results also indicate that serum concentrations of chenodeoxycholic acid (CDCA), deoxycholic acid (DCA) and lithocholic acid (LCA) were significantly lower in patients with severe DILI, when compared to healthy controls, and that this decrease was closely correlated to the severity of liver damage.Taken together, these results demonstrate that bile acids could serve as potential biomarkers for the early diagnosis and severity of DILI.

Serum metabolomics profiling and potential biomarkers of myopia using LC-QTOF/MS

Myopia is the most common form of refractive eye disease, and the prevalence is increasing rapidly worldwide. However, the key metabolic alterations in individuals with high myopia are not understood clearly, and serum biomarkers remain to be determined. The objectives of this study were to identify serum biomarkers and investigate the metabolic alterations of myopia. The serum metabolomics profiling was investigated on 30 high myopia cases and 30 controls (without myopia) using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS), and an independent additional cohort including 20 cases and 19 controls were investigated to validate potential metabolite candidates for biomarkers. According to the metabolic differences, the myopia patients and controls could be divided into different clusters and nine metabolites were found to be closely correlated with myopia. In the cohort of validation, eight metabolites were confirmed. Metabolic pathway analyses of these metabolites of high myopia involved abnormal phospholipid, diacylglycerol, amino acid, and vitamin metabolism, which were closely correlated with oxidative stress and inflammation. Multiple logistic regression analyses showed that γ-glutamyltyrosine and 12-oxo-20-trihydroxy-leukotriene B4 were potential biomarkers of myopia with a combined high sensitivity (97%), specificity (90%), and area under the curve value (0.983). These findings may contribute to an understanding of the pathophysiological changes and pathogenesis of myopia, and provide novel insight into the early prevention and control of high myopia.

Magnetic Resonance Spectroscopy-based Metabolomic Biomarkers for Typing, Staging, and Survival Estimation of Early-Stage Human Lung Cancer

Low-dose CT has shown promise in detecting early stage lung cancer. However, concerns about the adverse health effects of radiation and high cost prevent its use as a population-wide screening tool. Effective and feasible screening methods to triage suspicious patients to CT are needed. We investigated human lung cancer metabolomics from 93 paired tissue-serum samples with magnetic resonance spectroscopy and identified tissue and serum metabolomic markers that can differentiate cancer types and stages. Most interestingly, we identified serum metabolomic profiles that can predict patient overall survival for all cases (p = 0.0076), and more importantly for Stage I cases alone (n = 58, p = 0.0100), a prediction which is significant for treatment strategies but currently cannot be achieved by any clinical method. Prolonged survival is associated with relative overexpression of glutamine, valine, and glycine, and relative suppression of glutamate and lipids in serum.

The value of serum metabolomics analysis in predicting the response to cardiac resynchronization therapy.

ObjectiveTo construct a prediction model based on metabolic profiling for predicting the response to cardiac resynchronization therapy (CRT).MethodsPeripheral venous (PV) and coronary sinus (CS) blood samples were collected from 25 patients with heart failure (HF) at the time of CRT implantation, and PV blood samples were obtained from ten healthy controls. The serum samples were analyzed by liquid chromatography-mass spectrometry (LC-MS). As per the clinical and echocardiographic assessment at the 6-month follow-up, the HF patients were categorized as CRT responders and non-responders.ResultsHF patients had altered serum metabolomic profiles that were significantly different from those of the healthy controls. Differential metabolites were also observed between CRT responders and non-responders. A prediction model for CRT response (CRT-Re) was constructed using the concentration levels of the differential metabolites, L-arginine and taurine. The optimal cutoff value of the CRT-Re model was found to be 0.343 by ROC analysis (sensitivity, 88.2%; specificity, 87.5%; Area under curve (AUC) = 0.897, P = 0.002). The concentration levels of the differential metabolites, L-arginine and lysyl-gamma-glutamate, in PV serum were significantly correlated with that in CS serum (r = 0.945 and 0.680, respectively, all P < 0.001).ConclusionsOur results suggest that serum-based metabolic profiling may be a potential complementary screening tool for predicting the outcome of CRT.

