Relationship Between Serum Total Testosterone and Coronary Artery Disease in Men.

BACKGROUND: Male sex has always been considered as an independent risk factor for cardiovascular disease. But recent studies have shown controversial results. This study aimed to investigate the relation of serum testosterone withrisk factors of coronary artery diseasesand with degree of severity of coronary artery stenosisin men with coronary artery diseases. METHODS: After applying inclusion and exclusion criteria 102 men (aged 60.42 += 11.11), were included. Fasting blood sample were obtained and blood sugar, total testosterone and lipid profile were measured. Severity of coronary stenosis was estimated by Gensini score. The relationships were assessed using chi-square test, one way analysis of variance and Pearson's Correlation. RESULTS: Of the total 102 patients, majority of them 42 (41.2%) had triple vessel disease. Testosterone (nmol/L) was found to be 12.01 ± 6.1. Cardiovascular diseaserisk factors like age, body mass index etc. were found to be negatively correlated with testosterone but not statistically significant. Likewise, Gensini score also correlated negatively with testosteronebut not up to the threshold of statistical significance (r=-0.069, p-value = 0.496). Similar results were obtained when number of vessels involved and testosterone were compared. However, the number of diabetic patients gradually decreased with the increasing value of testosterone in the three tertile group (p-value = 0.040). CONCLUSIONS: This study could not find significant association between testosterone and coronary artery diseases, however low testosterone was associated with diabetes mellitus.

The human serum metabolome.

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.

SMPDB: The Small Molecule Pathway Database.

The Small Molecule Pathway Database (SMPDB) is an interactive, visual database containing more than 350 small-molecule pathways found in humans. More than 2/3 of these pathways (>280) are not found in any other pathway database. SMPDB is designed specifically to support pathway elucidation and pathway discovery in clinical metabolomics, transcriptomics, proteomics and systems biology. SMPDB provides exquisitely detailed, hyperlinked diagrams of human metabolic pathways, metabolic disease pathways, metabolite signaling pathways and drug-action pathways. All SMPDB pathways include information on the relevant organs, organelles, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Each small molecule is hyperlinked to detailed descriptions contained in the Human Metabolome Database (HMDB) or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. All SMPDB pathways are accompanied with detailed descriptions, providing an overview of the pathway, condition or processes depicted in each diagram. The database is easily browsed and supports full text searching. Users may query SMPDB with lists of metabolite names, drug names, genes/protein names, SwissProt IDs, GenBank IDs, Affymetrix IDs or Agilent microarray IDs. These queries will produce lists of matching pathways and highlight the matching molecules on each of the pathway diagrams. Gene, metabolite and protein concentration data can also be visualized through SMPDB's mapping interface. All of SMPDB's images, image maps, descriptions and tables are downloadable. SMPDB is available at: http://www.smpdb.ca.

HMDB: a knowledgebase for the human metabolome.

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

The human cerebrospinal fluid metabolome.

With continuing improvements in analytical technology and an increased interest in comprehensive metabolic profiling of biofluids and tissues, there is a growing need to develop comprehensive reference resources for certain clinically important biofluids, such as blood, urine and cerebrospinal fluid (CSF). As part of our effort to systematically characterize the human metabolome we have chosen to characterize CSF as the first biofluid to be intensively scrutinized. In doing so, we combined comprehensive NMR, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography (LC) Fourier transform-mass spectrometry (FTMS) methods with computer-aided literature mining to identify and quantify essentially all of the metabolites that can be commonly detected (with today's technology) in the human CSF metabolome. Tables containing the compounds, concentrations, spectra, protocols and links to disease associations that we have found for the human CSF metabolome are freely available at http://www.csfmetabolome.ca.

DrugBank: a knowledgebase for drugs, drug actions and drug targets.

DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With approximately 4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more 'experimental' drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food-drug interactions, drug-drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca.