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Background: Colorectal cancer is one of the leading causes of cancer deaths worldwide. The detection and removal of the precursors to colorectal cancer, adenomatous polyps, is the key for screening. The aim of this study was to develop a clinically scalable (high throughput, low cost, and high sensitivity) mass spectrometry (MS)-based urine metabolomic test for the detection of adenomatous polyps. Methods: Prospective urine and stool samples were collected from 685 participants enrolled in a colorectal cancer screening program to undergo colonoscopy examination. Statistical analysis was performed on 69 urine metabolites measured by one-dimensional nuclear magnetic resonance spectroscopy to identify key metabolites. A targeted MS assay was then developed to quantify the key metabolites in urine. A MS-based urine metabolomic diagnostic test for adenomatous polyps was established using 67% samples (un-blinded training set) and validated using the remaining 33% samples (blinded testing set). Results: The MS-based urine metabolomic test identifies patients with colonic adenomatous polyps with an AUC of 0.692, outperforming the NMR based predictor with an AUC of 0.670. Conclusion: Here we describe a clinically scalable MS-based urine metabolomic test that identifies patients with adenomatous polyps at a higher level of sensitivity (86%) over current fecal-based tests (<18%). [ABSTRACT FROM AUTHOR]

Cancer-associated muscle wasting is associated with reduction in functional status, in response to treatment and in life expectancy. Methods currently used to assess muscle loss involve diagnostic imaging techniques such as computed tomography (CT), which are costly, inconvenient, invasive, time consuming and have limited ability to detect early or slowly evolving wasting. We present a novel approach using single time-point urinary metabolite profiles to determine whether a patient is experiencing muscle wasting. We analyzed 93 random urine samples from patients with cancer using H-NMR. Using two successive CT images we assessed their lumbar skeletal muscle area (cm) to estimate the rate of muscle change (% loss or gain over time) for each patient. The average muscle change over time was −4.71%/100 days in the muscle-losing group and +3.91%/100 days in the comparator group. Bivariate statistics identified metabolites related with muscle loss, including constituents and metabolites of muscle (creatine, creatinine, 3-OH-isovalerate), amino acids (Leu, Ile, Val, Ala, Thr, Tyr, Gln, Ser) and intermediary metabolites. We also applied machine-learning techniques to identify patterns of urinary metabolites that identify which patients are likely to lose muscle mass. We evaluated the predictive performance of 8 machine-learning approaches using fivefold cross validation and permutation testing, and found that SVM provided the best generalization accuracy (82.2%). These results suggest that H-NMR analysis of a single random urine sample may be a fast, cheap, safe and inexpensive tool to screen and monitor muscle loss, and that useful classifiers for predicting related metabolic conditions are possible with the methodology presented. [ABSTRACT FROM AUTHOR]

Phenol-Explorer, launched in 2009, is the only comprehensive web-based database on the content in foods of polyphenols, a major class of food bioactives that receive considerable attention due to their role in the prevention of diseases. Polyphenols are rarely absorbed and excreted in their ingested forms, but extensively metabolized in the body, and until now, no database has allowed the recall of identities and concentrations of polyphenol metabolites in biofluids after the consumption of polyphenol-rich sources. Knowledge of these metabolites is essential in the planning of experiments whose aim is to elucidate the effects of polyphenols on health. Release 2.0 is the first major update of the database, allowing the rapid retrieval of data on the biotransformations and pharmacokinetics of dietary polyphenols. Data on 375 polyphenol metabolites identified in urine and plasma were collected from 236 peer-reviewed publications on polyphenol metabolism in humans and experimental animals and added to the database by means of an extended relational design. Pharmacokinetic parameters have been collected and can be retrieved in both tabular and graphical form. The web interface has been enhanced and now allows the filtering of information according to various criteria. Phenol-Explorer 2.0, which will be periodically updated, should prove to be an even more useful and capable resource for polyphenol scientists because bioactivities and health effects of polyphenols are dependent on the nature and concentrations of metabolites reaching the target tissues. The Phenol-Explorer database is publicly available and can be found online at http://www.phenol-explorer.eu.Database URL: http://www.phenol-explorer.eu [ABSTRACT FROM AUTHOR]

