4.7 Article

Interstitial Cystitis-Associated Urinary Metabolites Identified by Mass-Spectrometry Based Metabolomics Analysis

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SCIENTIFIC REPORTS
卷 6, 期 -, 页码 -

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NATURE PUBLISHING GROUP
DOI: 10.1038/srep39227

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资金

  1. National Institutes of Health [1U01DK103260, 1R01DK100974, U24 DK097154, NIH NCATS UCLA CTSI UL1TR000124]
  2. Department of Defense [W81XWH-15-1-0415]
  3. Centers for Disease Controls and Prevention [1U01DP006079]
  4. IMAGINE NO IC Research Grant
  5. Steven Spielberg Discovery Fund in Prostate Cancer Research Career Development Award
  6. U.S.-Egypt Science and Technology Development Fund by the National Academies of Sciences, Engineering, and Medicine
  7. Inha University Research grant [INHA-50487]
  8. Interstitial Cystitis Association Pilot Grant
  9. Fishbein Family IC Research Grant
  10. New York Academy of Medicine
  11. Boston Children's Hospital Faculty Development
  12. NSF [MCB1139644, MCB1611846]
  13. NIH [P20HL113452, U24DK097154]
  14. American Heart Association [15SDG25760020, NIH 2R01HL091357-05]

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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.

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