Fecal Microbiota Transplant From Healthy Lean Donors to Morbidly Obese Individuals: Effect on Insulin Resistance and Other Obesity-related Parameters. A Randomized Controlled Trial.
Phase: PHASE2
Conditions: Obesity, Morbid, Insulin Resistance
Sponsor: Johane Allard

 
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Fecal microbiota transplant from healthy lean 
donors to morbidly obese individuals: effect on 
insulin resistance and other obesity-related 
parameters. A randomized controlled trial. 
 
 
NCT02970877 
Date: 10-May-2020 
 

 
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Title: Fecal microbiota transplant from healthy lean donors to morbidly obese individuals: effect on 
insulin resistance and other obesity-related parameters. A randomized controlled trial.  
 
Protocol Number: 
 
16-5475-A 
Date of Protocol: 
 
10 May 2020 
 
University Health Network 
Principal Investigator/Sponsor: Johane Allard, MD, FRCPC 
 
Toronto General Hospital 
 
585 University Ave, 9-NU-973 
 
Toronto, Ontario  M5G 2N2 
 
Canada 
 
Phone: 416-340-5159 
 
Fax: 416-348-0065 
 
 
Study Product: 
Fecal filtrate prepared from feces of healthy lean pre-screened donors 
 
Study Start Date: 
01 March 2017 
 
Prepared By: 
Bianca Arendt, PhD, CCRP 
Amended by: 
Katherine Schwenger, BASc, M.A.N., RD, PhD 
 
This protocol has been reviewed and approved by the Principal Investigator: 
 
 
________  ________________ 10 May 2020__ 
Johane Allard  
 
 
Date 
 
 
This study will be conducted in accordance with the protocol, 
Good Clinical Practice and any other applicable regulatory requirements, including the 
archiving of essential documents. 
 
 

 
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1 MATERIAL AND METHODS 
1.1 Study Design 
This will be a double-blind, placebo controlled randomized controlled trial. Morbidly obese men and 
women with IR who are eligible for bariatric surgery but did not perform such procedure will be 
approached. Those consenting will be randomly assigned to either allogenic FMT (prepared from stool 
donated by healthy lean donors with BMI <23 kg/m2, matched for gender with the recipient) or 
autologous FMT (re-infusion of own collected feces; placebo). We plan to randomize 30 patients. 
Stool, blood, anthropometric measurements, questionnaires, quality of life, depression and anxiety will 
be assessed and will be collected before FMT and at 1 and 3 months post FMT. At baseline a physical 
examination will be performed, and will also be preformed if symptoms are present 3-months post-
FMT . OM will be assessed before FMT and 3-months post FMT. Intestinal mucosa biopsies will be 
collected during colonoscopy for FMT administration as well as samples for safety. 
1.2 Outcomes 
Main outcome: Insulin resistance (HOMA-IR) 
Secondary outcomes: weight (absolute number, BMI, % change), appetite score, quality of life, anxiety 
and depression scores 
Other measurements: HbA1c, C-peptide, IM in stools (composition and metagenome), amount of 
selected groups of microorganisms measured by qPCR, blood lipids, gut hormones, blood pressure, 
physical examination, food intake (energy and % of energy from fat, carbohydrates, protein, fiber 
intake (g/d)), physical activity (units/day), environmental questionnaire, stool and serum metabolomics. 
.  
1.3 Study Participants 
1.3.1 Patients receiving intervention 
Inclusion criteria: Morbidly obese men and women, age 18 years or older, fulfilling the 1991 NIH 
criteria (BMI >40 kg/m2 or BMI >35–40 kg/m2 with other severe weight loss responsive comorbidities) 
(12, 36), who were referred to the Bariatric Clinic at the Toronto Western Hospital for weight loss 
surgery, but declining or deferring the surgery, will be included. Will accept also patients fulfilling 
same criteria above who are approaching us via other streams like MTOP website, cinicaltrials.gov and 
flyers posted in Hospitals and Universities. Patients have to be insulin resistant, which is defined as 
having a HOMA-IR value >2.73 (129).  
Exclusion criteria: Regular intake of: non-steroidal anti-inflammatory drugs; immune-suppressed 
subjects for any reason (due to diseases or medications like steroids and other immune-suppressant 
agents); iron supplements; prebiotics or probiotics from other than food sources, antibiotics, or any 
experimental drug in the 3 months prior to study entry; type 1 or type 2 diabetes, severe chronic 
gastrointestinal diseases, previous gastrointestinal surgery modifying the anatomy, smoking; pregnancy 
or breastfeeding.  
 
Participant withdrawal criteria:  
All participants:  
Any participant may discontinue from the study at any time:  
• At the discretion of the investigator  
• At the request of the participant (drop-out)  

 
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The reason for discontinuation will be clearly documented in the relevant Case Report Form 
(CRF).  
 
The following will result in withdrawal of a participant from the study:  
• Significant non-compliance with the protocol, based on the investigator’s judgment following 
discussion with the study team and/or a co-investigator  
• Any other situation where, in the opinion of the investigator, continued participation would NOT 
be in the best interest of the participant.  
 
In the event that the participant discontinues the study due to a serious adverse event (SAE), a follow-up 
contact will be made. This contact will be documented in the source document.  
If a participant fails to appear for a follow-up examination, up to three documented phone calls and/or 
registered mail will be used to locate and recall the participant, if possible. 
Specific criteria for FMT recipients: Only one single dose of study product will be administered. Patients 
will be withdrawn from the study, if the FMT cannot be administered for any reasons. Data of these 
patients will not be included in the study, except for comparison with the patients who did receive the 
FMT. These patients will be replaced through additionally randomized patients. There will be no follow-
up for patients who did not receive FMT. 
 
1.3.2 Healthy lean stool donors 
Inclusion criteria: Men or women 18–45 years old, BMI 18.5-23kg/m2, not related to or sharing the 
household with any of the recipients. Donors will be matched by sex with the FMT recipients.  
Exclusion criteria:  
The following exclusion criteria are standard at the University Health Network Transplant program for 
all donors: 
 
● 
History or symptoms suggestive of underlying gastrointestinal disease, such as: 
○ Inflammatory bowel disease 
○ Gastrointestinal motility disorder 
○ Diverticular disease 
○ Other chronic symptoms of diarrhea NYD 
○ Episode of acute diarrhea in the last 3 months 
○ Irritable bowel syndrome 
○ Chronic constipation 
● 
Infection or colonization with transmissible agents, including: 
○ HIV-1/2 
○ Hepatitis A, B and C viruses 
○ HTLV-1/2 
○ Syphilis  
○ VRE, MRSA, Carbapenemase-producing Organisms (CPO), Extended spectrum beta-
lactamase-producing (ESBL) organisms  
○ Clostridium difficile  
○ Salmonella, Shigella, Enterotoxigenic E. Coli, E.coli 0157-H7 and other Shiga-toxin 
producing E. Coli, Yersinia, Campylobacter, Aeromonas, Plesiomonas, and Vibrio  
○ Enteric viruses (norovirus, rotavirus and adenovirus)  
○ Ova and parasites  
○ Helicobacter pylori  

 
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○ Gonorrhea  
○ Chlamydia  
● 
History of active malignancy, or any cancer within the last 5 years (excluding basal cell 
carcinoma of skin).  
● 
Risk factors for prion-related disease, including: family history of Creutzfeld-Jacob 
Disease, corneal or dural transplant, or receipt of human-derived pituitary growth factor.  
● 
History of high risk for recent acquisition of HIV, Hepatitis B or Hepatitis C, as determined 
by self-screening using a questionnaire of risk-associated behavior  
● 
History or signs of chronic sexually transmitted infections, such as genital ulcerative 
diseases, anogenital herpes, anogenital warts, cancroid or syphilitic lesions.  
● 
Immunosuppression, as determined by history or suggestive physical signs. Items on 
history that indicate immunosuppression include: 
o   use of any immunosuppressive medication within last 6 months, including any dose of 
prednisone 
o   chronic underlying condition known to produce immunosuppression (e.g. systemic 
lupus erythematosus, end-stage renal disease) 
● 
Physical signs suggestive of immunosuppression include: 
o   oral thrush 
o   lesions of Kaposi sarcoma 
o   disseminated lymphadenopathy 
● 
History or physical findings of chronic liver disease or cholestasis  
● 
Evidence of active encephalitis or meningitis  
● 
Evidence of active systemic viral, bacterial or fungal infection, including malaria and 
tuberculosis  
● 
Receipt of live vaccine in preceding 30 days  
● 
Receipt of blood transfusion from a country other than Canada in preceding 6 months  
● 
History of dementia or degenerative neurological disorders of unknown etiology 
● 
Recent bite from an animal that may have rabies within the past 6 months 
● 
Antibiotic use in the 6 months preceding donation  
● 
Probiotic agent use for medicinal purposes in the 3 months prior to donation  
● 
Use of cholestyramine within 3 months of donation  
● 
Known and current history of blood in stools 
● 
Travel outside Canada/United States in the past 6 months  
● 
Health care workers  
● 
Persons who have recently been hospitalized or discharged from long term care facilitites  
● 
Persons who regularly attened outpatient medical or surgical clinics  
● 
Persons who have recently engaged in medical tourism  
● 
Use of any prescription or nonprescription medications, supplements, vitamins, herbal 
remedies on a regular basis  
● 
History of COVID-19 in last 3 months 
● 
Symptoms suggestive of COVID-19 or close contact with known or suspected COVID-19 
infected person or travel history of any country known to be affected by COVID-19 in last 
one month 
 
Additional exclusion criteria that apply to all donors for repeat FMT and the donors in our study: 
 
● 
History, family history in first degree (blood) relatives, or current screening symptoms (as 
determined by positive MINI, Ham-A or MADRS) of psychiatric illness (including 
depression, anxiety disorder, post-partum depression, bipolar disorder, schizophrenia) 

 
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● 
History or family history of autoimmune disease in first degree (blood) relatives 
● 
Family history in first degree (blood) relatives of colorectal cancer 
● 
Anaphylactic food and environmental allergies 
● 
Overweight (BMI ≥25 kg/m2) at any time during lifetime 
● 
Actual BMI lower than 18.5 or higher than 23 kg/m2  
● 
Waist circumference > 80 cm for women, > 102 cm for men 
● 
Diabetes or “pre-diabetes”, defined as HbA1c > 6% according to FMT screening protocol, 
or HOMA-IR more than 2.73 (129) specifically for this study 
● 
Smoker (cigarette, marijuana, other) 
● 
Use of recreational drugs 
● 
Greater than 2 alcoholic beverages per day on a regular basis 
 
1.4 Intervention 
Morbidly obese participants will receive one single dose of fecal filtrate prepared from feces of healthy 
lean pre-screened donors (allogenic FMT) or prepared from their own feces (autologous FMT) per 
colonoscopy. Patients will be followed for 3 months post FMT. Patients will be allocated to allogenic 
or autologous FMT by randomization. As diet can influence the IM composition (131, 132), all patients 
will receive a brief initial counselling on a healthy diet, where they will be provided with a sheet listing 
the most important rules for a healthy diet, a copy of Canada’s food guide, and an information sheet on 
a low glycemic index diet from the Canadian Diabetes Association. These are standard tools that Dr. 
Allard is using in her clinic for patients with non-alcoholic fatty liver disease at the University Health 
Network.  
 
1.4.1 Stool collection for FMT 
Patients and donors will receive a stool collection kit including a collection bowl with a lid, an 
insulated bag and cooling elements. They will collect one full stool and keep it in the home refrigerator 
(4°C) for 24 hours (maximum 48 hours) before bringing it to the hospital in the insulated bag with the 
cooling elements (see instructions).  
 
1.4.2 Preparation of fecal filtrate for FMT  
Allogenic and autologous FMT will be produced the same way, using either healthy lean donor stool or 
the recipient’s own stool, respectively.  
FMT manufacturing 
FMT manufacturing will follow actual methodologies used in Dr. Poutanen’s laboratory, based on their 
previous randomized controlled trial (NCT01226992).  
 