Characterisation of the Serum Metabolic Signature of Cholangiocarcinoma in a United Kingdom Cohort

A distinct serum metabonomic pattern has been previously revealed to be associated with various forms of liver disease. Here, we aimed to apply mass spectrometry to obtain serum metabolomic profiles from individuals with cholangiocarcinoma and benign hepatobiliary diseases to gain an insight into pathogenesis and search for potential early-disease biomarkers. Serum samples were profiled using a hydrophilic interaction liquid chromatography platform, coupled to a mass spectrometer. A total of 47 serum specimens from 8 cholangiocarcinoma cases, 20 healthy controls, 8 benign disease controls (bile duct strictures)and 11 patients with hepatocellular carcinoma (as malignant disease controls) were included. Data analysis was performed using univariate and multivariate statistics. The serum metabolome disparities between the metabolite profiles from healthy controls and patients with hepatobiliary disease were predominantly related to changes in lipid and lipid-derived compounds (phospholipids, bile acids and steroids) and amino acid metabolites (phenylalanine). A metabolic pattern indicative of inflammatory response due to cirrhosis and cholestasis was associated with the disease groups. The abundance of phospholipid metabolites was altered in individuals with liver disease, particularly cholangiocarcinoma, but no significant difference was seen between profiles from patients with benign biliary strictures and cholangiocarcinoma. The serum metabolome in cholangiocarcinoma exhibited changes in metabolites related to inflammation, altered energy production and phospholipid metabolism. This study serves to highlight future avenues for biomarker research in large-scale studies.

2296-PUB: Serum Metabolome Reflects the Status of Glucose Metabolism of Japanese Pregnant Women

Objective: We reported that some metabolites during early pregnancy could predict subsequent development of gestational diabetes. In this study, to elucidate the metabolic status of pregnant women associated with glucose metabolism, we examined the association of the metabolomic profile and the glycohemoglobin (A1c) levels in Japanese pregnant women. Research Design and Methods: This study was conducted with participants enrolled by the Center of Chiba Unit of the Japan Environment and Children’s Study. Of those, 237 participants were enrolled in this study. Metabolite levels of their serum obtained during early pregnancy were analyzed using hydrophilic interaction chromatography tandem mass spectrometry. To examine the association between A1c levels and metabolic profiles, we performed least absolute shrinkage and selection operator (lasso) regression. The study protocol was approved by the Biomedical Research Ethics Committee of the Graduate School of Medicine, Chiba University. Results: Characteristics of participants were followed: age 32.0 (5.0) years, pre-gestational BMI 22.7 (4.6) kg/m2, A1c 5.09 (0.37) %. The association between their A1c levels and the metabolomic profiles, analyzed using lasso regression showed that nineteen metabolites were associated with A1c levels. Enrichment analysis revealed that the metabolites associated with A1c were related to ABC transporters, aminoacyl-tRNA biosynthesis, arginine and proline metabolism, glycine, serine and threonine metabolism, and metabolic pathway. Conclusion: Our results suggest the association of the serum metabolomic profile with the level of glycemia during pregnancy. Disclosure K. Sakurai: None. A. Eguchi: None. M. Watanabe: None. M. Yamamoto: None. C. Mori: None. Funding Japan Society for the Promotion of Science; Japan Ministry of the Environment

Biomarkers identified by serum metabolomic analysis to predict biologic treatment response in rheumatoid arthritis patients.

