Metabolomic Profiling of Urine Samples for the Identification of Novel Biomarkers and Mechanisms in the Diagnosis and Management of Malodor Associated With Metabolic Inefficiencies
Conditions: Nutritional and Metabolic Diseases
Sponsor: Mebo Research, Inc.

Irene Gabashvili NCT02683876 
Study Protocol and Statistical Analysis Plan 
 
 
Title: Exploratory Study of Relationships Between 
Malodor and Urine Metabolomics 
ClinicalTrials.gov Identifier: NCT02683876 
 
Date of first draft: August 26, 2015 
Current version date: January 28, 2016 
Date Approved by IRB: February 3, 2016  
The approval is issued under OHRP Federal-wide Assurance 
#00018466. 
Protocol Number:  201505010014MEBO 
 
Information provided by (Responsible Party): Irene Gabashvili, 
PhD, Principal Investigator 
 
 
 
 
 
 
 

Irene Gabashvili NCT02683876 
STATEMENT OF COMPLIANCE 
The study will be conducted in accordance with the International Conference on Harmonization 
guidelines for Good Clinical Practice (ICH E6), the Code of Federal Regulations on the Protection of 
Human Subjects (45 CFR Part 46), and the NIDCR Clinical Terms of Award.  All personnel involved in the 
conduct of this study have completed human subjects’ protection training.   
INTRODUCTION 
Certain body and breath odors are known to indicate serious infections and life-threatening conditions. 
Malodors without accompanying physical symptoms, however, could be a sign of psychologically but not 
physically debilitating errors of metabolism. One example is Trimethylaminuria (TMAU) leading to 
excessive excretion of foul-smelling trimethylamine (TMA) in the sweat and breath. Even this relatively 
straightforward disorder of choline metabolism exhibits complex genetic and environmental regulation. 
Trimethylaminuria is diagnosed based on the symptoms, a clinical exam, and a test to measure the level 
of trimethylamine in the urine after a choline load [1]. Genetic testing can also help confirm the 
diagnosis, but is not sufficient as not all mutations and all genes responsible for this condition have been 
mapped. Since clinical exam can miss the symptoms, and urinary TMA levels are abnormal in less than 
30% of sufferers with the most severe TMAU-like cases [2], new diagnostic tests are needed. 
 
AIM 
The aim of this observational study is to examine diagnostics potential of urine metabolomics for 
idiopathic body odor of metabolic origin (MEBO) and “People are Allergic to Me” (PATM) syndrome.  
 
RESEARCH PROPOSAL 
Urine has been used for medical diagnostics for more than 3000 years. It is easy to obtain, yet 
chemically complex providing an ideal snapshot of the body’s metabolic processes 
To identify metabolic signatures associated with malodor conditions, the investigators propose to 
perform state-of-the-art metabolomics tests and bioinformatic data analytics to explore if conditions 
leading to malodor can be screened by metabolomic profiling of urine samples. 
To capture metabolomic signatures in idiopathic malodor, we will ask participants to continue their 
usual diet and collect a morning urine sample.  At the same time, subjects will be asked to describe 
observations of daily living, describing diet, quality of life, activities, stress and other environmental 
exposures. 
Urine sample preparation is one of the simplest processes employed in bioanalysis and the complexity 
of this step usually differs according to the aim of the experiment. We will aim to keep the sample as 
intact as possible containing all unknown metabolites. When analyzing urine by GC-MS there is a need 
to pretreat the sample with urease (to reduce urea levels) that can diminish the abundance of some 
metabolites [3]. We aimed at establishing the most effective sample pretreatment for urea removing 
while preserving target metabolites in the specimen [4]. The Metabolomics Innovation Centre will send 
every participant a kit with detailed instructions on collection, handling and storage.  

Irene Gabashvili NCT02683876 
We will then apply a targeted metabolomics appro ach that uses a direct injection and tandem mass 
spectrometry (DI–MS/MS) coupled with a liquid chromatography–tandem mass spectrometry (LC–
MS/MS)-based metabolomics [4]. We will also use metabolite databases [5] diet analytics platform [6], 
Chenomx NMRSuite v7.0 software (Chenomx, Edmonton, Alberta, Canada), R libraries and VBA Excel 
macros.  
 
STUDY DESIGN 
 
Study Type: Observational 
Proposed Enrollment: 50 
Allocation: Non-Randomized 
Primary Purpose: Diagnostics 
 
Prior to  
Enrollment 
 
 
10 months   
after mailing  
kits 
 
 
 
 
 
 
 
RECRUITMENT 
Patients will be recruited from TMAU testing program, other previous studies and non-interventional 
surveys. In addition, the study will be advertised on blogs, social media patient support groups and on 
Clinicaltrials.gov. 
Inclusion Criteria: 
Total N:  Obtain informed consent. Screen potential participants by inclusion and 
exclusion criteria; obtain history, document. 
Final Assessments 
Follow-up assessments 
After specimens collected, examined, questionnaires completed) 

Irene Gabashvili NCT02683876 
• 
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 
 
STATISTICAL ANALYSIS 
 
STUDY HYPOTHESIS 
The hypothesis of this study is that, in spite of genetic and environmental heterogeneity, the pathology 
involves common patterns in the urine metabolome. 
 