FMT delivery 
FMT by colonoscopy will be administered on one occasion using FMT derived from 150 g stool.  
• Three 50 g frozen FMT, each in 30 mL conical tubes, from the same donor (i.e. a total of 150 g 
frozen FMT) will be thawed at 2-5°C over 24-48 hours in order to best maintain the integrity of 
microorganisms.  
• Each thawed FMT will be diluted with sterile 0.9N NaCL to make up a total volume of 100 mL 
per “50g dose” and place into transport container (total volume 300 mL) 
• The diluted filtrate will be transported at room temperature to the endoscopy suite in a biosafety 
specimen transportation bag. 

 
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• Once received by the gastroenterology team the filtrate will be administered within 9 hours 
throughout the colon using a colonoscope. 
• Store 2 aliquots of the filtrate in 2ml vials, labelled with the same unique donor identification 
number, and frozen in the –80°C freezer in the event that further testing is required in the 
future.  
• The colonoscopy procedure and post-procedure observation will otherwise follow standard 
medical procedures. The FMT by colonoscopy will be administered by a trained 
gastroenterologist. 
 
The amount of 150 g stool suspended in 300 mL reflects the range of acceptable stool mass and volume 
in published literature (102). Freezing the stool up to 6 months (increasing to 12 months as soon as data 
from Dr. Poutanen lab will become available) in Normal Saline containing glycerol as a cryopreservant 
has been shown not to affect the efficacy against C. difficile or the viability of representative organisms 
(133). 
Patients who are allocated to the allogenic FMT still provide a stool sample (as this is a double-blind 
study). The laboratory technician who is allocating the groups based on the randomization list will 
produce an autologous FMT only for those patients who are in this arm. The FMT products for 
administration will be labelled only with the study ID of the recipient, in order to keep the double-blind 
design. The aliquots that are kept from the lean healthy donors for future testing are labelled with the 
donor’s study ID. Allogenic FMT will be delivered only after the corresponding donor has passed the 
second safety screening (same sample as the first one: stools, urine and blood). 
1.4.3 Administration of FMT by colonoscopy 
For FMT, subjects undergo standard preparation for colonoscopy (colonoscopy preparation attached), 
starting with adjustment of diet 5-7 days prior to colonoscopy and clear fluids only 2 days before the 
procedure. Two days before the procedure, patients have to take 2 tablets of Dulcolax at 5:00pm. The 
evening prior to colonoscopy, 4 L polyethylene glycol laxative solution have to be taken to complete 
bowel cleansing. Colonoscopy will be performed according to medical and safety standards at the 
University Health Network. Once received by the gastroenterology team the filtrate will be dispensed 
into six 60ml syringes. Then the fecal filtrate will be administered while the colonoscope is slowly 
removed. No FMT will be administered in the ileum, to avoid potential infection due to the bacteria 
from the filtrate to reach the bloodstream through the small solution of continuity due to the biopsy in 
the ileum. The whole amount (300ml) of filtrate will be administered in the cecum/ascending colon at 
an infusion rate of about 50ml/min. At the time of infusion, the patients will be in his left lateral 
recumbence position and will be asked to maintain a right lateral position for at least 1 hour after the 
procedure to facilitate as much as possible the permanence of the material infused into the proximal 
portions of the colon.  
1.4.4 Rationale for dose and route of administration selected 
Even though FMT is increasingly used in the treatment of C. difficile infection and other 
gastrointestinal diseases in clinical practice and research (102, 103), there are currently no standards for 
the collection of donor feces and preparation of the fecal filtrate. Usually, the donor specimen is 
homogenized and filtered and either administered directly or frozen and used later (102, 136). 
Gastrointestinal tubes, colonoscopy, enema, and recently even oral capsules are used to administer 
FMT (102). In the present trial we will freeze the filtrate before administration via colonoscopy.   
We are preparing the fecal filtrate using standard methods developed at Mount Sinai Hospital, which 

 
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have been used in a previous Health Canada approved trial on FMT for C. difficile infection 
(NCT01226992, clinicaltrials.gov). The amount of filtrate to be administered was changed from 50 g 
feces in 500 mL saline to 150 g in 300 mL, as this stool volume is now the standard amount used in our 
own FMT program at Mount Sinai Hospital. Colonoscopy is a safe way to administer FMT (102). This 
route was selected, as it allows us to reach more proximal regions of the gut compared to enema. We 
decided against small intestinal infusion used by Vrieze for their metabolic syndrome study (26), as we 
are treating morbidly obese individuals that may have large BMI ( >40 kg/m2) and we are concerned 
with the higher prevalence of gastroesophageal reflux in this population (137), which may increase the 
risk of aspiration. 
Due to the control arm receiving autologous FMT, we are using frozen fecal filtrate. Patients need to 
prepare for FMT colonoscopy following standard instructions with clear fluids and a laxative the day 
before the procedure to clean the colon (see colonoscopy preparation instructions). The stool for FMT 
has to be collected before the adjustment of diet and bowel preparation in order to maintain the original 
IM (stool need to be collected no later than 3 to 4 days prior to colonoscopy). The use of an autologous 
FMT control group is essential, as the bowel preparation and colonoscopy itself can induce changes to 
the IM (134, 135). Lean donor stool will be processed the same way as the autologous stool. Freezing 
stool by adding glycerol as a cryoprotectant has been shown to yield active FMT preparations (136, 
138). Costello and colleagues (133) examined the long-term stability of frozen stools for FMT and 
confirmed that fecal filtrate for treatment of C. difficile infection could safely be stored frozen in 10% 
glycerol for at least 6 months without loss of clinical efficacy or viability in the six bacterial groups 
tested. In our study, we will store fecal filtrate for a maximum of 3 months before using it for FMT. 
We will follow patients for 3 months post FMT. Vrieze et al. found improvements in IR already 6 
weeks after FMT, without weight loss (26). We will extend the observation period, as 6 weeks might 
not have been sufficient to cause significant weight loss. In addition, we want to see, whether FMT 
leads to sustainable improvements in IR and IM. Patients will be approached by the MTOP Team and 
will be offered a 3 years follow-up post-FMT (REB# 16-5404-AE, separate consent form).    
 
1.5 Time line of assessments and measurement 
Healthy donors: After successful pre-screening over the phone, the potential donors will receive a copy 
of the consent form by mail or email. This will be discussed with the study coordinator over the phone. 
If the candidates agrees to participate, they will come to the hospital for a first visit, where screening 
questionnaires will be administered and screening blood work will be taken. In addition, the MINI semi-
structured interview (130), Ham-A, MADRS and CR-RISC questionnaires will be administered. In order 
to perform an infectious disease screening, also stools and urine will be collected. If the donors pass the 
screening, they will be provided with a stool collection kit and forms for further assessments: food record, 
activity log, and environmental questionnaire. The donors will complete these forms during the week 
preceding their first stool donation. Three to five stool donations per donor will be collected within 2-4 
weeks of successful screening (one donation is equal to 150gr of stools). These samples will be used for 
FMT. In addition, IM and metabolomics will be measured on one additional sample provided on first 
donation and frozen right after collection. Between each donation, donors have to fill-in a risk self-
assessment and a diet screening form. Final screening will be performed two weeks after last donation, 
to ensure maximum safety of the samples collected. 
For MTOP donors, bookend testing will also be done for AMDROs (MRSA, VRE, ESBL and CPO) 
occurs at a minimum of two-month intervals. Rescreening for other infectious diseases (stool, urine and 
blood) occurs at least every 6-months. FMT donations provided up to 2 weeks preceding re-screening 
are released only if re-screening is negative for AROs and all other screened infectious diseases. In 
addition, while donating stool, FMT donors complete self-screening questionnaires with every donation 

 
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for ongoing assessment for their medical health and risk of acquiring infectious diseases. If risk for 
infection is determined to be present on the questionnaire (e.g. travel to an endemic area for infectious 
disease), the donor is placed in a washout period prior to being rescreened to re-donate. Diabetes risk 
(HOMA-IR score), anthropometric measurements and mental health questionnaires are repeated at least 
every 12 months.  
Morbidly obese patients: Patients will be approached if they have attended the information session and 
signed the psychosocial study consent where they agree to be contacted for future research studies. We 
will be targeting specifically those patients who decide not to go ahead with the bariatric surgery at this 
point in time (i.e. no follow-up arranged four weeks after the orientation session). Patients interested in 
our study calling us on their own initiative will be screened even if not previously evaluated by the 
Bariatric Clinic at TWH as long as they fulfil all other requirements. After a short phone screening, the 
patients receive a copy of the consent form, which will be discussed over the phone by the study 
coordinator. Then they will come to the hospital for the screening visit, where their medical history and 
medications are recorded and HOMA-IR will be measured to assess eligibility. Patients will be provided 
with forms for food records, activity log, environmental questionnaire, and a stool collection kit. If the 
patient lives far or specifically request it, he can have blood works done in an outside lab and kit will be 
mailed home. In this case medical history and medications are recorded over the phone. Once eligibility 
is confirmed depending on HOMA-IR measured at screening, patients will be called back for the baseline 
visit. The following will be assessed: bloodwork, anthropometric measurements, appetite rating, 
psychological MINI interview, depression, anxiety, and quality of life questionnaires. Then, an FMT 
appointment will be scheduled and patients will be randomized. Patients will collect stool samples 
adequate for a single transplant (150gr) before they start the bowel preparation for the colonoscopy. In 
the week before the stool sample is collected, they will complete the questionnaires provided at baseline. 
When they bring their stool sample to the hospital, completed forms will be collected, anthropometric 
measurements will be performed. Baseline measurements will be repeated at 1 and 3 months post FMT, 
except for quality of life, depression and anxiety scores.. After FMT patients are required to fill-in a 
symptoms diary to keep track of any AE possibly occurring. In addition, during the colonoscopy 
performed to administer FMT, mucosal samples will be taken from the rectum, the colon, and the 
terminal ileum to assess the mucosa-associated microbiome. Blood and colon secretions will be collected 
to evaluate the baseline immune status of the subject (infections existing prior to FMT) and colon 
secretions will be also stored for future research. 
 
1.6 Methods  
5.6.1 Stool Measurements 
Sample collection and processing: Participants receive a stool collection kit, including a plastic 
collection/storage container, insulated bag, and cooling elements. Patients will collect one stool sample 
within 24 h before the study appointment (donors) or prior to the 2-day preparation for the colonoscopy 
(patients). Stools will be placed in the participant’s home refrigerator (+4°C) immediately after 
defecation. Within 24 h (up to 48 h acceptable) the stool will be brought to hospital in the insulated bag 
with cooling elements, where the stool will be transferred into sterile sample bags and homogenized a 
stomacher. In the autologous FMT group and in the donors, 150 g of the stool will be processed for 
FMT as described above. Another stool sample will be collected within 72 hours prior to colonoscopy 
preparation and stored in patient’s freezer. The sample will be stored in our -80°C freezer and then 
homogenized and separated. Two tubes containing 20 mg each will be stored for stool metabolomics. 
Three tubes of about 0.1 g stool will be stored for IM measurement, and the rest will be aliquoted in 10-
20 mL tubes and stored for potential future analysis. All samples, except for FMT filtrate, will be 
stored at -80°C until analysis.   

