Elucidating the Dynamics and Impact of the Gut Microbiome on Maternal Nutritional Status During Pregnancy
Conditions: Weight Gain, Pregnancy, Microbial Colonization, Pregnancy Related, Pregnancy Loss, Pregnancy Complications, Pregnancy; Parasitic Disease, Parasitic Disease, Microbial Disease, Infant Malnutrition, Malnutrition, Malnutrition in Pregnancy, Breastfeeding
Sponsor: Aga Khan University

 
Version: 2.0 Date: 04 Aug 2021 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Elucidating the Dynamics and Impact of the Gut 
Microbiome on Maternal Nutritional Status During 
Pregnancy  
 
 
 
 
 
 
Principal Investigator: Dr. Zulfiqar Bhutta 
 
 
 
 
Funded by: Canadian Institutes of Health Research (CIHR) 
Aug 4, 2021

  
 
Table of Contents 
1 
PROTOCOL SUMMARY .................................................................................................. 1 
1.1 
Synopsis ..................................................................................................................... 1 
1.2 
Schema ...................................................................................................................... 3 
1.3 
Schedule of Activities (SoA) .................................................................................... 6 
2 
INTRODUCTION ............................................................................................................... 9 
2.1 
Study Rationale ....................................................................................................... 10 
2.2 
Background .............................................................................................................. 10 
2.2.1 
Undernutrition Before and During Pregnancy Dramatically Impacts 
Maternal Health, Birth and Life Outcomes ...................................................................... 10 
2.2.2 
The Gut Microbiome Impacts Maternal and Infant Nutrition ............ 12 
2.2.3 
Pathogen Exposure Contributes to Environmental Enteric 
Dysfunction 
12 
2.2.4 
Eukaryotic Microbes are a Key Component of the Microbiome ...... 13 
2.2.5 
Marker Gene Surveys Coupled with Advances in Next Generation 
Sequencing Provide Unprecedented Insights into Microbial Communities................ 13 
2.2.6 
Metagenomics, Metranscriptomics and Metabolomics Deliver 
Readouts of Microbiome Function .................................................................................... 14 
2.2.7 
Gender Impacts Nutritional Status and the Microbiome .................. 14 
2.3 
Risk and Benefits .................................................................................................... 15 
3 
STUDY OBJECTIVES .................................................................................................... 16 
4 
STUDY DESIGN .............................................................................................................. 20 
4.1 
Study Hypothesis .................................................................................................... 20 
4.2 
Study Design ........................................................................................................... 20 
4.3 
End of Study Definition .......................................................................................... 20 
5 
STUDY POPULATION .................................................................................................... 21 
5.1 
Inclusion Criteria ..................................................................................................... 21 
5.2 
Exclusion Criteria .................................................................................................... 21 
5.3 
Study Setting ........................................................................................................... 22 
5.4 
Strategies for Recruitment and Retention .......................................................... 23 
5.4.1 
METHOD FOR Monitoring new pregnancies .................................... 23 
5.4.2 
Obtaining Informed Consent ................................................................ 24 
5.4.1 
Study Honourarium ................................................................................ 24 
6 
DISCONTINUATION AND WITHDRAWAL ................................................................. 24 
6.1 
Lost to Follow-Up .................................................................................................... 25 
6.2 
Removal of Participants ......................................................................................... 25 
7 
STUDY PROCEDURES ................................................................................................. 26 
7.1 
Study Visits .............................................................................................................. 26 
7.2 
Study Assessments and Sample Collection ....................................................... 28 
7.3 
Contact with Study Participants ............................................................................ 31 
7.4 
Safety and Adverse Events ................................................................................... 32 
7.4.1 
Reporting Events to Participants ......................................................... 33 
8 
STOOL AND BLOOD SAMPLE ANALYSIS ................................................................ 34 
8.1 
Profiling Microbial Community Structure and Function..................................... 34 
8.1.1 
Stool Sample Processing ...................................................................... 34 

  
 