SAMPLE SIZE 
No formal sample size calculation was performed. Obviously, larger sample numbers allow for higher 
accuracy. Our exploratory study aimed at recruiting the largest possible number of volunteers suffering 
from the above-mentioned rare conditions and residing in Canada.  
 
FINAL ANALYSIS PLAN 
 
Study coordinator will assign each human subject a de-identified unique identifier code. It will be used 
to link kit IDs with medical histories.  
The data will then be examined for errors (e.g., outliers - out of range values), consistency, missing and 
spurious values. Missing data will not be imputed unless specifically noted. Any apparently spurious data 
will be verified. Non verified data will be excluded from summaries or analyses. Initial data exploration 
will be undertaken using inspection of frequency distributions and plots (i.e., graphical methods such as 
histograms and box plots), estimation of z-scores, estimation of skewness and kurtosis, and test for 
normality (i.e., analytic methods). If necessary, data will be transformed and centered for minimizing 
problems. 
Standard summary statistics including means, standard deviations, proportions, and 95% confidence 
intervals will be produced for all measures. The focus of the data exploration will be on exploring the 
shape of the curves, identifying peaks and troughs. For the Primary Study Endpoint, we will identify 
population-wide signatures  

Irene Gabashvili NCT02683876 
Descriptive statistics will be calculated and compared across all groups. Inter-group differences will be 
computed using statistical tests.  
If the assumptions of the t-test cannot be met, yet the observations are independent, the Mann–
Whitney test will be used for two group comparisons, if the data are at least ordinal in nature. Wilcoxon 
matched pairs test will be used for samples of the same subjects before and after improvement of 
symptoms.  
Statistical analyses will be separately defined for each endpoint. There will be no adjustment of p-values 
for multiplicity unless specifically noted. P-values generated for this study are not intended to be 
conclusive, but provided for guidance only.  
Appropriate algorithms of clustering or statistical models will be used to define subgroups of 
MEBO/PATM subjects with symptoms of different severity based on the variation in specific metabolic, 
inflammatory or dietary markers. 
 
ETHICS/PROTECTION OF HUMAN SUBJECTS 
This study is to be conducted in accordance with applicable US government regulations and 
international standards of Good Clinical Practice, and applicable institutional research policies and 
procedures. This protocol and any amendments are submitted to a properly constituted independent 
Institutional Review Board (IRB). 
Potential Risks and Discomforts 
The primary risk to participants is loss of privacy. To mitigate such risk, we link all data to coded 
identifiers for the participants during the collection and transmission process. Coded data will be only 
accessible to trained researchers.  
Withdrawal of Subjects  
Subjects can leave the study at any time for any reason if they wish to do so without any consequences. 
They can also request study coordinators to have their personal data permanently deleted from our 
databases. 
 
REFERENCES 
 
1. Chalmers RA, Bain MD, Michelakakis H, Zschocke J, Iles RA. Diagnosis and management of 
trimethylaminuria (FMO3 deficiency) in children. Journal of inherited metabolic disease. 2006 Feb 
1;29(1):162-72. 
2. Wise PM, Eades J, Tjoa S, Fennessey PV, Preti G. Individuals reporting idiopathic malodor 
production: demographics and incidence of trimethylaminuria. The American journal of 
medicine. 2011 Nov 1;124(11):1058-63. 
3. Pasikanti KK, Ho PC, Chan ECY (2008) Development and validation of a gas 
chromatography/mass spectrometry metabonomic platform for the global 
profiling of urinary metabolites. Rapid Commun Mass Sp 22: 2984–2992. 

Irene Gabashvili NCT02683876 
4. Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, Bjorndahl TC, Krishnamurthy R, 
Saleem F, Liu P, Dame ZT, Poelzer J., Huynh J, Yallou FS, Psychogios N, Dong E, Bogumil R, 
Roehring C, Wishart DS. The human urine metabolome. PloS one. 2013 Sep 4;8(9):e73076. 
PMID: 24023812  
5. Wishart DS, et al. HMDB: A knowledgebase for the human metabolome. Nucleic Acids 
Research. 2009;37:D603–D610  
6. Gabashvili IS. Why Red Beans and Rice Are Good ... But Not with Coffee 2012, Forbes 
7. Scalbert, A., Brennan, L., Fiehn, O., Hankemeier, T., Kristal, B.S., van Ommen, B., Pujos-Guillot, 
E., Verheij, E., Wishart, D. and Wopereis, S., 2009. Mass-spectrometry-based metabolomics: 
limitations and recommendations for future progress with particular focus on nutrition research. 
Metabolomics, 5(4), p.435.