 
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Intestinal microbiome (IM): DNA extraction: DNA will be extracted from samples using the PowerSoil® 
DNA Isolation Kit aiming for 100 mg of sample for stool and the entire sample for plaque (MO BIO 
Laboratories, Inc., Carlsbad, CA). The extraction protocol will be modified to include a lysozyme 
digestion step (incubation at 37°C for 30 minutes) (139). DNA concentration and purity will be measured 
using a Nanodrop Spectrophotometer (Thermo Scientific, Rockford, IL). Extracted DNA will be stored 
at -20°C until analysis.  
Amplification and sequencing: Samples will be processed according to our standard operating procedures 
and existing pipeline (https://github.com/ggloor/miseq_bin). The workflow will be frozen as a release at 
the outset of the analysis so that all analyses are conducted using the same protocol and software versions. 
The current protocol is in brief as follows. DNA samples are amplified with barcoded primers specific 
for the V4 rRNA gene variable region using the Earth Microbiome Primers modified to have 
combinatorial inline barcodes (140). Post-polymerase chain reaction (PCR) samples are pooled in 
equimolar amounts, merged into a single library, and paired-end sequenced on an Illumina MiSeq using 
the version 3 chemistry. Samples are overlapped, chimeric sequences removed, and operational 
taxonomic units (OTUs) determined by the USEARCH set of integrated tools (141). These are 
instantiated in our existing pipeline. As a complementary approach, we will use DADA2, which 
generates unique sequences corresponding to strain level variation in the input samples (142). In either 
case, singleton sequences and OTUs that are less than 0.1% abundant in any sample will be discarded, 
and tables result that contain the number of reads mapping to a particular taxonomic group in each 
sample.  
Statistical analyses for IM will be done using a compositional data analysis paradigm to acknowledge 
the distortion of the data that arises due to high throughput sequencing (143-148). This approach has the 
advantage of simultaneously adjusting for read depth differences between samples (144, 148). 
Exploratory data analysis will be done using compositional biplots (149, 150) that simultaneously display 
in a semi-quantitative manner the variation in the samples and in the taxonomic groups using an approach 
similar to that described by Gloor and colleagues (151, 152). Data will be examined and adjusted for any 
batch effects using an approach taken for microarray and RNA-seq analyses (153).   
Differential abundance between groups will be determined using the ALDEx2 package using either the 
pairwise or glm methods that accounts for both the distortion in the data and the uncertainty of 
measurement inherent in these samples (144, 151). We will use an effect-size approach (154), rather than 
use p-values for significance testing, since effect sizes are known to be more reproducible and predictive 
in multivariate datasets (155). Since collection of whole metagenome sequence or whole meta-
transcriptomes would be prohibitively expensive for the entire number of samples, we propose to use 
16S rRNA gene profiles to impute metagenome function and pathway abundance tables using the 
PICRUSt tool (156), and further downstream analyses of function using a compositional approach as 
done previously (157).  
The internal correlation structure of the microbial communities will be examined using the SPIEC-EASI 
tool that assumes a sparse and highly compositionally-correlated  structure (147). Should the sparsity 
assumption not be met, we will use the phi-statistic developed by Lovell (148) which is useful for non-
sparse compositional data.  
Functional microbiome analysis: We will determine the full microbial DNA sequence of the subset of 6 
patients at 0 and 3 months, using paired-end sequencing on the Illumina HiSeq. This will serve as internal 
control for the PICRUSt imputed data and allow us to uncover novel functions that are not represented 
in existing sequenced genomes. We aim to recover 75 million bacterial reads per sample, and assuming 
25% human contamination after depleting methylated DNA, we anticipate multiplexing 2 samples per 
HiSeq lane. Reads will be assessed for microbiome composition using MetaPhlAn2 (158) and a library 

 
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encompassing unique genes for the dataset will be produces and processed as for a recent meta-
transcriptomic study of vaginal microbial community (157). Reads that are not assigned with this pipeline 
will be assembled using the Trinity assembly pipeline and assembled fragments will be compared to 
existing genes to determine if there are any novel functions (159). We have a dedicated 64 core computer 
with 128 GB of RAM available through SharcNet for this task. Differential abundance of functions and 
pathways will be examined using the ALDEx2 tool (145, 160) and LEfSe (161) for the imputed data. 
Metabolomics analysis: Fecal samples will be collected and stored at -80°C until metabolomics 
analysis via nuclear magnetic resonance (NMR) spectrometry. Fecal extracts will be prepared by 
mixing 20 mg of frozen fecal material with 1 mL of saline phosphate buffer in D2O (deuterium oxide), 
followed by centrifugation (18,000×g, 1 min). Fecal supernatants will be removed and filtered through 
0.2 μm membrane filters as described by Le Gall and colleagues (163). The samples will be transferred 
to a standard NMR tube for 1H-NMR spectral analysis on a 500 MHz Inova spectrometer. Spectral 
fitting for metabolites will be done using the standard Chenomx 500 MHz metabolite library. Typically 
90% of visible peaks are assigned to a compound and more than 90% of the spectral area can be 
routinely fit using the Chenomx spectral analysis software. These methods have been perfected at the 
Metabolomic Innovation Centre (164). As for the full microbial DNA sequencing, this measurement 
will only be performed, if additional funding can be acquired.  
 
1.6.3 Bloodwork  
Sample collection and processing: Blood will be drawn in the morning after 12 hours fasting, based on 
the standard procedures at the University Health Network Laboratory Medicine Program. As insulin 
secretion is pulsatile, three samples will be taken at 5-min intervals, and the average of the three 
measurements will be calculated (165). Serum for metabolomics measurements will be collected into 
tubes without anticoagulant, placed on ice immediately, and centrifuged at 4°C, 2,800×g for 15 min. 
Serum will be aliquoted and frozen at -80°C until analysis. Samples for TNF-α, IL-6, and vitamin E 
will be collected in EDTA containing tubes, separated by centrifugation (910 × g, 10 min) and stored at  
-80°C until analysis. The same will be done for plasma endotoxin, but blood will be collected in 
pyrogen free heparin tubes (Monovette, Sarstedt, Nuernbrecht, Germany). Samples for hormone 
analysis will be collected in tubes containing protease inhibitors and other added chemicals described 
below, then separated by centrifugation (910 x g, 10 min) and stored at  -80°C until analysis. 
Analyses of standard blood biochemistry: The following standard tests will be performed at the 
Laboratory Medicine Program at the University Health Network: 
Glucose: enzymatic hexokinase method (Bayer Advia 1650 Glucose package) 
Insulin: microparticle enzyme immunoassay (MEIA) (Abbott Architect i2000) 
Hemoglobin A1c: High-performance liquid chromatography (Variant)  
C-peptide: chemiluminometric technology (Immulite 2000) 
Plasma lipids: Triglycerides, total and high density lipoprotein (HDL) cholesterol are measured using 
the Abbot Architect. Low density lipoprotein (LDL) cholesterol is calculated as total cholesterol – 
HDL cholesterol.  
Homeostasis model of assessment (HOMA):  
Insulin Resistance HOMA-IR = glucose [mmol/L] x insulin [mU/L] / 22.5 (166, 167). Patients with 
insulin resistance (HOMA-IR >2.73) (129) and healthy donors with HOMA-IR ≤2.73 are eligible. 

 
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Serum metabolomics: Serum will be thawed and ultra-filtered for removal of large molecular weight 
proteins and lipoproteins. The metabolomics analysis by nuclear magnetic resonance spectroscopy is 
performed as described above for stool samples.   
 
1.6.4 Nutritional and other assessment  
Anthropometry: Weight, BMI, waist circumference and waist to hip ratio are measured. Change in 
these parameters and excess weight loss (in %) between baseline and 1 month as well as between 
baseline and 3 months post FMT will also be assessed.  
Food intake: Three-day food records will be kept. Participants will eat their regular meals and itemize 
the food using the 2D Food Portion Visual chart (Nutrition Consulting Enterprises, Framingham, MA) 
to estimate portion sizes. The data will be analyzed using Food Processor SQL (ESHA Research) to 
assess the intake of macro- and micronutrients (168, 169) (food record form enclosed). 
Physical activity: Patients will record their physical activity (daily life activities and sports exercises) 
for seven days, including time, type and duration and intensity of these activities. Units of exercise will 
be calculated (1 unit = 30 min mild, 20 min moderate, 10 min strenuous, or 5 min very strenuous 
activity) (170) (food/activity forms enclosed).   
Appetite will be assessed in a fasting state, using a validated tool in obese subjects (172, 173). Research 
participants will be asked to rate their level for each feeling on a 100 mm visual analogue scale, 
therefore participants will have a numerical ranking for each feeling in mm which can be used for 
statistical analysis (see attached form). 
Blood pressure: will be measured in a seated position according to clinical practice.  
 
1.6.5 Psychological assessment 
At baseline to all participants and 1 and 3 months after FMT for patients, we will also administer: The 
Montgomery-Åsberg Depression Rating Scale (MADRS), a 10-item questionnaire which psychiatrists 
use to measure the severity of depressive episodes in patients with mood disorders (174); and The 
Hamilton Anxiety Rating Scale (Ham-A), a clinician-rated evaluation whose purpose is to analyze the 
severity of anxiety. The scale is intended for adults, adolescents, and children and should take 
approximately ten to fifteen minutes to administer (175). Both questionnaires will be administered by 
the study coordinator. 
To identify whether there are particular personality styles that make someone a good donor, we will 
also administer to the donors at baseline: the Connor-Davidson Resilience scale (CD-RISC), which 
comprises of 25 items, each rated on a 5-point scale (0-4), with higher scores reflecting greater 
resilience (176). 
Quality of life: This will be assessed using the RAND 36-Item Health Survey 1.0 (RAND SF-36), 
which has been developed for the Medical Outcomes Study (177). Patients will fill out the form at 
baseline and 3 months after FMT (all psychiatric forms enclosed). 
 
 
1.7 Statistical Considerations  
5.7.1 Sample size and power calculations 
Our sample size is determined based on current literature, feasibility and the experience of team 
members, due to the pilot nature of the proposed study. Following common practice for this kind of 
studies (e.g. (179, 180)), we plan to recruit until we have randomized 36 eligible patients (18 per FMT 

 
Protocol FMT, Version 10 May 2020 
 
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group). With a 20% attrition rate we assume that 15 patients per group will finish the intervention. This 
represents a greater sample size than the only previous FMT study on metabolic syndrome (26). We are 
confident that this sample size will allow us to investigate the stated aims.  
5.7.2 Statistical analysis plans  
A descriptive summary of all measurements will first be compiled using appropriate graphical 
representations, and means, standard deviations and proportions will be calculated. Baseline 
characteristics of the randomized groups will be compared to ensure balance on the important variables 
(e.g. HOMA, BMI). Trajectory plots will be created to examine visually the changes over time.  
The changes in the outcome variables of (HOMA-IR, weight, scores for appetite, quality of life, anxiety, 
and depression) within the two FTM groups (allogenic vs. autologous) will be compared using 
generalized linear mixed models (GLMM), where the within and between groups changes over time will 
be examined simultaneously. Factors with known influence on the outcomes will be examined for 
possible inclusion in our multivariate models as covariates, particularly if found imbalanced between the 
randomized groups. For patient drop-out, we will check missingness patterns and employ appropriate 
methods of handling missing data (181). The relationships between changes in outcomes and IM will 
first be investigated using correlation analysis (Spearman and Pearson correlation coefficients, Chi-
square and Fischer exact tests as appropriate), and subsequently using methods for correlated data when 
considering multiple measures (incl. covariates, multiple time points, etc.). For the latter, we will employ 
generalized linear and nonlinear mixed models, as well as the generalized estimating equations approach. 
Goodness-of-fit tests will be applied for model diagnostics (182). In addition, principal component 
analysis (183), co-inertia analysis will be considered when investigating possible strategies for dimension 
reduction (where large numbers of variables may be correlated).   
Statistical analyses will be performed using SAS 9.4 and R 3.2 (or subsequent versions as they become 
available), and all tests will be two-sided with a significance level of 5%. Where necessary, p-value 
adjustments for multiple testing (e.g. Holm’s Bonferroni Step-down, Benjamini and Hochberg False 
Discovery Rate) will be employed (184). 
5.7.3 Selection of subjects included in the analysis 
As this is a single-dose treatment, we will only include dosed subjects into the analysis. In the initial 
analysis, all dosed subjects will be included, followed by a per-protocol analysis.  
5.8 Randomization and blinding 
5.8.1 Randomization list and blinding 
Allocation concealment will be maintained by the lab technologist at the UHN-SHS FMT program, 
who will prepare the fecal filtrates for each patient but will otherwise not be involved in the conduct or 
analysis of the study. The allocation list will be kept in sequentially numbered envelopes with the 
group assignments. The group assignments will be based on a blocked randomization list (4 patients 
per block), created in SAS proc PLAN. The list will be created to randomize 60 patients, even though 
36 are planned to be randomized. As we are including only dosed patients in the analysis, this will 
allow us to randomize additional patients, in case the FMT is not conducted in participants after they 
were randomized. The list is created by a person at the UHN, who will not be involved in conduct or 
analysis of the study. The technologist will have to date and sign the opened envelope.  
The technologist will open the allocation on the day the patients at providing their baseline stool 
sample. Only if the patient is allocated to autologous FMT, a fecal filtrate from the patient’s own feces 
is produced. On the day of the FMT, the bags with the fecal filtrate will be provided to the study team 