8.1.2 
Profiling Microbial Community Structure ............................................ 35 
8.1.3 
Profiling Microbial Community Function ............................................. 35 
8.1.4 
Profiling of Patient and Microbiome Metabolites and Inflammatory 
Markers 
35 
8.2 
Potential Problems, Mitigations and Expected Results .................................... 36 
9 
STATISTICAL CONSIDERATIONS .............................................................................. 37 
9.1 
Power Calculation ................................................................................................... 37 
9.2 
SubGroup Analysis ................................................................................................. 37 
9.3 
Statistical Analysis .................................................................................................. 37 
9.4 
Plan For Statistical Analysis .................................................................................. 38 
9.4.1 
Addressing the Primary ObjectiveS..................................................... 39 
9.4.2 
Addressing the Secondary ObjectiveS ............................................... 40 
9.4.3 
Addressing the Exploratory Objectives ............................................... 40 
9.5 
Future Use of Stored Specimens and Data ........................................................ 41 
9.5.1 
Future Genetic Testing .......................................................................... 42 
10 
SUPPORTING DOCUMENTATION AND OPERATIONAL CONSIDERATIONS . 42 
10.1 
Regulatory, Ethical, and Study Oversight Considerations ............................... 42 
10.2 
Study Discontinuation and Closure ...................................................................... 43 
10.3 
Confidentiality and Privacy .................................................................................... 43 
10.4 
Quality Assurance and Quality Control ............................................................... 44 
10.4.1 
Data Handling and Record Keeping .................................................... 44 
10.5 
Protocol Deviations................................................................................................. 45 
10.6 
Conflict of Interest ................................................................................................... 45 
10.7 
Abbreviations ........................................................................................................... 46 
11 
REFERENCES ................................................................................................................. 47 
 
 
 
 
 
 
 

 
                         
 
 
 
 
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1 
PROTOCOL SUMMARY 
 
1.1 
SYNOPSIS  
 
Title 
Elucidating the Dynamics and Impact of the Gut Microbiome on Maternal 
Nutritional Status during Pregnancy. 
 
Study Short Title 
MMIP (Microbiome and Malnutrition in Pregnancy) 
Principal Investigator 
 
Professor Zulfiqar A Bhutta FRS, MBBS, FRCPCH, FAAP, PhD 
Distinguished University Professor & Founding Director Institute for 
Global Health & Development, The Aga Khan University, South Central 
Asia, East Africa & United Kingdom & Karachi 74800, Pakistan 
 
Robert Harding Chair in Global Child Health & Policy, 
Ibn-e-Sina Scholar in Global Child Health, Co-Director, SickKids Centre 
for Global Child Health, Senior Scientist, Research Institute 
The Hospital for Sick Children, Professor, Departments of Paediatrics, 
Nutritional Sciences and Public Health, University of Toronto, SickKids 
Peter Gilgan Centre for Research and Learning 
Co-Investigators 
Dr. John Parkinson, PhD (Senior Scientist at the Hospital for Sick Children) 
Dr. Sajid Bashir Soofi, MBBS, FCPS (Center of Excellence in Women and 
Child Health, Aga Khan University) 
Dr. Junaid Iqbal, PhD (Centre of Excellence in Women and Child Health, 
Aga Khan University) 
Mr. Yaqub Wasan, MPH MSc (Center of Excellence in Women and Child 
Health, Aga Khan University) 
Dr. Jessie Hulst, MD, PhD (Staff Pediatrician in the Division of  
Gastroenterology, Hepatology and Nutrition at the Hospital for Sick 
Children)  
Dr. Shazeen Suleman, MSc, MPH, MD (Assistant Professor in the 
Department of Pediatrics at the University of Toronto, and an Associate 
Scientist at the Li Ka Shing Knowledge Institute) 
Dr. Jo-Anna Baxter, PhD, MSc (Centre for Global Child Health, Hospital 
for Sick Children)  
Dr. Robert Bandsma, MD, PhD (Pediatric Gastroenterologist, Hepatologist 
and Nutritionist, at the Hospital for Sick Children) 
Ms. Carolyn Spiegel-Feld, MMASc (Translational Medicine at the Hospital 
for Sick Childen) 
Collaborators 
Dr. Arthur Mortha PhD (Assistant Professor in the Department of 
Immunology at the University of Toronto 
Dr. Ben Willing, PhD (Associate Professor and Tier 2 Canada Research 
Chair in Microbiology of Nutrigenomics at the University of Alberta)  
Dr. Marie Claire Arrieta Mendez, PhD (Assistant Professor at the University 
of Calgary) 
Dr. Elena Comelli, PhD (Associate Professor and the Lawson Family Chair 
in Microbiome Nutrition Research in the Department of Nutritional Sciences 
at the University of Toronto) 
Dr. Michael Grigg, PhD (Chief of Molecular Parasitology at the NIH)  

 
                         
 
 
 
 
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Dr. Andrew Roger, PhD (Professor and Canada Research Chair in 
Comparative Genomics at Dalhousie University) 
Study Design 
Prospective, longitudinal observational study design 
 
Objectives 
 
Primary Objective: 
 
To assess if alterations of the microbiota in the maternal gut (dysbiosis) is 
associated with maternal gestational weight gain. 
 
