Disease activity and cognition in rheumatoid arthritis : an open label pilot study

Acknowledgements This work was supported in part by NIHR Newcastle Biomedical Research Centre. Funding for this study was provided by Abbott Laboratories. Abbott Laboratories were not involved in study design; in the collection, analysis and interpretation of data; or in the writing of the report.Peer reviewedPublisher PD

Detection of Hepatocellular Carcinoma in Hepatitis C Patients: Biomarker Discovery by LC-MS

Hepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n=37) and HCV (n=21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC-MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p<0.05) and sufficiently (FC≤0.7 or FC≥1.3) showed the best performance using p-values alone, the PLS-DA model was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC-MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between highrisk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLSDA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application

Bile Acids Conjugation in Human Bile Is Not Random: New Insights from 1H-NMR Spectroscopy at 800 MHz

Bile acids constitute a group of structurally closely related molecules and represent the most abundant constituents of human bile. Investigations of bile acids have garnered increased interest owing to their recently discovered additional biological functions including their role as signaling molecules that govern glucose, fat and energy metabolism. Recent NMR methodological developments have enabled single-step analysis of several highly abundant and common glycine- and taurine- conjugated bile acids, such as glycocholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurocholic acid, taurodeoxycholic acid, and taurochenodeoxycholic acid. Investigation of these conjugated bile acids in human bile employing high field (800 MHz) (1)H-NMR spectroscopy reveals that the ratios between two glycine-conjugated bile acids and their taurine counterparts correlate positively (R2 = 0.83-0.97; p = 0.001 x 10(-2)-0.006 x 10(-7)) as do the ratios between a glycine-conjugated bile acid and its taurine counterpart (R2 = 0.92-0.95; p = 0.004 x 10(-3)-0.002 x 10(-10)). Using such correlations, concentration of individual bile acids in each sample could be predicted in good agreement with the experimentally determined values. These insights into the pattern of bile acid conjugation in human bile between glycine and taurine promise useful clues to the mechanism of bile acids' biosynthesis, conjugation and enterohepatic circulation, and may improve our understanding of the role of individual conjugated bile acids in health and disease

Modelling national HIV/AIDS epidemics: revised approach in the UNAIDS Estimation and Projection Package 2011

Objective: United Nations Programme on HIV/AIDS reports regularly on estimated levels and trends in HIV/AIDS epidemics, which are evaluated using an epidemiological model within the Estimation and Projection Package (EPP). The relatively simple four-parameter model of HIV incidence used in EPP through the previous round of estimates has encountered challenges when attempting to fit certain data series on prevalence over time, particularly in settings with long running epidemics where prevalence has increased recently. To address this, the most recent version of the modelling package (EPP 2011) includes a more flexible epidemiological model that allows HIV infection risk to vary over time. This paper describes the technical details of this flexible approach to modelling HIV transmission dynamics within EPP 2011. Methodology For the flexible modelling approach, the force of infection parameter, r, is allowed to vary over time through a random walk formulation, and an informative prior distribution is used to improve short-term projections beyond the last year of data. Model parameters are estimated using a Bayesian estimation approach in which models are fit to HIV seroprevalence data from surveillance sites. Results: This flexible model can yield better estimates of HIV prevalence over time in situations where the classic EPP model has difficulties, such as in Uganda, where prevalence is no longer falling. Based on formal out-of-sample projection tests, the flexible modelling approach also improves predictions and CIs for extrapolations beyond the last observed data point. Conclusions: We recommend use of a flexible modelling approach where data are sufficient (eg, where at least 5 years of observations are available), and particularly where an epidemic is beyond its peak

PPARα contributes to protection against metabolic and inflammatory derangements associated with acute kidney injury in experimental sepsis

Abstract Sepsis‐associated acute kidney injury (AKI) is a significant problem in critically ill children and adults resulting in increased morbidity and mortality. Fundamental mechanisms contributing to sepsis‐associated AKI are poorly understood. Previous research has demonstrated that peroxisome proliferator‐activated receptor α (PPARα) expression is associated with reduced organ system failure in sepsis. Using an experimental model of polymicrobial sepsis, we demonstrate that mice deficient in PPARα have worse kidney function, which is likely related to reduced fatty acid oxidation and increased inflammation. Ultrastructural evaluation with electron microscopy reveals that the proximal convoluted tubule is specifically injured in septic PPARα deficient mice. In this experimental group, serum metabolomic analysis reveals unanticipated metabolic derangements in tryptophan‐kynurenine‐NAD+ and pantothenate pathways. We also show that a subgroup of children with sepsis whose genome‐wide expression profiles are characterized by repression of the PPARα signaling pathway has increased incidence of severe AKI. These findings point toward interesting associations between sepsis‐associated AKI and PPARα‐driven fatty acid metabolism that merit further investigation

Metabolomic Characterization of Human Model of Liver Rejection Identifies Aberrancies Linked to Cyclooxygenase (COX) and Nitric Oxide Synthase (NOS)