 
Protocol FMT, Version 10 May 2020 
 
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without indication, which type of FMT is provided. Only the patient number will be on the label. Both 
the patient and the study team (clinical team, study coordinator, data analyst) will be blinded to group 
assignments. 
5.8.2 Unblinding 
The code for a patient will be broken, if a serious adverse event occurs that is likely due to the study 
product, e.g. a new infection of a recipient with a communicable disease, and where the further 
examination of the incident cannot be delayed until the end of the study based on clinical judgement. 
The breaking of the code for one patient will not affect the blinding of the other patients during the 
study. However, it may compromise the blinding during data analysis. In order to maintain the highest 
blinding level possible, only the study physician with the respective expertise (e.g. Susy Hota and 
Susan Poutanen for infections) will be unblinded if required for treatment of the patient. The rest of the 
study team will not be informed.  
 
6 PRODUCT ACCOUNTABILITY 
 
The laboratory technician at Mount Sinai Hospital is responsible for study product accountability, as long 
as the study is blinded. The technician will maintain current, complete, and accurate records accounting 
for all study product produced, use of study product by each subject, and product disposition. Records of 
product dispensed to each subject should include the product lot or batch number, subject number, subject 
initials, amount dispensed, and date. Records of product disposition should include records of destruction 
[rendering product unusable by destroying primary packaging (i.e. packaging in direct contact with 
product)] or return of expired or excess study product. As soon as the study is unblinded, the investigator 
or designee will take over the responsibility for the product accountability.   
 
7 SAFETY EVALUATIONS AND ADVERSE EVENTS 
7.1 Safety Evaluations 
Timely and complete reporting of all adverse events (AE) assists in identifying any untoward medical 
occurrence thereby allowing: 
• 
Continuous safety of study subjects 
• 
A greater understanding of the overall safety profile of FMT. 
• 
Appropriate modification of study protocols 
• 
Improvements in study design or procedures 
• 
Adherence to worldwide regulatory requirements 
In this study, all subjects will be assessed according to their outcomes result (IR, HbA1c, weight, 
appetite). All parameters will be assessed again 1 and 3 months of treatment with FMT. In addition, a 
symptom diary will be kept for the 3 months to cover solicited and unsolicited AE. Monitoring process 
and safety assessments will be done by the study coordinator and the sponsor-investigator. The study 
coordinator will be in direct contact with the patients at each visit. The site investigator will monitor 
the safety data reported by the study coordinator. The bariatric surgery team will be consulted, in case 
their expert opinion is needed. As safety assessment, adverse events will be assessed at each visit. 
Patients may also call the study team at any time in case of concerns.   
7.2 Adverse Events 
During the study, the site investigator is responsible for the detection and documentation of any 
adverse event (AE) or serious adverse event (SAE), as defined in this protocol. 

 
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7.2.1 Adverse Event Definition 
An AE is defined as any untoward medical occurrence in a subject or clinical investigation subject 
administered a pharmaceutical/medicinal product. An AE does not necessarily have to have a causal 
relationship with this treatment. 
An AE can therefore be any unfavorable and unintended sign (including a clinically significant 
abnormal laboratory finding, for example), symptom, or disease temporally associated with the use 
of a medicinal product (investigational or marketed), whether or not considered related to treatment 
with the medicinal product. 
An AE includes: 
• 
An exacerbation of a pre-existing illness, sign, symptom, or clinically significant (as 
determined by the investigator) laboratory test abnormality 
• 
An illness, sign, symptom, or clinically significant laboratory abnormality that is detected or 
diagnosed after study drug administration 
• 
Pretreatment or post treatment events that occur as a result of protocol-mandated 
procedures 
An AE does not include: 
• 
The disease or disorder being studied or signs and symptoms associated with the disease or 
disorder, unless there is worsening of the condition of the disease or disorder 
Although an overdose of the study drug or concomitant medication without any clinical sign or 
symptom is not technically an AE, it should be captured as AE to provide valuable safety data on 
higher untested doses of study drug. 
7.2.2 Procedures for Reporting Adverse Events 
Adverse events may be spontaneously reported, obtained through non-leading questioning of a 
subject. Adverse events will be recorded from the signing of informed consent until the study 
participant exits the study.  
 
Adverse events do not require expedited reporting. They will be documented on the appropriate case 
report form/source document. During the duration of the study, the participants will be directed to 
report any AEs the study team. For more details on documentation and reporting guidelines, see UHN 
REB Unanticipated Problem Reporting Guidance (document enclosed).  
At each visit, new AEs are recorded sequentially. The AE term should note the diagnosis whenever 
possible, not the individual signs or symptoms (e.g., myocardial infarction should be recorded rather 
than chest pain, and elevated cardiac enzymes). Also recorded are: 
• Start and stop date and time 
• Whether or not the event is continuing 
• Frequency (intermittent, continuous) 
• Intensity (mild, moderate, severe) 
o Mild: usually transient, requiring no special treatment and generally not interfering with 
usual daily activities 
o Moderate: usually ameliorated by simple therapeutic maneuvers and impairs usual 
activities 

 
Protocol FMT, Version 10 May 2020 
 
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o Severe: requires vigorous therapeutic intervention and interrupts usual activities. 
Hospitalization may or may not be required. 
• Relationship to study drug (not related, related): identify relationship as “related” if a causal 
relationship between the investigational product and an AE is at least a reasonable possibility 
(i.e., the relationship cannot be ruled out). 
• Whether or not the AE is serious (i.e., an SAE). If identified as an SAE, the AE should be 
reported as described below 
• Actions taken (none, study drug dose interrupted, study drug discontinued, other 
medication change, non-drug therapy).  
• Outcome (resolved, severity or frequency increased, ongoing, and fatal). An individual AE 
receives only one outcome. 
Adverse events not resolved at the end of treatment will be followed until resolution or until the AE 
is judged by the investigator to have stabilized. 
Laboratory values and clinical findings at the scheduled examinations must be reported as AEs 
if they: 
• Are considered clinically significant by the site investigator, 
• Fulfill SAE criteria, and/or 
• Cause subject discontinuation from the study. 
 
7.3 Serious Adverse Events 
An event that is serious must be recorded on the Serious Adverse Event Report Form. An SAE 
requires expeditious handling to comply with regulatory requirements. Serious adverse events will 
be recorded from the signing of informed consent until the participant exits the study. 
 
7.3.1 Serious Adverse Event Definition 
An SAE is defined as an AE that meets any of the following criteria: 
• Death 
• Is life-threatening. A life-threatening AE is any AE that places the subject – in the 
investigator’s opinion – at immediate risk of death from the reaction as it occurred. It does 
not include a reaction that, had it occurred in a more serious form, might have caused 
death. 
• Persistent or significant disability or incapacity 
• Hospitalization or prolongation of existing hospitalization (Hospitalization is defined as 
inpatient hospitalization for no less than 24 hours and which was not planned prior to the start 
of the study.) 
• Congenital anomaly/birth defect 
• Important medical events that may not result in death, be life-threatening, or require 
hospitalization may be considered an SAE when, based upon appropriate medical 
judgment, they may jeopardize the subject or may require medical or surgical intervention 
to prevent one of the other outcomes listed in this definition. Examples of such medical 
events include allergic bronchospasm requiring intensive treatment in an emergency room 

 
Protocol FMT, Version 10 May 2020 
 
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or at home. 
Note: The term “severe” is used to describe the severity of a specific event (e.g., mild, moderate, or 
severe myocardial infarction); the event itself, however, may be of relatively minor medical 
significance (such as a severe headache). This is not to be confused with “seriousness,” which is based 
on the SAE definition that mentioned above. Seriousness (not severity) serves as a guide for defining 
regulatory reporting obligations.  
 
 

 
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REFERENCES 
 
1. Tjepkema M. Adult obesity in canada: Measured height and weight. in Nutrition: Findings from the 
Canadian Community Health Survey. Ottawa: Statistics Canada; 2005. 
2. Shields M, Carroll MD, Ogden CL. Adult obesity prevalence in canada and the united states. in 
NCHS Data Brief. Hyattsville, MD: National Center for Health Statistics. 2011.; 2011. 
3. Courcoulas AP, Christian NJ, Belle SH, Berk PD, Flum DR, Garcia L, Horlick M, Kalarchian MA, 
King WC, Mitchell JE, et al. Weight change and health outcomes at 3 years after bariatric surgery 
among individuals with severe obesity. JAMA 2013;310:2416-25. 
4. Mostaedi R, Lackey DE, Adams SH, Dada SA, Hoda ZA, Ali MR. Prevalence of undiagnosed and 
inadequately treated type 2 diabetes mellitus, hypertension, and dyslipidemia in morbidly obese 
patients who present for bariatric surgery. Obesity Surgery 2014;24:927-35. 
5. Sjostrom L, Peltonen M, Jacobson P, Ahlin S, Andersson-Assarsson J, Anveden A, Bouchard C, 
Carlsson B, Karason K, Lonroth H, et al. Association of bariatric surgery with long-term remission of 
type 2 diabetes and with microvascular and macrovascular complications. JAMA 2014;311:2297-304. 
6. Mummadi RR, Kasturi KS, Chennareddygari S, Sood GK. Effect of bariatric surgery on 
nonalcoholic fatty liver disease: Systematic review and meta-analysis. Clin Gastroenterol Hepatol 
2008;6:1396-402. 
7. Ahrens M, Ammerpohl O, von Schonfels W, Kolarova J, Bens S, Itzel T, Teufel A, Herrmann A, 
Brosch M, Hinrichsen H, et al. DNA methylation analysis in nonalcoholic fatty liver disease suggests 
distinct disease-specific and remodeling signatures after bariatric surgery. Cell Metab 2013;18:296-
302. 
8. Hewitt S, Humerfelt S, Sovik TT, Aasheim ET, Risstad H, Kristinsson J, Mala T. Long-term 
improvements in pulmonary function 5 years after bariatric surgery. Obesity Surgery 2014;24:705-11. 
9. Alizai PH, Akkerman MK, Kaemmer D, Ulmer F, Klink CD, Ernst S, Mathiak K, Neumann UP, 
Perlitz V. Presurgical assessment of bariatric patients with the patient health questionnaire (phq)--a 
screening of the prevalence of psychosocial comorbidity. Health Qual Life Outcomes 2015;13:80. 
10. Dawes AJ, Maggard-Gibbons M, Maher AR, Booth MJ, Miake-Lye I, Beroes JM, Shekelle PG. 
Mental health conditions among patients seeking and undergoing bariatric surgery: A meta-analysis. 
Jama 2016;315:150-63. 
11. Duarte-Guerra LS, Coelho BM, Santo MA, Wang YP. Psychiatric disorders among obese patients 
seeking bariatric surgery: Results of structured clinical interviews. Obes Surg 2015;25:830-7. 
12. NHLBI Obesity Education Initiative Expert Panel on the Identification E, and Treatment of 
Overweight and Obesity in Adults,. The practical guide. Identification, evaluation, and treatment of 
overweight and obesity in adults. U.S. Department of Health and Human Services, Public Health 
Service, National Institutes of Health, and National Heart, Lung, and Blood Institute; 2000. 
13. Pitzul KB, Jackson T, Crawford S, Kwong JC, Sockalingam S, Hawa R, Urbach D, Okrainec A. 
Understanding disposition after referral for bariatric surgery: When and why patients referred do not 
undergo surgery. Obes Surg 2014;24:134-40. 
14. Doo M, Kim Y. Obesity: Interactions of genome and nutrients intake. Prev Nutr Food Sci 
2015;20:1-7. 
15. Bondia-Pons I, Maukonen J, Mattila I, Rissanen A, Saarela M, Kaprio J, Hakkarainen A, Lundbom 
J, Lundbom N, Hyotylainen T, et al. Metabolome and fecal microbiota in monozygotic twin pairs 
discordant for weight: A big mac challenge. Faseb j 2014;28:4169-79. 
16. Zhang H, DiBaise JK, Zuccolo A, Kudrna D, Braidotti M, Yu Y, Parameswaran P, Crowell MD, 
Wing R, Rittmann BE, et al. Human gut microbiota in obesity and after gastric bypass. Proc Natl Acad 
Sci U S A 2009;106:2365-70. 