To determine the association between maternal microbiome dysbiosis 
during pregnancy and birth outcomes, infant growth, infant nutritional 
status and health in the first year of life.  
 
Secondary Objectives:   
 
To link the maternal microbiome to dietary intake, with a focus on calories 
and macronutrients. 
 
To integrate maternal anthropometric factors, morbidity and mortality with 
microbiome data to reveal key modulators (microbial taxa and metabolites) 
of dietary intake during pregnancy and the post-partum period. 
 
To determine the impact of the maternal microbiome during pregnancy, 
including the exposure to pathogens and parasites, on the development of 
the infant microbiome. 
 
To investigate the maternal microbiome’s exposure to pathogens and 
parasites, and the association with intestinal inflammation. 
 
Exploratory Objectives: 
 
To explore the role of the maternal gut microbiome during pregnancy and 
to identify gut community dynamics in pregnant women and how this 
impacts differences between dietary intake and nutritional status. 
 
To investigate socio-economic factors; including gender, poverty, exclusion 
and empowerment, and their influence on the health of a mother’s 
microbiome (assessed by alpha and/or beta diversity, and absence of 
pathogens). 
 
To explore the role of the human microbiota on nutritional status by 
performing fecal microbiota transplants in germ free mice and sterile 
piglets. 
 
Recruitment and 
Follow-up  
1) MV1: 10-14 weeks post conception 
2) MV2: 30-34 weeks post conception 
3) MV3/ IV1: 24-72 hours after birth 
4) MV4/IV2: 3 months post-partum 
5) MV5/ IV3: 6 months post-partum 

 
                         
 
 
 
 
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6) MV6/IV4:12 months post-partum 
 
MV: Maternal Visit IV: Infant Visit  
Study Population: 
Pregnant, married women aged 17 to 24 years from the Matiari district, 
Pakistan and their infants. 
 
No. of Subjects 
400 women and their infants. 
• 
200 subjects with normal BMI (18.5-24.9) at conception 
• 
200 subjects with low BMI (less than 18.5) at conception 
Study Duration: 
The study duration will be approximately 3 years. 
Participant Duration: 
Participants will be enrolled in the study from 10-14 weeks post-conception 
until 12 months post-partum. The post-conception and post-partum phase of 
the study will last between 17-19 months for participants. 
 
The infants will be followed for 12 months, from their birth until 12 months 
of age. 
Collaborating 
Institutions:  
 
Institute for Global Health & Development, AKU 
 
Centre for Global Child Health, Hospital for Sick Children 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1.2 
SCHEMA 
Figure 1A: Maternal Study Flow Diagram 

 
                         
 
 
 
 
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Pregnancy  
Surveillance 
 
 
 
 
 
 
 
 
 
MV1: 
10-14 wks  
post- 
conception 
 
 
 
 
 
 
 
MV2: 30-34 wks  
post-conception 
 
 
 
 
MV3: 24-72 hours 
post-partum 
 
 
 
 
MV4: 3 months  
post-partum 
 
 
 
 
 
 
MV5: 6 months  
post-partum 
 
  
 
 
 
MV6: 
12-Months  
Post-Partum 
 
 
 
 
 
 
Upon identification of pregnancy, participants will be screened for eligibility and 
informed consent will be obtained. The following questionnaires will be collected: 
Baseline study visit form, 24hour dietary recall and the empowerment questionnaire 
Stool and blood samples will be collected. 
The following measurements will be collected: weight, height, mid upper arm 
circumference (MUAC) and triceps skinfold thickness (SFT). 
The maternal morbidity and anthropometric assessment, and the following 
measurements will be collected: height, weight, MUAC and SFT. 
The following questionnaires will be completed: maternal morbidity and 
anthropometric assessment form and 24hour dietary recall 
Stool and blood samples will be collected. 
The following measurements will be collected: weight, height, mid upper arm 
circumference (MUAC) and triceps skinfold thickness (SFT). 
Stool and blood 
samples will be sent 
for microbiome, 
metabolomics and 
nutritional analysis. 
 
Pregnancy surveillance: Lady health workers (LHWs), volunteers and staff will 
monitor for local pregnancies. Upon pregnancy identification, recruitment and 
enrollment procedures will commence.  
 
N=400 
Normal BMI=200 
Low BMI=200 
 The following questionnaires will be completed: maternal morbidity and 
anthropometric assessment form and the post-partum questionnaire.  
The following measurements will be collected: weight, height, MUAC, and SFT.  
 