BACKGROUND Acute liver rejection (ALR), a significant complication of liver transplantation, burdens patients, healthcare payers, and the healthcare providers due to an increase in morbidity, cost, and resources. Despite clinical resolution, ALR is associated with an increased risk of graft loss. A unique protocol of delayed immunosuppression used in our institute provided a model to characterize metabolomic profiles in human ALR. MATERIAL AND METHODS Twenty liver allograft biopsies obtained 48 hours after liver transplantation in the absence of immunosuppression were studied. Hepatic metabolites were quantitated in these biopsies by liquid chromatography and mass spectroscopy (LC/MS). Metabolite profiles were compared among: 1) biopsies with reperfusion injury but no histological evidence of rejection (n=7), 2) biopsies with histological evidence of moderate or severe rejection (n=5), and 3) biopsies with histological evidence of mild rejection (n=8). RESULTS There were 133 metabolites consistently detected by LC/MS and these were prioritized using variable importance to projection (VIP) analysis, comparing moderate or severe rejection vs. no rejection or mild rejection using partial least squares discriminant statistical analysis (PLS-DA). Twenty metabolites were identified as progressively different. Further PLS-DA using these metabolites identified 3 metabolites (linoleic acid, γ-linolenic acid, and citrulline) which are associated with either cyclooxygenase or nitric oxide synthase functionality. CONCLUSIONS Hepatic metabolic aberrancies associated with cyclooxygenase and nitric oxide synthase function occur contemporaneous with ALR. Additional studies are required to better characterize the role of these metabolic pathways to enhance utility of the metabolomics approach in diagnosis and outcomes of ALR

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies

Esophageal Cancer Metabolite Biomarkers Detected by LC-MS and NMR Methods

Background: Esophageal adenocarcinoma (EAC) is a rarely curable disease and is rapidly rising worldwide in incidence. Barret’s esophagus (BE) and high-grade dysplasia (HGD) are considered major risk factors for invasive adenocarcinoma. In the current study, unbiased global metabolic profiling methods were applied to serum samples from patients with EAC, BE and HGD, and healthy individuals, in order to identify metabolite based biomarkers associated with the early stages of EAC with the goal of improving prognostication. Methodology/Principal Findings: Serum metabolite profiles from patients with EAC (n = 67), BE (n = 3), HGD (n = 9) and healthy volunteers (n = 34) were obtained using high performance liquid chromatography-mass spectrometry (LC-MS) methods. Twelve metabolites differed significantly (p,0.05) between EAC patients and healthy controls. A partial leastsquares discriminant analysis (PLS-DA) model had good accuracy with the area under the receiver operative characteristic curve (AUROC) of 0.82. However, when the results of LC-MS were combined with 8 metabolites detected by nuclear magnetic resonance (NMR) in a previous study, the combination of NMR and MS detected metabolites provided a much superior performance, with AUROC = 0.95. Further, mean values of 12 of these metabolites varied consistently from healthy controls to the high-risk individuals (BE and HGD patients) and EAC subjects. Altered metabolic pathways including a number of amino acid pathways and energy metabolism were identified based on altered levels of numerous metabolites

NMR-based metabolomics study of canine bladder cancer

AbstractBladder cancer is one of the leading lethal cancers worldwide. With the high risk of recurrence for bladder cancer following the initial diagnoses, lifelong monitoring of patients is necessary. The lack of adequate sensitivity and specificity of current noninvasive monitoring approaches including urine cytology, other urine tests, and imaging, underlines the importance of studies that focus on the detection of more reliable biomarkers for this cancer. The emerging area of metabolomics, which deals with the analysis of a large number of small molecules in a single step, promises immense potential for discovering metabolite markers for screening and monitoring treatment response and recurrence in patients with bladder cancer. Since naturally-occurring canine transitional cell carcinoma of the urinary bladder is very similar to human invasive bladder cancer, spontaneous canine transitional cell carcinoma has been applied as a relevant animal model of human invasive transitional cell carcinoma. In this study, we have focused on profiling the metabolites in urine from dogs with transitional cell carcinoma and healthy control dogs combining nuclear magnetic resonance spectroscopy and statistical analysis methods. 1H NMR-based metabolite profiling analysis was shown to be an effective approach for differentiating samples from dogs with transitional cell carcinoma and healthy controls based on a partial least square-discriminant analysis of the NMR spectra. In addition, there were significant differences in the levels of six individual metabolites between samples from dogs with transitional cell carcinoma and the control group based on the Student's t-test. These metabolites were selected to build a separate partial least square‐discriminant analysis model that was then used to test the classification accuracy. The result showed good classification between transitional cell carcinoma and control groups with the area under the receiver operating characteristic curve of 0.85. The sensitivity and specificity of the model were 86% and 78%, respectively. These results suggest that urine metabolic profiling may have potential for early detection of bladder cancer and of bladder cancer recurrence following treatment, and may enhance our understanding of the mechanisms involved

The future of NMR-based metabolomics

The two leading analytical approaches to metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Although currently overshadowed by MS in terms of numbers of compounds resolved, NMR spectroscopy offers advantages both on its own and coupled with MS. NMR data are highly reproducible and quantitative over a wide dynamic range and are unmatched for determining structures of unknowns. NMR is adept at tracing metabolic pathways and fluxes using isotope labels. Moreover, NMR is non-destructive and can be utilized in vivo. NMR results have a proven track record of translating in vitro findings to in vivo clinical applications