 
Protocol FMT, Version 10 May 2020 
 
Page 19 of 29 
17. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: Human gut microbes associated 
with obesity. Nature 2006;444:1022-3. 
18. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D, et al. A 
metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012;490:55-60. 
19. Remely M, Aumueller E, Jahn D, Hippe B, Brath H, Haslberger AG. Microbiota and epigenetic 
regulation of inflammatory mediators in type 2 diabetes and obesity. Benef Microbes 2014;5:33-43. 
20. Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, Semenkovich CF, Gordon JI. The gut 
microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A 
2004;101:15718-23. 
21. Ridaura VK, Faith JJ, Rey FE, Cheng J, Duncan AE, Kau AL, Griffin NW, Lombard V, Henrissat 
B, Bain JR, et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. 
Science 2013;341:1241214. 
22. Turnbaugh PJ, Backhed F, Fulton L, Gordon JI. Diet-induced obesity is linked to marked but 
reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 2008;3:213-23. 
23. Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. The effect of diet on the human 
gut microbiome: A metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med 
2009;1:6ra14. 
24. Petrof EO, Khoruts A. From stool transplants to next-generation microbiota therapeutics. 
Gastroenterology 2014;146:1573-82. 
25. van Nood E, Vrieze A, Nieuwdorp M, Fuentes S, Zoetendal EG, de Vos WM, Visser CE, Kuijper 
EJ, Bartelsman JF, Tijssen JG, et al. Duodenal infusion of donor feces for recurrent clostridium 
difficile. N Engl J Med 2013;368:407-15. 
26. Vrieze A, Van Nood E, Holleman F, Salojarvi J, Kootte RS, Bartelsman JF, Dallinga-Thie GM, 
Ackermans MT, Serlie MJ, Oozeer R, et al. Transfer of intestinal microbiota from lean donors 
increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology 2012;143:913-
6.e7. 
27. Bercik P, Denou E, Collins J, Jackson W, Lu J, Jury J, Deng Y, Blennerhassett P, Macri J, McCoy 
KD, et al. The intestinal microbiota affect central levels of brain-derived neurotropic factor and 
behavior in mice. Gastroenterology 2011;141:599-609, .e1-3. 
28. De Palma G, Blennerhassett P, Lu J, Deng Y, Park AJ, Green W, Denou E, Silva MA, Santacruz A, 
Sanz Y, et al. Microbiota and host determinants of behavioural phenotype in maternally separated 
mice. Nat Commun 2015;6:7735. 
29. Li M, Xue X, Shao S, Shao F, Wang W. Cognitive, emotional and neurochemical effects of 
repeated maternal separation in adolescent rats. Brain Res 2013;1518:82-90. 
30. Jiang H, Ling Z, Zhang Y, Mao H, Ma Z, Yin Y, Wang W, Tang W, Tan Z, Shi J, et al. Altered 
fecal microbiota composition in patients with major depressive disorder. Brain Behav Immun 
2015;48:186-94. 
31. Naseribafrouei A, Hestad K, Avershina E, Sekelja M, Linlokken A, Wilson R, Rudi K. Correlation 
between the human fecal microbiota and depression. Neurogastroenterol Motil 2014;26:1155-62. 
32. Zheng P, Zeng B, Zhou C, Liu M, Fang Z, Xu X, Zeng L, Chen J, Fan S, Du X, et al. Gut 
microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host's 
metabolism. Mol Psychiatry 2016. 
33. Collins SM, Kassam Z, Bercik P. The adoptive transfer of behavioral phenotype via the intestinal 
microbiota: Experimental evidence and clinical implications. Curr Opin Microbiol 2013;16:240-5. 
34. Dinan TG, Cryan JF. Mood by microbe: Towards clinical translation. Genome Medicine 2016;8:36. 
35. Arimatsu K, Yamada H, Miyazawa H, Minagawa T, Nakajima M, Ryder MI, Gotoh K, Motooka D, 
Nakamura S, Iida T, et al. Oral pathobiont induces systemic inflammation and metabolic changes 
associated with alteration of gut microbiota. Sci Rep 2014;4:4828. 

 
Protocol FMT, Version 10 May 2020 
 
Page 20 of 29 
36. National Institutes of Health. Gastrointestinal surgery for severe obesity. Nih consens statement 
online  Bethesda, Maryland: US Department of Health and Human Services - National Institutes of 
Health; 1991. pp. 1-20. 
37. Dethlefsen L, McFall-Ngai M, Relman DA. An ecological and evolutionary perspective on human-
microbe mutualism and disease. Nature 2007;449:811-8. 
38. Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, Schlegel ML, Tucker 
TA, Schrenzel MD, Knight R, et al. Evolution of mammals and their gut microbes. Science 
2008;320:1647-51. 
39. Turnbaugh PJ, Gordon JI. An invitation to the marriage of metagenomics and metabolomics. Cell 
2008;134:708-13. 
40. Frank DN, St Amand AL, Feldman RA, Boedeker EC, Harpaz N, Pace NR. Molecular-
phylogenetic characterization of microbial community imbalances in human inflammatory bowel 
diseases. Proc Natl Acad Sci U S A 2007;104:13780-5. 
41. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, Gill SR, Nelson KE, 
Relman DA. Diversity of the human intestinal microbial flora. Science 2005;308:1635-8. 
42. Gosalbes MJ, Durban A, Pignatelli M, Abellan JJ, Jimenez-Hernandez N, Perez-Cobas AE, Latorre 
A, Moya A. Metatranscriptomic approach to analyze the functional human gut microbiota. PLoS One 
2011;6:e17447. 
43. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, 
Bruls T, Batto JM, et al. Enterotypes of the human gut microbiome. Nature 2011;473:174-80. 
44. Faith JJ, Guruge JL, Charbonneau M, Subramanian S, Seedorf H, Goodman AL, Clemente JC, 
Knight R, Heath AC, Leibel RL, et al. The long-term stability of the human gut microbiota. Science 
2013;341:1237439. 
45. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, 
Roe BA, Affourtit JP, et al. A core gut microbiome in obese and lean twins. Nature 2009;457:480-4. 
46. Faith JJ, McNulty NP, Rey FE, Gordon JI. Predicting a human gut microbiota's response to diet in 
gnotobiotic mice. Science 2011;333:101-4. 
47. Duncan SH, Belenguer A, Holtrop G, Johnstone AM, Flint HJ, Lobley GE. Reduced dietary intake 
of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-
producing bacteria in feces. Applied and Environmental Microbiology 2007;73:1073-8. 
48. Payne AN, Chassard C, Lacroix C. Gut microbial adaptation to dietary consumption of fructose, 
artificial sweeteners and sugar alcohols: Implications for host–microbe interactions contributing to 
obesity. Obesity Reviews 2012;13:799-809. 
49. Ma J, Prince AL, Bader D, Hu M, Ganu R, Baquero K, Blundell P, Alan Harris R, Frias AE, Grove 
KL, et al. High-fat maternal diet during pregnancy persistently alters the offspring microbiome in a 
primate model. Nat Commun 2014;5:3889. 
50. Queipo-Ortuno MI, Seoane LM, Murri M, Pardo M, Gomez-Zumaquero JM, Cardona F, Casanueva 
F, Tinahones FJ. Gut microbiota composition in male rat models under different nutritional status and 
physical activity and its association with serum leptin and ghrelin levels. PLoS One 2013;8:e65465. 
51. Mueller S, Saunier K, Hanisch C, Norin E, Alm L, Midtvedt T, Cresci A, Silvi S, Orpianesi C, 
Verdenelli MC, et al. Differences in fecal microbiota in different european study populations in relation 
to age, gender, and country: A cross-sectional study. Appl Environ Microbiol 2006;72:1027-33. 
52. Ohigashi S, Sudo K, Kobayashi D, Takahashi T, Nomoto K, Onodera H. Significant changes in the 
intestinal environment after surgery in patients with colorectal cancer. J Gastrointest Surg 
2013;17:1657-64. 
53. Beamish LA, Osornio-Vargas AR, Wine E. Air pollution: An environmental factor contributing to 
intestinal disease. J Crohns Colitis 2011;5:279-86. 

 
Protocol FMT, Version 10 May 2020 
 
Page 21 of 29 
54. Biedermann L, Zeitz J, Mwinyi J, Sutter-Minder E, Rehman A, Ott SJ, Steurer-Stey C, Frei A, Frei 
P, Scharl M, et al. Smoking cessation induces profound changes in the composition of the intestinal 
microbiota in humans. PLoS One 2013;8:e59260. 
55. Xu J, Gordon JI. Honor thy symbionts. Proc Natl Acad Sci U S A 2003;100:10452-9. 
56. Liolios K, Chen IM, Mavromatis K, Tavernarakis N, Hugenholtz P, Markowitz VM, Kyrpides NC. 
The genomes on line database (gold) in 2009: Status of genomic and metagenomic projects and their 
associated metadata. Nucleic Acids Res 2010;38:D346-54. 
57. Karlsson FH, Tremaroli V, Nookaew I, Bergstrom G, Behre CJ, Fagerberg B, Nielsen J, Backhed 
F. Gut metagenome in european women with normal, impaired and diabetic glucose control. Nature 
2013;498:99-103. 
58. Graessler J, Qin Y, Zhong H, Zhang J, Licinio J, Wong ML, Xu A, Chavakis T, Bornstein AB, 
Ehrhart-Bornstein M, et al. Metagenomic sequencing of the human gut microbiome before and after 
bariatric surgery in obese patients with type 2 diabetes: Correlation with inflammatory and metabolic 
parameters. Pharmacogenomics J 2013;13:514-22. 
59. Tang MS, Poles J, Leung JM, Wolff MJ, Davenport M, Lee SC, Lim YA, Chua KH, Loke P, Cho I. 
Inferred metagenomic comparison of mucosal and fecal microbiota from individuals undergoing 
routine screening colonoscopy reveals similar differences observed during active inflammation. Gut 
Microbes 2015;6:48-56. 
60. Parthasarathy G, Chen J, Chen X, Chia N, O'Connor HM, Wolf PG, Gaskins HR, Bharucha AE. 
Relationship between microbiota of the colonic mucosa vs feces and symptoms, colonic transit, and 
methane production in female patients with chronic constipation. Gastroenterology 2016;150:367-
79.e1. 
61. Bajaj JS, Betrapally NS, Hylemon PB, Heuman DM, Daita K, White MB, Unser A, Thacker LR, 
Sanyal AJ, Kang DJ, et al. Salivary microbiota reflects changes in gut microbiota in cirrhosis with 
hepatic encephalopathy. Hepatology 2015;62:1260-71. 
62. Maynard CL, Elson CO, Hatton RD, Weaver CT. Reciprocal interactions of the intestinal 
microbiota and immune system. Nature 2012;489:231-41. 
63. Armougom F, Henry M, Vialettes B, Raccah D, Raoult D. Monitoring bacterial community of 
human gut microbiota reveals an increase in lactobacillus in obese patients and methanogens in 
anorexic patients. PLoS One 2009;4:e7125. 
64. Schwiertz A, Taras D, Schafer K, Beijer S, Bos NA, Donus C, Hardt PD. Microbiota and scfa in 
lean and overweight healthy subjects. Obesity (Silver Spring) 2009;18:190-5. 
65. Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P, Flint HJ. Human colonic 
microbiota associated with diet, obesity and weight loss. Int J Obes (Lond) 2008;32:1720-4. 
66. Jumpertz R, Le DS, Turnbaugh PJ, Trinidad C, Bogardus C, Gordon JI, Krakoff J. Energy-balance 
studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans. Am 
J Clin Nutr 2011;94:58-65. 
67. Million M, Maraninchi M, Henry M, Armougom F, Richet H, Carrieri P, Valero R, Raccah D, 
Vialettes B, Raoult D. Obesity-associated gut microbiota is enriched in lactobacillus reuteri and 
depleted in bifidobacterium animalis and methanobrevibacter smithii. Int J Obes (Lond) 2012;36:817-
25. 
68. Fernandes J, Su W, Rahat-Rozenbloom S, Wolever TM, Comelli EM. Adiposity, gut microbiota 
and faecal short chain fatty acids are linked in adult humans. Nutr Diabetes 2014;4:e121. 
69. Zupancic ML, Cantarel BL, Liu Z, Drabek EF, Ryan KA, Cirimotich S, Jones C, Knight R, Walters 
WA, Knights D, et al. Analysis of the gut microbiota in the old order amish and its relation to the 
metabolic syndrome. PLoS One 2012;7:e43052. 
70. Jeffery IB, Claesson MJ, O'Toole PW, Shanahan F. Categorization of the gut microbiota: 
Enterotypes or gradients? Nat Rev Microbiol 2012;10:591-2. 

