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

Abstract

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.

Integrative metabolomics as emerging tool to study autophagy regulation

“…Due to high throughput and robust detection in metabolomics, it enables characterization of a high number of metabolites in large sample series with reliable results. Information gathered in these studies will further improve current knowledge about autophagy, its regulation and its outcome 11 12 53 174 .…”

“…In an untargeted approach, a general profile of metabolites present in the biological sample is obtained. This is mainly used to detect global differences in the metabolic fingerprint 11 53 . For targeted analysis, the experimental setup is optimized for the detection of distinct metabolites 54 .…”

Large‐scale profiling of serum metabolites in African American and European American patients with bladder cancer reveals metabolic pathways associated with patient survival

“…[11][12][13] As markers, metabolites could be used to improve individual risk prediction in BLCA. Mass spectrometry (MS), in combination with various liquid chromatography methods, has been previously applied to study BLCA-induced metabolomic alterations in tissues, 11,14-17 urine, [18][19][20][21] and serum. 22 Earlier reports have identified tryptophan, hypoxanthine, and glycocholic acid as serum metabolites that can distinguish BLCA from healthy normal samples.…”

Predicting human health from biofluid-based metabolomics using machine learning

“…Cancer represented the largest category, with 14 studies 11,12,25,29,[31][32][33][34][35][36][37][38][39] ; cardiovascular 28 , rheumatologic 26 , ‡ , renal ‡ , and respiratory 16 diseases were represented by only one or two studies. Other health states with a small to medium number of studies included neuro/neuropsychiatric 18,19 , endocrine 21,24,40,41 , and infectious disease 14,15,27,30,42,43 , as well as a general 'other' category 17,[44][45][46] .…”