Biologic treatment has recently revolutionized the management of RA. Despite this success, ∼30-40% of the patients undergoing biologic treatment respond insufficiently. The aim of this study was to identify several specific reliable metabolites for predicting the response of RA patients to TNF-α inhibitors (TNFi) and abatacept (ABT), using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS). We collected serum from RA patients with moderate or high disease activity prior to biologic treatment, and obtained the serum metabolomic profiles of these samples using CE-TOFMS. The patients' response was determined 12 weeks after starting biologic treatment, according to the EULAR response criteria. We compared the metabolites between the response and non-response patient groups and analysed their discriminative ability. Among 43 total patients, 14 of 26 patients in the TNFi group and 6 of 17 patients in the ABT group responded to the biologic treatment. Of the metabolites separated by CE-TOFMS, 196 were identified as known substances. Using an orthogonal partial least-squares discriminant analysis, we identified five metabolites as potential predictors of TNFi responders and three as predictors of ABT responders. Receiver operating characteristic analyses for multiple biomarkers revealed an area under the curve (AUC) of 0.941, with a sensitivity of 85.7% and specificity of 100% for TNFi, and an AUC of 0.985, with a sensitivity of 100% and specificity of 90.9% for ABT. By metabolomic analysis, we identified serum biomarkers that have a high ability to predict the response of RA patients to TNFi or ABT treatment.

Serum metabolomic profiling and its association with 25-hydroxyvitamin D

The beneficial effect of vitamin D on the risk of non-musculoskeletal diseases has been investigated in observational studies and randomized clinical trials, but the findings were inconsistent. Identification of the metabolomic profile associated with vitamin D helps to identify novel biomarkers and increase the understanding of the biochemical and physiological role of vitamin D in different health conditions. Serum metabolomic profiling was performed using liquid chromatography/tandem mass spectrometry [LC/MS] and their association with serum 25(OH)D was evaluated using multivariable linear regression in the baseline cohort of 316 participants (aged 20 or above; 92 men, 224 women; mean age±SD: 48.1±15.8 years) and in the follow-up cohort of 275 participants (aged 20 or above; 12 men, 263 women; mean age: 56.2±9.6) of the Hong Kong Osteoporosis Study. We discovered and validated potential metabolites; and by meta-analysis of these associations in two cohorts, we identified metabolites that were significantly associated with serum 25(OH)D levels. Among 835 known metabolites, 102 metabolites showed significant correlation with 25(OH)D levels at baseline visit. Of these metabolites, 27 were validated in the follow-up visit. In meta-analysis of data from these two visits, 13 metabolites were highly correlated with 25(OH)D. The majority of metabolites identified were lipid in nature. Docosahexaenoylcarnitine and eicosapentaenoylcholine had the highest correlations, with effect estimates 0.2554 (p=9.60×10-9) and 0.1682 (p=4.94×10-7) respectively. In Hong Kong Chinese at least, serum vitamin D level is closely related to lipid metabolism. Our finding highlights an important new direction in the study of vitamin D in different health conditions.

Global serum metabolomics profiling of colorectal cancer (Review)

Accurate diagnosis of colorectal cancer (CRC) relies on the use of invasive tools such as colonoscopy and sigmoidoscopy. Non-invasive tools are less sensitive in detecting the disease, particularly in the early stage. A number of researchers have used metabolomics analyses on serum/plasma samples of patients with CRC compared with normal healthy individuals in an effort to identify biomarkers for CRC. The aim of the present review is to compare reported serum metabolomics profiles of CRC and to identify common metabolites affected among these studies. A literature search was performed to include any experimental studies on global metabolomics profile of CRC using serum/plasma samples published up to March 2018. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool was used to assess the quality of the studies reviewed. In total, nine studies were included. The studies used various analytical platforms and were performed on different populations. A pathway enrichment analysis was performed using the data from all the studies under review. The most affected pathways identified were protein biosynthesis, urea cycle, ammonia recycling, alanine metabolism, glutathione metabolism and citric acid cycle. The metabolomics analysis revealed levels of metabolites of glycolysis, tricarboxylic acid cycle, anaerobic respiration, protein, lipid and glutathione metabolism were significantly different between cancer and control samples. Although the majority of differentiating metabolites identified were different in the different studies, there were several metabolites that were common. These metabolites include pyruvic acid, glucose, lactic acid, malic acid, fumaric acid, 3-hydroxybutyric acid, tryptophan, phenylalanine, tyrosine, creatinine and ornithine. The consistent dysregulation of these metabolites among the different studies suggest the possibility of common diagnostic biomarkers for CRC.