 
Protocol FMT, Version 10 May 2020 
 
Page 22 of 29 
71. Kalliomaki M, Collado MC, Salminen S, Isolauri E. Early differences in fecal microbiota 
composition in children may predict overweight. Am J Clin Nutr 2008;87:534-8. 
72. Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, Almeida M, Arumugam M, 
Batto JM, Kennedy S, et al. Richness of human gut microbiome correlates with metabolic markers. 
Nature 2013;500:541-6. 
73. Cotillard A, Kennedy SP, Kong LC, Prifti E, Pons N, Le Chatelier E, Almeida M, Quinquis B, 
Levenez F, Galleron N, et al. Dietary intervention impact on gut microbial gene richness. Nature 
2013;500:585-8. 
74. Dethlefsen L, Eckburg PB, Bik EM, Relman DA. Assembly of the human intestinal microbiota. 
Trends Ecol Evol 2006;21:517-23. 
75. Mai V, Draganov PV. Recent advances and remaining gaps in our knowledge of associations 
between gut microbiota and human health. World J Gastroenterol 2009;15:81-5. 
76. Zoetendal EG, Vaughan EE, de Vos WM. A microbial world within us. Molecular Microbiology 
2006;59:1639-50. 
77. DiBaise JK, Zhang H, Crowell MD, Krajmalnik-Brown R, Decker GA, Rittmann BE. Gut 
microbiota and its possible relationship with obesity. Mayo Clin Proc 2008;83:460-9. 
78. Samuel BS, Gordon JI. A humanized gnotobiotic mouse model of host-archaeal-bacterial 
mutualism. Proc Natl Acad Sci U S A 2006;103:10011-6. . 
79. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated 
gut microbiome with increased capacity for energy harvest. Nature 2006;444:1027-31. 
80. Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut 
microbial ecology. Proc Natl Acad Sci U S A 2005;102:11070-5. 
81. Parnell JA, Reimer RA. Prebiotic fibres dose-dependently increase satiety hormones and alter 
bacteroidetes and firmicutes in lean and obese jcr:La-cp rats. Br J Nutr 2011:1-13. 
82. Backhed F, Manchester JK, Semenkovich CF, Gordon JI. Mechanisms underlying the resistance to 
diet-induced obesity in germ-free mice. Proc Natl Acad Sci U S A 2007;104:979-84. 
83. Siebler J, Galle PR, Weber MM. The gut-liver-axis: Endotoxemia, inflammation, insulin resistance 
and nash. J Hepatol 2008;48:1032-4. 
84. Henao-Mejia J, Elinav E, Jin C, Hao L, Mehal WZ, Strowig T, Thaiss CA, Kau AL, Eisenbarth SC, 
Jurczak MJ, et al. Inflammasome-mediated dysbiosis regulates progression of nafld and obesity. Nature 
2012;482:179-85. 
85. Cani PD, Geurts L, Matamoros S, Plovier H, Duparc T. Glucose metabolism: Focus on gut 
microbiota, the endocannabinoid system and beyond. Diabetes and Metabolism 2014:[Epub ahead of 
print]. 
86. Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D, Neyrinck AM, Fava F, Tuohy KM, 
Chabo C, et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 2007;56:1761-
72. 
87. Brun P, Castagliuolo I, Di Leo V, Buda A, Pinzani M, Palu G, Martines D. Increased intestinal 
permeability in obese mice: New evidence in the pathogenesis of nonalcoholic steatohepatitis. Am J 
Physiol Gastrointest Liver Physiol 2007;292:G518-25. 
88. Cani PD, Bibiloni R, Knauf C, Waget A, Neyrinck AM, Delzenne NM, Burcelin R. Changes in gut 
microbiota control metabolic endotoxemia-induced inflammation in high-fat diet-induced obesity and 
diabetes in mice. Diabetes 2008;57:1470-81. 
89. Shi H, Kokoeva MV, Inouye K, Tzameli I, Yin H, Flier JS. Tlr4 links innate immunity and fatty 
acid-induced insulin resistance. J Clin Invest 2006;116:3015-25. 
90. Tsukumo DM, Carvalho-Filho MA, Carvalheira JB, Prada PO, Hirabara SM, Schenka AA, Araujo 
EP, Vassallo J, Curi R, Velloso LA, et al. Loss-of-function mutation in toll-like receptor 4 prevents 
diet-induced obesity and insulin resistance. Diabetes 2007;56:1986-98. 

 
Protocol FMT, Version 10 May 2020 
 
Page 23 of 29 
91. Cani PD, Delzenne NM. The role of the gut microbiota in energy metabolism and metabolic 
disease. Curr Pharm Des 2009;15:1546-58. 
92. Cai D, Yuan M, Frantz DF, Melendez PA, Hansen L, Lee J, Shoelson SE. Local and systemic 
insulin resistance resulting from hepatic activation of ikk-beta and nf-kappab. Nat Med 2005;11:183-
90. 
93. Bastard JP, Maachi M, Lagathu C, Kim MJ, Caron M, Vidal H, Capeau J, Feve B. Recent advances 
in the relationship between obesity, inflammation, and insulin resistance. European Cytokine Network 
2006;17:4-12. 
94. Frost G, Sleeth ML, Sahuri-Arisoylu M, Lizarbe B, Cerdan S, Brody L, Anastasovska J, Ghourab 
S, Hankir M, Zhang S, et al. The short-chain fatty acid acetate reduces appetite via a central 
homeostatic mechanism. Nat Commun 2014;5:3611. 
95. Tolhurst G, Heffron H, Lam YS, Parker HE, Habib AM, Diakogiannaki E, Cameron J, Grosse J, 
Reimann F, Gribble FM. Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the g-
protein-coupled receptor ffar2. Diabetes 2012;61:364-71. 
96. Tilg H, Moschen AR. Microbiota and diabetes: An evolving relationship. Gut 2014:[Epub ahead of 
print]. 
97. Liou AP, Paziuk M, Luevano JM, Jr., Machineni S, Turnbaugh PJ, Kaplan LM. Conserved shifts in 
the gut microbiota due to gastric bypass reduce host weight and adiposity. Sci Transl Med 
2013;5:178ra41. 
98. Kong LC, Tap J, Aron-Wisnewsky J, Pelloux V, Basdevant A, Bouillot JL, Zucker JD, Dore J, 
Clement K. Gut microbiota after gastric bypass in human obesity: Increased richness and associations 
of bacterial genera with adipose tissue genes. Am J Clin Nutr 2013;98:16-24. 
99. Everard A, Belzer C, Geurts L, Ouwerkerk JP, Druart C, Bindels LB, Guiot Y, Derrien M, Muccioli 
GG, Delzenne NM, et al. Cross-talk between akkermansia muciniphila and intestinal epithelium 
controls diet-induced obesity. Proc Natl Acad Sci U S A 2013;110:9066-71. 
100. Shin NR, Lee JC, Lee HY, Kim MS, Whon TW, Lee MS, Bae JW. An increase in the 
akkermansia spp. Population induced by metformin treatment improves glucose homeostasis in diet-
induced obese mice. Gut 2014;63:727-35. 
101. Smits LP, Bouter KE, de Vos WM, Borody TJ, Nieuwdorp M. Therapeutic potential of fecal 
microbiota transplantation. Gastroenterology 2013;145:946-53. 
102. Kelly CR, Kahn S, Kashyap P, Laine L, Rubin D, Atreja A, Moore T, Wu G. Update on fecal 
microbiota transplantation 2015: Indications, methodologies, mechanisms, and outlook. 
Gastroenterology 2015;149:223-37. 
103. Rossen NG, MacDonald JK, de Vries EM, D'Haens GR, de Vos WM, Zoetendal EG, Ponsioen 
CY. Fecal microbiota transplantation as novel therapy in gastroenterology: A systematic review. World 
J Gastroenterol 2015;21:5359-71. 
104. Health Canada. Guidance document: Fecal microbiota therapy used in the treatment of clostridium 
difficile infection not responsive to conventional therapies. Ottawa2015. 
105. Alang N, Kelly CR. Weight gain after fecal microbiota transplantation. Open Forum Infect Dis 
2015;2:ofv004. 
106. Kelly JR, Kennedy PJ, Cryan JF, Dinan TG, Clarke G, Hyland NP. Breaking down the barriers: 
The gut microbiome, intestinal permeability and stress-related psychiatric disorders. Front Cell 
Neurosci 2015;9:392. 
107. Petra AI, Panagiotidou S, Hatziagelaki E, Stewart JM, Conti P, Theoharides TC. Gut-microbiota-
brain axis and its effect on neuropsychiatric disorders with suspected immune dysregulation. Clin Ther 
2015;37:984-95. 
108. Gareau MG, Silva MA, Perdue MH. Pathophysiological mechanisms of stress-induced intestinal 
damage. Curr Mol Med 2008;8:274-81. 