Polyphenols are a major class of bioactive phytochemicals whose consumption may play a role in the prevention of a number of chronic diseases such as cardiovascular diseases, type II diabetes and cancers. Phenol-Explorer, launched in 2009, is the only freely available web-based database on the content of polyphenols in food and their in vivo metabolism and pharmacokinetics. Here we report the third release of the database (Phenol-Explorer 3.0), which adds data on the effects of food processing on polyphenol contents in foods. Data on >100 foods, covering 161 polyphenols or groups of polyphenols before and after processing, were collected from 129 peer-reviewed publications and entered into new tables linked to the existing relational design. The effect of processing on polyphenol content is expressed in the form of retention factor coefficients, or the proportion of a given polyphenol retained after processing, adjusted for change in water content. The result is the first database on the effects of food processing on polyphenol content and, following the model initially defined for Phenol-Explorer, all data may be traced back to original sources. The new update will allow polyphenol scientists to more accurately estimate polyphenol exposure from dietary surveys.

Phenol-Explorer, launched in 2009, is the only comprehensive web-based database on the content in foods of polyphenols, a major class of food bioactives that receive considerable attention due to their role in the prevention of diseases. Polyphenols are rarely absorbed and excreted in their ingested forms, but extensively metabolized in the body, and until now, no database has allowed the recall of identities and concentrations of polyphenol metabolites in biofluids after the consumption of polyphenol-rich sources. Knowledge of these metabolites is essential in the planning of experiments whose aim is to elucidate the effects of polyphenols on health. Release 2.0 is the first major update of the database, allowing the rapid retrieval of data on the biotransformations and pharmacokinetics of dietary polyphenols. Data on 375 polyphenol metabolites identified in urine and plasma were collected from 236 peer-reviewed publications on polyphenol metabolism in humans and experimental animals and added to the database by means of an extended relational design. Pharmacokinetic parameters have been collected and can be retrieved in both tabular and graphical form. The web interface has been enhanced and now allows the filtering of information according to various criteria. Phenol-Explorer 2.0, which will be periodically updated, should prove to be an even more useful and capable resource for polyphenol scientists because bioactivities and health effects of polyphenols are dependent on the nature and concentrations of metabolites reaching the target tissues. The Phenol-Explorer database is publicly available and can be found online at http://www.phenol-explorer.eu. Database URL: http://www.phenol-explorer.eu.

One of the growing challenges in life science research lies in finding useful, descriptive or quantitative data about newly reported biomolecules (genes, proteins, metabolites and drugs). An even greater challenge is finding information that connects these genes, proteins, drugs or metabolites to each other. Much of this information is scattered through hundreds of different databases, abstracts or books and almost none of it is particularly well integrated. While some efforts are being undertaken at the NCBI and EBI to integrate many different databases together, this still falls short of the goal of having some kind of human-readable synopsis that summarizes the state of knowledge about a given biomolecule - especially small molecules. To address this shortfall, we have developed BioSpider. BioSpider is essentially an automated report generator designed specifically to tabulate and summarize data on biomolecules - both large and small. Specifically, BioSpider allows users to type in almost any kind of biological or chemical identifier (protein/gene name, sequence, accession number, chemical name, brand name, SMILES string, InCHI string, CAS number, etc.) and it returns an in-depth synoptic report (approximately 3-30 pages in length) about that biomolecule and any other biomolecule it may target. This summary includes physico-chemical parameters, images, models, data files, descriptions and predictions concerning the query molecule. BioSpider uses a web-crawler to scan through dozens of public databases and employs a variety of specially developed text mining tools and locally developed prediction tools to find, extract and assemble data for its reports. Because of its breadth, depth and comprehensiveness, we believe BioSpider will prove to be a particularly valuable tool for researchers in metabolomics. BioSpider is available at: www.biospider.ca