Metabolomic profiling for identification of potential biomarkers in patients with dermatomyositis.

Dermatomyositis (DM) is a rare autoimmune myopathy characterized by skin lesions, proximal muscle weakness and muscle inflammation. The pathogenesis of DM is unclear, and identification of reliable biomarkers for early diagnosis of DM is critical for design of a specific therapy for this disease. To find and identify potential serum biomarkers in DM patients. We performed an untargeted metabolomic approach using UHPLC-MS/MS. The blood serum metabolomic profiles of 26 DM patients and 26 healthy controls were collected. Multivariate analysis of the metabolomic profile was applied to differentiate DM patients and controls and to find potential biomarkers. A significantly disturbed metabolic profile of DM patients was observed. Pathway analysis showed that aminoacyl-tRNA biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, and nitrogen metabolism are the most prominently altered pathways in DM. Receiver operating characteristic curve indicated that glutamine, methionine, isoleucine, tryptophan, glutamic acid, indole, protocatechuic acid, and phenylalanine were potential biomarkers for DM diagnosis in terms of both sensitivity and specificity. Our study provides new insight into underlying mechanisms of DM, and we suggest that we should pay more attention to these metabolic pathways in the prevention and treatment of DM.

Metabolomics Profiling of Visceral Adipose Tissue: Results From MESA and the NEO Study.

BackgroundIdentifying associations between serum metabolites and visceral adipose tissue (VAT) could provide novel biomarkers of VAT and insights into the pathogenesis of obesity‐related diseases. We aimed to discover and replicate metabolites reflecting pathways related to VAT.Methods and ResultsAssociations between fasting serum metabolites and VAT area (by computed tomography or magnetic resonance imaging) were assessed with cross‐sectional linear regression of individual‐level data from participants in MESA (Multi‐Ethnic Study of Atherosclerosis; discovery, N=1103) and the NEO (Netherlands Epidemiology of Obesity) study (replication, N=2537). Untargeted 1H nuclear magnetic resonance metabolomics profiling of serum was performed in MESA, and metabolites were replicated in the NEO study using targeted 1H nuclear magnetic resonance spectroscopy. A total of 30 590 metabolomic spectral variables were evaluated. After adjustment for age, sex, race/ethnicity, socioeconomic status, smoking, physical activity, glucose/lipid‐lowering medication, and body mass index, 2104 variables representing 24 nonlipid and 49 lipid/lipoprotein subclass metabolites remained significantly associated with VAT (P=4.88×10−20–1.16×10−3). These included conventional metabolites, amino acids, acetylglycoproteins, intermediates of glucose and hepatic metabolism, organic acids, and subclasses of apolipoproteins, cholesterol, phospholipids, and triglycerides. Metabolites mapped to 31 biochemical pathways, including amino acid substrate use/metabolism and glycolysis/gluconeogenesis. In the replication cohort, acetylglycoproteins, branched‐chain amino acids, lactate, glutamine (inversely), and atherogenic lipids remained associated with VAT (P=1.90×10−35–8.46×10−7), with most associations remaining after additional adjustment for surrogates of VAT (glucose level, waist circumference, and serum triglycerides), reflecting novel independent associations.ConclusionsWe identified and replicated a metabolite panel associated with VAT in 2 community‐based cohorts. These findings persisted after adjustment for body mass index and appear to define a metabolic signature of visceral adiposity.