 
Protocol FMT, Version 10 May 2020 
 
Page 24 of 29 
109. Aisa B, Gil-Bea FJ, Marcos B, Tordera R, Lasheras B, Del Rio J, Ramirez MJ. Neonatal stress 
affects vulnerability of cholinergic neurons and cognition in the rat: Involvement of the hpa axis. 
Psychoneuroendocrinology 2009;34:1495-505. 
110. Barreau F, Ferrier L, Fioramonti J, Bueno L. Neonatal maternal deprivation triggers long term 
alterations in colonic epithelial barrier and mucosal immunity in rats. Gut 2004;53:501-6. 
111. Bercik P. Role of the microbiome in brain development and function. in International Human 
Microbiome Congress. Luxembourg2015. 
112. Dinan T. Research work at the alimentary pharmabiotic centre biosciences institute at university 
college cork. 2016. 
113. Diaz Heijtz R, Wang S, Anuar F, Qian Y, Bjorkholm B, Samuelsson A, Hibberd ML, Forssberg 
H, Pettersson S. Normal gut microbiota modulates brain development and behavior. Proc Natl Acad Sci 
U S A 2011;108:3047-52. 
114. Neufeld KM, Kang N, Bienenstock J, Foster JA. Reduced anxiety-like behavior and central 
neurochemical change in germ-free mice. Neurogastroenterol Motil 2011;23:255-64, e119. 
115. Ximenez-Fyvie LA, Haffajee AD, Socransky SS. Microbial composition of supra- and subgingival 
plaque in subjects with adult periodontitis. J Clin Periodontol 2000;27:722-32. 
116. Tervonen T, Oliver RC, Wolff LF, Bereuter J, Anderson L, Aeppli DM. Prevalence of periodontal 
pathogens with varying metabolic control of diabetes mellitus. J Clin Periodontol 1994;21:375-9. 
117. Haffajee AD, Socransky SS. Relation of body mass index, periodontitis and tannerella forsythia. J 
Clin Periodontol 2009;36:89-99. 
118. Chaffee BW, Weston SJ. Association between chronic periodontal disease and obesity: A 
systematic review and meta-analysis. J Periodontol 2010;81:1708-24. 
119. Bascones-Martinez A, Arias-Herrera S, Criado-Camara E, Bascones-Ilundain J, Bascones-
Ilundain C. Periodontal disease and diabetes. Adv Exp Med Biol 2012;771:76-87. 
120. Brown LJ, Johns BA, Wall TP. The economics of periodontal diseases. Periodontol 2000 
2002;29:223-34. 
121. Eke PI, Dye BA, Wei L, Slade GD, Thornton-Evans GO, Borgnakke WS, Taylor GW, Page RC, 
Beck JD, Genco RJ. Update on prevalence of periodontitis in adults in the united states: Nhanes 2009 - 
2012. J Periodontol 2015:1-18. 
122. Pihlstrom BL, Michalowicz BS, Johnson NW. Periodontal diseases. Lancet 2005;366:1809-20. 
123. Modeer T, Blomberg CC, Wondimu B, Julihn A, Marcus C. Association between obesity, flow 
rate of whole saliva, and dental caries in adolescents. Obesity (Silver Spring) 2010;18:2367-73. 
124. Hintao J, Teanpaisan R, Chongsuvivatwong V, Ratarasan C, Dahlen G. The microbiological 
profiles of saliva, supragingival and subgingival plaque and dental caries in adults with and without 
type 2 diabetes mellitus. Oral Microbiol Immunol 2007;22:175-81. 
125. Zhou M, Rong R, Munro D, Zhu C, Gao X, Zhang Q, Dong Q. Investigation of the effect of type 2 
diabetes mellitus on subgingival plaque microbiota by high-throughput 16s rdna pyrosequencing. PLoS 
One 2013;8:e61516. 
126. Kuo LC, Polson AM, Kang T. Associations between periodontal diseases and systemic diseases: 
A review of the inter-relationships and interactions with diabetes, respiratory diseases, cardiovascular 
diseases and osteoporosis. Public Health 2008;122:417-33. 
127. Shillitoe E, Weinstock R, Kim T, Simon H, Planer J, Noonan S, Cooney R. The oral microflora in 
obesity and type-2 diabetes. J Oral Microbiol 2012;4. 
128. Casarin RC, Barbagallo A, Meulman T, Santos VR, Sallum EA, Nociti FH, Duarte PM, Casati 
MZ, Goncalves RB. Subgingival biodiversity in subjects with uncontrolled type-2 diabetes and chronic 
periodontitis. J Periodontal Res 2013;48:30-6. 
129. Sumner AE, Cowie CC. Ethnic differences in the ability of triglyceride levels to identify insulin 
resistance. Atherosclerosis 2008;196:696-703. 

 
Protocol FMT, Version 10 May 2020 
 
Page 25 of 29 
130. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, 
Dunbar GC. The mini-international neuropsychiatric interview (m.I.N.I.): The development and 
validation of a structured diagnostic psychiatric interview for dsm-iv and icd-10. J Clin Psychiatry 
1998;59 Suppl 20:22-33;quiz 4-57. 
131. Brahe LK, Le Chatelier E, Prifti E, Pons N, Kennedy S, Blaedel T, Hakansson J, Dalsgaard TK, 
Hansen T, Pedersen O, et al. Dietary modulation of the gut microbiota--a randomised controlled trial in 
obese postmenopausal women. Br J Nutr 2015;114:406-17. 
132. Chen J, He X, Huang J. Diet effects in gut microbiome and obesity. J Food Sci 2014;79:R442-51. 
133. Costello SP, Conlon MA, Vuaran MS, Roberts-Thomson IC, Andrews JM. Faecal microbiota 
transplant for recurrent clostridium difficile infection using long-term frozen stool is effective: Clinical 
efficacy and bacterial viability data. Aliment Pharmacol Ther 2015;42:1011-8. 
134. Drago L, Toscano M, De Grandi R, Casini V, Pace F. Persisting changes of intestinal microbiota 
after bowel lavage and colonoscopy. Eur J Gastroenterol Hepatol 2016;28:532-7. 
135. Jalanka J, Salonen A, Salojarvi J, Ritari J, Immonen O, Marciani L, Gowland P, Hoad C, Garsed 
K, Lam C, et al. Effects of bowel cleansing on the intestinal microbiota. Gut 2015;64:1562-8. 
136. Hamilton MJ, Weingarden AR, Unno T, Khoruts A, Sadowsky MJ. High-throughput DNA 
sequence analysis reveals stable engraftment of gut microbiota following transplantation of previously 
frozen fecal bacteria. Gut Microbes 2013;4:125-35. 
137. Mora F, Cassinello N, Mora M, Bosca M, Minguez M, Ortega J. Esophageal abnormalities in 
morbidly obese adult patients. Surg Obes Relat Dis 2015. 
138. Hamilton MJ, Weingarden AR, Sadowsky MJ, Khoruts A. Standardized frozen preparation for 
transplantation of fecal microbiota for recurrent clostridium difficile infection. Am J Gastroenterol 
2012;107:761-7. 
139. Mouzaki M, Comelli EM, Arendt BM, Bonengel J, Fung SK, Fischer SE, McGilvray ID, Allard 
JP. Intestinal microbiota in patients with nonalcoholic fatty liver disease. Hepatology 2013;58:120-7. 
140. Gloor GB, Hummelen R, Macklaim JM, Dickson RJ, Fernandes AD, MacPhee R, Reid G. 
Microbiome profiling by illumina sequencing of combinatorial sequence-tagged pcr products. PLoS 
One 2010;5:e15406. 
141. Edgar RC, Flyvbjerg H. Error filtering, pair assembly and error correction for next-generation 
sequencing reads. Bioinformatics 2015;31:3476-82. 
142. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. Dada2: High resolution 
sample inference from amplicon data. bioRxiv 2015. 
143. Aitchison J. The statistical analysis of compositional data. London, UK: Chapman and Hall; 1986. 
144. Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB. Anova-like differential expression 
(aldex) analysis for mixed population rna-seq. PLoS One 2013;8:e67019. 
145. Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. Unifying the 
analysis of high-throughput sequencing datasets: Characterizing rna-seq, 16s rrna gene sequencing and 
selective growth experiments by compositional data analysis. Microbiome 2014;2:15. 
146. Gloor GB, Macklaim JM, Vu M, Fernandes AD. Compositional uncertainty should not be ignored 
in high throughput sequencing data analysis. Austrian J Statistics;Invited and in press. 
147. Kurtz ZD, Muller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and 
compositionally robust inference of microbial ecological networks. PLoS Comput Biol 
2015;11:e1004226. 
148. Lovell D, Pawlowsky-Glahn V, Egozcue JJ, Marguerat S, Bahler J. Proportionality: A valid 
alternative to correlation for relative data. PLoS Comput Biol 2015;11:e1004075. 
149. Aitchison J, Greenacre M. Biplots of compositional data. Journal of the Royal Statistical Society: 
Series C (Applied Statistics) 2002;51:375-92. 
150. Van den Googaart KG, Tolosana-Delgado R. Analyzing compositional data with r. Berlin 
Heidelberg, Germany: Springer-Verlag; 2013. 

 
Protocol FMT, Version 10 May 2020 
 
Page 26 of 29 
151. Gloor GB, Reid G, Links M. Compositional analysis: A valid approach to analyze microbiome 
high throughput sequencing data. Can J Microbiol;Invited and under review. 
152. Gloor GB, Wu JR, Pawlowsky-Glahn V, Egozcue JJ. It’s all relative: Analyzing microbiome data 
as compositions. Ann Epidemiol;In press. 
153. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using 
empirical bayes methods. Biostatistics 2007;8:118-27. 
154. Gloor GB, Macklaim JM, Fernandes AD. Displaying variation in large datasets: Plotting a visual 
summary of effect sizes. Journal of Computational and Graphical Statistics 2015:1-16. 
155. Halsey LG, Curran-Everett D, Vowler SL, Drummond GB. The fickle p value generates 
irreproducible results. Nat Methods 2015;12:179-85. 
156. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, 
Burkepile DE, Vega Thurber RL, Knight R, et al. Predictive functional profiling of microbial 
communities using 16s rrna marker gene sequences. Nat Biotechnol 2013;31:814-21. 
157. Macklaim JM, Fernandes AD, Di Bella JM, Hammond JA, Reid G, Gloor GB. Comparative meta-
rna-seq of the vaginal microbiota and differential expression by lactobacillus iners in health and 
dysbiosis. Microbiome 2013;1:12. 
158. Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C. Metagenomic 
microbial community profiling using unique clade-specific marker genes. Nat Methods 2012;9:811-4. 
159. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, Couger MB, Eccles D, 
Li B, Lieber M, et al. De novo transcript sequence reconstruction from rna-seq using the trinity 
platform for reference generation and analysis. Nat Protoc 2013;8:1494-512. 
160. McMurrough TA, Dickson RJ, Thibert SM, Gloor GB, Edgell DR. Control of catalytic efficiency 
by a coevolving network of catalytic and noncatalytic residues. Proc Natl Acad Sci U S A 
2014;111:E2376-83. 
161. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. 
Metagenomic biomarker discovery and explanation. Genome Biol 2011;12:R60. 
162. Penders J, Vink C, Driessen C, London N, Thijs C, Stobberingh EE. Quantification of 
bifidobacterium spp., escherichia coli and clostridium difficile in faecal samples of breast-fed and 
formula-fed infants by real-time pcr. FEMS Microbiol Lett 2005;243:141-7. 
163. Le Gall G, Noor SO, Ridgway K, Scovell L, Jamieson C, Johnson IT, Colquhoun IJ, Kemsley EK, 
Narbad A. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in 
ulcerative colitis and irritable bowel syndrome. J Proteome Res 2011;10:4208-18. 
164. Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, 
Eisner R, Gautam B, et al. The human serum metabolome. PLoS One 2011;6:e16957. 
165. Wallace TM, Levy JC, Matthews DR. Use and abuse of homa modeling. Diabetes Care 
2004;27:1487-95. 
166. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis 
model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin 
concentrations in man. Diabetologia 1985;28:412-9. 
167. Levy JC, Matthews DR, Hermans MP. Correct homeostasis model assessment (homa) evaluation 
uses the computer program. Diabetes Care 1998;21:2191-2. 
168. Allard JP, Aghdassi E, Mohammed S, Raman M, Avand G, Arendt BM, Jalali P, Kandasamy T, 
Prayitno N, Sherman M, et al. Nutritional assessment and hepatic fatty acid composition in non-
alcoholic fatty liver disease (nafld): A cross-sectional study. Journal of Hepatology 2008;48:300-7. 
169. Arendt BM, Aghdassi E, Mohammed SS, Fung LY, Jalali P, Salit IE, Allard JP. Dietary intake 
and physical activity in a canadian population sample of male patients with hiv infection and metabolic 
abnormalities. Curr HIV Res 2008;6:82-90. 

