Exploratory Study of Relationships Between Malodor and Urine Metabolomics
This trial is active, not recruiting.
|Condition||nutritional and metabolic diseases|
|Sponsor||Mebo Research, Inc.|
|Collaborator||University of Alberta|
|Start date||February 2016|
|End date||March 2017|
|Trial size||54 participants|
|Trial identifier||NCT02683876, 201505010014MEBO|
The purpose of this study is to identify metabolic signatures associated with malodor conditions. The investigators will perform state-of-the art metabolomics tests and bioinformatic data mining to explore if conditions leading to malodor can be screened by metabolomic profiling of urine samples.
Differences in metabolite concentrations measured by mass spectrometry, comparing urine samples from individuals with malodor issues, and age-matched healthy controls.
time frame: ten months
Correlations between urine biomarkers and frequency/severity of malodor symptoms (questionnaires)
time frame: one year
All participants at least 18 years old.
Inclusion Criteria: - 18 years or older - unpredictable and uncontrollable episodes of malodor - willing and able to ship a urine sample (in the kit provided) by an overnight courier to Edmonton, Alberta, Canada - good general health Exclusion Criteria: - serious medical conditions that require treatment - conditions that, in the opinion of the investigator, would prevent participation - under the age of 18 - elect not to participate in the study
|Official title||Metabolomic Profiling of Urine Samples for the Identification of Novel Biomarkers and Mechanisms in the Diagnosis and Management of Malodor Associated With Metabolic Inefficiencies|
|Principal investigator||David Wishart, PhD|
|Description||In this study, metabolite profiling analysis will be carried out on urine samples of individuals with malodor conditions related to metabolism inefficiencies. Metabolic profiles will be identified using the metabolomics equipment located in the NMR, HPLC and MS facilities of the Metabolomics Innovation Centre (TMIC). Multivariate statistical analyses will be used, as well as other approaches to mine complex data from heterogeneous sources.|
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