Identification of Lysophosphatidylcholines and Sphingolipids as Potential Biomarkers for Acute Aortic Dissection via Serum Metabolomics

Acute aortic dissection (AAD) is a severe clinical emergency with a high mortality, and is easily misdiagnosed in its early stage. This study aimed at discovering serum metabolomic markers with the potential to diagnose AAD and distinguish between two subtypes of AAD. Thirty-five patients with AAD, including 20 with Stanford type A and 15 with Stanford type B were enrolled in this study, together with 20 healthy controls. All patients with AAD were admitted within 72 h of onset. Serum metabolomics profiles were determined by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry and the data were analysed by principal component analysis and partial least squares discriminant analysis. A total of 17 metabolites differing between the control and AAD groups were finally screened and identified as lysophosphatidylcholines (LPC) and sphingolipids including sphinganine, phytosphingosine, sphingomyelin, and ceramide. Compared with those in the healthy control group, LPC levels were significantly lower in both the Stanford type A and type B AAD groups. Interestingly, sphingolipids, including sphinganine, phytosphingosine, and ceramide, were remarkably reduced in the Stanford type A AAD group, but not in the Stanford type B AAD group. Subgroup analysis showed that the changes in LPC and sphingolipid levels were unrelated to hypertension or gender. The present results indicate that LPCs and sphingolipids are significantly altered in patients with AAD, and several sphingolipids, such as sphinganine, phytosphingosine, and ceramide, were dramatically decreased in patients with Stanford type A AAD. A combination of these two families of metabolites could serve as a potential biomarker for the diagnosis of AAD and distinguishing between Stanford type A and Stanford type B.

Prospective serum metabolomic profiling of lethal prostate cancer.

Impaired metabolism may play an important role in the pathogenesis of lethal prostate cancer, yet there is a paucity of evidence regarding the association. We conducted a large prospective serum metabolomic analysis of lethal prostate cancer in 523 cases and 523 matched controls nested within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Median time from baseline fasting serum collection to prostate cancer death was 18 years (maximum 30 years). We identified 860 known biochemicals through an ultrahigh-performance LC-MS/MS platform. Conditional logistic regression models estimated odds ratios (OR) and 95% confidence intervals of risk associated with 1-standard deviation (s.d.) increases in log-metabolite signals. We identified 34 metabolites associated with lethal prostate cancer with a false discovery rate (FDR) < 0.15. Notably, higher serum thioproline, and thioproline combined with two other cysteine-related amino acids and redox metabolites, cystine and cysteine, were associated with reduced risk (1-s.d. OR = 0.75 and 0.71, respectively; p ≤ 8.2 × 10-5 ). By contrast, the dipeptide leucylglycine (OR = 1.36, p = 8.2 × 10-5 ), and three gamma-glutamyl amino acids (OR = 1.28-1.30, p ≤ 4.6 × 10-4 ) were associated with increased risk of lethal prostate cancer. Cases with metastatic disease at diagnosis (n = 179) showed elevated risk for several lipids, including especially the ketone body 3-hydroxybutyrate (BHBA), acyl carnitines, and dicarboxylic fatty acids (1.37 ≤ OR ≤ 1.49, FDR < 0.15). These findings provide a prospective metabolomic profile of lethal prostate cancer characterized by altered biochemicals in the redox, dipeptide, pyrimidine, and gamma-glutamyl amino acid pathways, whereas ketone bodies and fatty acids were associated specifically with metastatic disease.

Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome.

There remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we evaluated whether integration of blood transcriptional profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression. Compared to either alone, the combined application of pre-existing transcriptome- and metabolome-based signatures more accurately predicted TB progression in the HHC cohorts and more accurately predicted disease severity in the NHPs. Pathway and data-driven correlation analyses of the integrated transcriptional and metabolomic datasets further identified novel immunometabolomic signatures significantly associated with TB progression in HHCs and NHPs, implicating cortisol, tryptophan, glutathione, and tRNA acylation networks. These results demonstrate the power of multi-omics analysis to provide new insights into complex disease processes.