 
Protocol FMT, Version 10 May 2020 
 
Page 27 of 29 
170. Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX, Hu ZX, Lin J, Xiao JZ, Cao HB, et al. 
Effects of diet and exercise in preventing niddm in people with impaired glucose tolerance. The da qing 
igt and diabetes study. Diabetes Care 1997;20:537-44. 
171. Mai V. Dietary modification of the intestinal microbiota. Nutr Rev 2004;62:235-42. 
172. Christou NV, Look D, McLean AP. Pre- and post-prandial plasma ghrelin levels do not correlate 
with satiety or failure to achieve a successful outcome after roux-en-y gastric bypass. Obes Surg 
2005;15:1017-23. 
173. Flint A, Raben A, Blundell JE, Astrup A. Reproducibility, power and validity of visual analogue 
scales in assessment of appetite sensations in single test meal studies. Int J Obes Relat Metab Disord 
2000;24:38-48. 
174. Williams JB, Kobak KA. Development and reliability of a structured interview guide for the 
montgomery asberg depression rating scale (sigma). Br J Psychiatry 2008;192:52-8. 
175. Maier W, Buller R, Philipp M, Heuser I. The hamilton anxiety scale: Reliability, validity and 
sensitivity to change in anxiety and depressive disorders. J Affect Disord 1988;14:61-8. 
176. Connor KM, Davidson JR. Development of a new resilience scale: The connor-davidson resilience 
scale (cd-risc). Depress Anxiety 2003;18:76-82. 
177. Hays RD, Sherbourne CD, Mazel RM. The rand 36-item health survey 1.0. Health Economics 
1993;2:217-27. 
178. Galimanas V, Hall MW, Singh N, Lynch MD, Goldberg M, Tenenbaum H, Cvitkovitch DG, 
Neufeld JD, Senadheera DB. Bacterial community composition of chronic periodontitis and novel oral 
sampling sites for detecting disease indicators. Microbiome 2014;2:32. 
179. Lancaster GA, Dodd S, Williamson PR. Design and analysis of pilot studies: Recommendations 
for good practice. J Eval Clin Pract 2004;10:307-12. 
180. Moore CG, Carter RE, Nietert PJ, Stewart PW. Recommendations for planning pilot studies in 
clinical and translational research. Clin Transl Sci 2011;4:332-7. 
181. Diggle P, Heagerty P, Liang KY, Zeger S. Analysis of longitudinal data. Oxford, England: 
Oxford; 2002. 
182. Jiang J. Goodness-of-fit tests for mixed model diagnostics. Ann Statist 2001;29:1137–64. 
183. Jolliffe IT. Principal component analysis. 2nd ed. New York, NY: Springer-Verlag; 2002. 
184. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical  and powerful 
approach to multiple testing. J R Stat Soc Series B  1995;57:289-300. 
185. Da Silva HE, Arendt BM, Noureldin SA, Therapondos G, Guindi M, Allard JP. A cross-sectional 
study assessing dietary intake and physical activity in canadian patients with nonalcoholic fatty liver 
disease vs healthy controls. J Acad Nutr Diet 2014:[Epub ahead of print]. 
186. Arendt BM, Comelli EM, Ma DW, Lou W, Teterina A, Kim T, Fung SK, Wong DK, McGilvray I, 
Fischer SE, et al. Altered hepatic gene expression in nonalcoholic fatty liver disease is associated with 
lower hepatic n-3 and n-6 polyunsaturated fatty acids. Hepatology 2015. 
187. Elnahas A, Urbach D, Okrainec A, Quereshy F, Jackson TD. Is next-day discharge following 
laparoscopic roux-en-y gastric bypass safe in select patients? Analysis of short-term outcomes. Surg 
Endosc 2014:[Epub ahead of print]. 
188. Okrainec A, Ferri LE, Feldman LS, Fried GM. Defining the learning curve in laparoscopic 
paraesophageal hernia repair: A cusum analysis. Surg Endosc 2011;25:1083-7. 
189. Sockalingam S, Cassin S, Crawford SA, Pitzul K, Khan A, Hawa R, Jackson T, Okrainec A. 
Psychiatric predictors of surgery non-completion following suitability assessment for bariatric surgery. 
Obes Surg 2013;23:205-11. 
190. Allen-Vercoe E, Reid G, Viner N, Gloor GB, Hota S, Kim P, Lee C, O'Doherty K, Vanner SJ, 
Weese JS, et al. A canadian working group report on fecal microbial therapy: Microbial ecosystems 
therapeutics. Canadian Journal of Gastroenterology 2012;26:457-62. 

 
Protocol FMT, Version 10 May 2020 
 
Page 28 of 29 
191. Kus JV, Tadros M, Simor A, Low DE, McGeer AJ, Willey BM, Larocque C, Pike K, Edwards IA, 
Dedier H, et al. New delhi metallo-beta-lactamase-1: Local acquisition in ontario, canada, and 
challenges in detection. CMAJ 2011;183:1257-61. 
192. Macklaim JM, Gloor GB, Anukam KC, Cribby S, Reid G. At the crossroads of vaginal health and 
disease, the genome sequence of lactobacillus iners ab-1. Proc Natl Acad Sci U S A 2011;108 Suppl 
1:4688-95. 
193. Hummelen R, Macklaim JM, Bisanz JE, Hammond JA, McMillan A, Vongsa R, Koenig D, Gloor 
GB, Reid G. Vaginal microbiome and epithelial gene array in post-menopausal women with moderate 
to severe dryness. PLoS One 2011;6:e26602. 
194. Petrof EO, Gloor GB, Vanner SJ, Weese SJ, Carter D, Daigneault MC, Brown EM, Schroeter K, 
Allen-Vercoe E. Stool substitute transplant therapy for the eradication of clostridium difficile infection: 
'Repoopulating' the gut. Microbiome 2013;1:3. 
195. Comelli EM, Simmering R, Faure M, Donnicola D, Mansourian R, Rochat F, Corthesy-Theulaz I, 
Cherbut C. Multifaceted transcriptional regulation of the murine intestinal mucus layer by endogenous 
microbiota. Genomics 2008;91:70-7. 
196. Shakur YA, Lou W, L'Abbe MR. Examining the effects of increased vitamin d fortification on 
dietary inadequacy in canada. Can J Public Health 2014;105:e127-32. 
197. Lou W. Finite markov chain approach for discrete and continuous scan statistics. In: Handbook of 
scan statistics. Glaz J, Koutras MV, editor^editors.  New York: Springer; 2014. 
198. Zipursky JS, Sidorsky TI, Freedman CA, Sidorsky MN, Kirkland KB. Patient attitudes toward the 
use of fecal microbiota transplantation in the treatment of recurrent clostridium difficile infection. Clin 
Infect Dis 2012;55:1652-8. 
199. Zipursky JS, Sidorsky TI, Freedman CA, Sidorsky MN, Kirkland KB. Physician attitudes toward 
the use of fecal microbiota transplantation for the treatment of recurrent clostridium difficile infection. 
Can J Gastroenterol Hepatol 2014;28:319-24. 
200. Buchwald H, Avidor Y, Braunwald E, Jensen MD, Pories W, Fahrbach K, Schoelles K. Bariatric 
surgery: A systematic review and meta-analysis. JAMA 2004;292:1724-37. 
201. Appel LJ, Clark JM, Yeh HC, Wang NY, Coughlin JW, Daumit G, Miller ER, 3rd, Dalcin A, 
Jerome GJ, Geller S, et al. Comparative effectiveness of weight-loss interventions in clinical practice. 
N Engl J Med 2011;365:1959-68. 
202. Wadden TA, Volger S, Sarwer DB, Vetter ML, Tsai AG, Berkowitz RI, Kumanyika S, Schmitz 
KH, Diewald LK, Barg R, et al. A two-year randomized trial of obesity treatment in primary care 
practice. N Engl J Med 2011;365:1969-79. 
203. Sjostrom L, Narbro K, Sjostrom CD, Karason K, Larsson B, Wedel H, Lystig T, Sullivan M, 
Bouchard C, Carlsson B, et al. Effects of bariatric surgery on mortality in swedish obese subjects. N 
Engl J Med 2007;357:741-52. 
204. Longitudinal Assessment of Bariatric Surgery C, Flum DR, Belle SH, King WC, Wahed AS, Berk 
P, Chapman W, Pories W, Courcoulas A, McCloskey C, et al. Perioperative safety in the longitudinal 
assessment of bariatric surgery. N Engl J Med 2009;361:445-54. 
205. Burguera B, Agusti A, Arner P, Baltasar A, Barbe F, Barcelo A, Breton I, Cabanes T, Casanueva 
FF, Couce ME, et al. Critical assessment of the current guidelines for the management and treatment of 
morbidly obese patients. J Endocrinol Invest 2007;30:844-52. 
206. Jackson TD, Hutter MM. Morbidity and effectiveness of laparoscopic sleeve gastrectomy, 
adjustable gastric band, and gastric bypass for morbid obesity. Adv Surg 2012;46:255-68. 
207. Schauer PR, Bhatt DL, Kirwan JP, Wolski K, Brethauer SA, Navaneethan SD, Aminian A, 
Pothier CE, Kim ES, Nissen SE, et al. Bariatric surgery versus intensive medical therapy for diabetes--
3-year outcomes. N Engl J Med 2014;370:2002-13. 

 
Protocol FMT, Version 10 May 2020 
 
Page 29 of 29 
208. Furet JP, Firmesse O, Gourmelon M, Bridonneau C, Tap J, Mondot S, Dore J, Corthier G. 
Comparative assessment of human and farm animal faecal microbiota using real-time quantitative pcr. 
FEMS Microbiol Ecol 2009;68:351-62. 
209. Haakensen M, Dobson CM, Deneer H, Ziola B. Real-time pcr detection of bacteria belonging to 
the firmicutes phylum. Int J Food Microbiol 2008;125:236-41. 
210. Song Y, Liu C, Finegold SM. Real-time pcr quantitation of clostridia in feces of autistic children. 
Appl Environ Microbiol 2004;70:6459-65. 
211. Haarman M, Knol J. Quantitative real-time pcr analysis of fecal lactobacillus species in infants 
receiving a prebiotic infant formula. Appl Environ Microbiol 2006;72:2359-65. 
212. Belenguer A, Duncan SH, Calder AG, Holtrop G, Louis P, Lobley GE, Flint HJ. Two routes of 
metabolic cross-feeding between bifidobacterium adolescentis and butyrate-producing anaerobes from 
the human gut. Appl Environ Microbiol 2006;72:3593-9. 
213. Matsuda K, Tsuji H, Asahara T, Matsumoto K, Takada T, Nomoto K. Establishment of an 
analytical system for the human fecal microbiota, based on reverse transcription-quantitative pcr 
targeting of multicopy rrna molecules. Appl Environ Microbiol 2009;75:1961-9. 
214. Schwiertz A, Le Blay G, Blaut M. Quantification of different eubacterium spp. In human fecal 
samples with species-specific 16s rrna-targeted oligonucleotide probes. Appl Environ Microbiol 
2000;66:375-82. 
215. Furet J, Firmesse O, Gourmelon M, Bridonneau C, Tap J, Mondot S, Dore J, Corthier G. 
Comparative assessment of human and farm animal faecal microbiota using real-time quantitative pcr. 
FEMS Microbiol Ecol 2009;68:351-62. Epub 2009 Mar 19. 
216. Palmer C, Bik EM, DiGiulio DB, Relman DA, Brown PO. Development of the human infant 
intestinal microbiota. PLoS Biol 2007;5:e177. 
217. Ramirez-Farias C, Slezak K, Fuller Z, Duncan A, Holtrop G, Louis P. Effect of inulin on the 
human gut microbiota: Stimulation of bifidobacterium adolescentis and faecalibacterium prausnitzii. Br 
J Nutr 2009;101:541-50.