Welcome to CDC Stacks | Interstitial Cystitis-Associated Urinary Metabolites Identified by Mass-Spectrometry Based Metabolomics Analysis - 43883 | CDC Public Access
Stacks Logo
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
 
 
Help
Clear All Simple Search
Advanced Search
Interstitial Cystitis-Associated Urinary Metabolites Identified by Mass-Spectrometry Based Metabolomics Analysis
Filetype[PDF - 926.87 KB]


Details:
  • Pubmed ID:
    27976711
  • Pubmed Central ID:
    PMC5156939
  • Funding:
    R01 HL091357/HL/NHLBI NIH HHS/United States
    UL1 TR000124/TR/NCATS NIH HHS/United States
    P20 HL113452/HL/NHLBI NIH HHS/United States
    U24 DK097154/DK/NIDDK NIH HHS/United States
    U01 DP006079/DP/NCCDPHP CDC HHS/United States
    R01 DK100974/DK/NIDDK NIH HHS/United States
    U01 DK103260/DK/NIDDK NIH HHS/United States
  • Document Type:
  • Collection(s):
  • Description:
    This study on interstitial cystitis (IC) aims to identify a unique urine metabolomic profile associated with IC, which can be defined as an unpleasant sensation including pain and discomfort related to the urinary bladder, without infection or other identifiable causes. Although the burden of IC on the American public is immense in both human and financial terms, there is no clear diagnostic test for IC, but rather it is a disease of exclusion. Very little is known about the clinically useful urinary biomarkers of IC, which are desperately needed. Untargeted comprehensive metabolomic profiling was performed using gas-chromatography/mass-spectrometry to compare urine specimens of IC patients or health donors. The study profiled 200 known and 290 unknown metabolites. The majority of the thirty significantly changed metabolites before false discovery rate correction were unknown compounds. Partial least square discriminant analysis clearly separated IC patients from controls. The high number of unknown compounds hinders useful biological interpretation of such predictive models. Given that urine analyses have great potential to be adapted in clinical practice, research has to be focused on the identification of unknown compounds to uncover important clues about underlying disease mechanisms.