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10.1186/s12882-015-0100-y http://scihub22266oqcxt.onion/10.1186/s12882-015-0100-y C4491861!4491861!26149577     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26149577&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Nephrol 2015 ; 16 (ä): ä Nephropedia Template TP {{tp|p=26149577|t=2015. Cross-sectional examination of metabolites and metabolic phenotypes in uremia |pdf=|usr=}}{{26149577}} gab.com Text Cross-sectional examination of metabolites and metabolic phenotypes in uremia JO: BMC Nephrol http://www.kidney.de/pget.php?&tw=1&pmid=26149577 #FOAvip Background: Although metabolomic approaches have begun to document numerous changes that arise in end stage renal disease (ESRD), how these alterations relate to established metabolic phenotypes in uremia is unknown. Methods: In 200 incident hemodialysis patients we used partial least squares discriminant analysis to identify which among 166 metabolites could best discriminate individuals with or without diabetes, and across tertiles of body mass index, serum albumin, total cholesterol, and systolic blood pressure. Results: Our data do not recapitulate metabolomic signatures of diabetes and obesity identified among individuals with normal renal function (e.g. elevations in branched chain and aromatic amino acids) and highlight several potential markers of diabetes status specific to ESRD, including xanthosine-5-phosphate and vanillylmandelic acid. Further, our data identify significant associations between elevated tryptophan and long-chain acylcarnitine levels and both decreased total cholesterol and systolic blood pressure in ESRD. Higher tryptophan levels were also associated with higher serum albumin levels, but this may reflect tryptophan?s significant albumin binding. Finally, an examination of the uremic retention solutes captured by our platform in relation to 24 clinical phenotypes provides a framework for investigating mechanisms of uremic toxicity. Conclusions: In sum, these studies leveraging metabolomic and metabolic phenotype data acquired in a well-characterized ESRD cohort demonstrate striking differences from metabolomics studies in the general population, and may provide clues to novel functional pathways in the ESRD population. Electronic supplementary material: The online version of this article (doi:10.1186/s12882-015-0100-y) contains supplementary material, which is available to authorized users. Twit Text FOAVip Cross-sectional examination of metabolites and metabolic phenotypes in uremia ????BMC Nephrol http://www.kidney.de/pget.php?&tw=1&pmid=26149577 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26149577 #FOAvip English Wikipedia <ref>{{Cite pmid|26149577}}</ref> Cross-sectional examination of metabolites and metabolic phenotypes in uremia #MMPMID26149577 Kalim S; Clish CB; Deferio JJ; Ortiz G; Moffet AS; Gerszten RE; Thadhani R; Rhee EP BMC Nephrol 2015[]; 16 (ä): ä PMID26149577show ga Background: Although metabolomic approaches have begun to document numerous changes that arise in end stage renal disease (ESRD), how these alterations relate to established metabolic phenotypes in uremia is unknown. Methods: In 200 incident hemodialysis patients we used partial least squares discriminant analysis to identify which among 166 metabolites could best discriminate individuals with or without diabetes, and across tertiles of body mass index, serum albumin, total cholesterol, and systolic blood pressure. Results: Our data do not recapitulate metabolomic signatures of diabetes and obesity identified among individuals with normal renal function (e.g. elevations in branched chain and aromatic amino acids) and highlight several potential markers of diabetes status specific to ESRD, including xanthosine-5-phosphate and vanillylmandelic acid. Further, our data identify significant associations between elevated tryptophan and long-chain acylcarnitine levels and both decreased total cholesterol and systolic blood pressure in ESRD. Higher tryptophan levels were also associated with higher serum albumin levels, but this may reflect tryptophan?s significant albumin binding. Finally, an examination of the uremic retention solutes captured by our platform in relation to 24 clinical phenotypes provides a framework for investigating mechanisms of uremic toxicity. Conclusions: In sum, these studies leveraging metabolomic and metabolic phenotype data acquired in a well-characterized ESRD cohort demonstrate striking differences from metabolomics studies in the general population, and may provide clues to novel functional pathways in the ESRD population. Electronic supplementary material: The online version of this article (doi:10.1186/s12882-015-0100-y) contains supplementary material, which is available to authorized users. äCross-sectional examination of metabolites and metabolic phenotypes in(epmc).pdf DeepDyve Pubget Overpricing