The following questionnaires will be completed: maternal morbidity anthropometric 
assessment form, empowerment questionnaire and dietary diversity questionnaire. 
Stool sample will be collected. 
The following measurements will be collected: weight, height, mid upper arm 
circumference (MUAC) and triceps skinfold thickness (SFT). 
The following questionnaires will be completed: 
maternal morbidity and anthropometric 
assessment form, 24 hour dietary recall 
Stool and blood samples will be collected 
The following measurements will be collected: 
weight, height, mid upper arm circumference 
(MUAC) and triceps skinfold thickness (SFT). 
 

 
                         
 
 
 
 
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Figure 1B: Infant Study Flow Diagram 
 
 
 
IV1: 24-72 hours 
post-delivery 
 
 
 
 
 
 
 
 
IV2: 3 months  
of age 
 
 
 
 
 
IV3: 6 months  
of age 
 
  
 
 
 
 
 
 
 
IV4: 
12-Months  
of age 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Stool and blood 
samples will be sent 
for microbiome, 
metabolomics and 
nutritional analyses. 
 
The infant study visit form will be completed. 
A stool sample and the following measurements will be collected: length, weight, 
head circumference, and MUAC. 
 
The infant study visit form and study 
completion form will be completed. 
A stool and blood sample will be collected 
and the following measurements will be 
taken: length, weight, head circumference, 
and MUAC. 
. 
 
The infant study visit form will be completed. 
A stool sample and the following measurements will be collected: length, weight, 
head circumference, and MUAC. 
The post-partum questionnaire will be completed. 
The following measurements will be collected: length, weight, MUAC and head 
circumference. 

 
                         
 
 
 
 
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1.3 
SCHEDULE OF ACTIVITIES (SOA) 
 
Table 1: Overview of study visits, visit activities, and data collection  
Procedures 
Pregnancy Surveillance  
MV1: 10-14 Weeks Post-
Conception 
MV2: 30-34 Weeks Post- 
Conception 
MV3: Maternal 24-72  
Hours Post- Partum 
IV1: Infant  24-72 Hours 
Post-delivery (same visit as 
mom) 
MV4: Maternal 3-months 
post-partum 
IV2: Child 3-Months (same 
visit as mom) 
MV5: Maternal 6 Months 
Post-Partum  
IV3: Infant 6 Months (same 
visit as mom) 
MV6:  Maternal 12-Months 
Post-Partum 
 
IV4: Child 12-Months (same 
visit as mom) 
Pregnancy 
Surveillance  
 
 
X 
 
 
 
 
 
 
 
 
 
 
Screening and 
Confirmation of 
Pregnancy 
 
 
 
 
X 
 
 
 
 
 
 
 
 
 
Informed Consent 
 
 
X 
 
 
 
 
 
 
 
 
 
 
Dietary Recall  
 
X 
X 
 
 
 
 
 
 
X 
 
Dietary Diversity 
Questionnaire 
 
 
 
 
 
X 
 
 
 
 
 
Infant feeding 
practices  
 
 
 
 
 
 
 
 
X 
 
 
X 
 
 
X 
Stool Sample 
 
 
X 
 
X 
 
 
 
X 
 
X 
 
 
 
X 
 
X 
Blood Sample 
 
 
 
X 
 
X 
 
 
 
 
 
 
 
X 
 
X 
Anthropometric 
Measurements 
 
 
 
 
X 
 
 
X 
 
 
X 
 
 
X 
 
 
X 
 
 
X 
 
 
X 
 
 
X 
 
 
X 
 
 
X 
Empowerment  
 
 
X 
 
 
 
 
X 
 
 
 
 
 
Birth and Labour 
History 
 
 
 
 
 
X 
 
X 
 
 
 
 
 
 
Health and 
Medication Use 
Assessment  
 
 
 
 
X 
 
 
X 
 
 
X 
 
X 
 
 
X 
 
 
X 
 
 
X 
 
X 
 
 
X 
Study Exit  
 
 
 
 
 
 
 
 
 
 
X 
 
X 
 
Purple: Pregnancy Surveillance  
Blue: Post Conception  
Orange: Maternal Post-Partum  
Green: Infant Visits 
 
 
 
 
 
 
 
 

 
                         
 
 
 
 
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Table 2 Overview of the Study Forms 
 
Study Form 
Code 
Study Form 
Name 
Study Visit 
Form Used 
Purpose 
Person 
Responsible 
Consent Forms 
(ICF) 
 
 
 
 
ICF 
Consent Form 
MV1 
To obtain informed 
consent 
Research Staff  
Maternal study 
visit forms (M) 
 
 
 
 
M-1 
 
Screening form- 
post conception 
baseline visit  
 
MV1 
To confirm eligibility 
Research Staff 
M-2 
Baseline visit form 
 
 
MV1 
 
To obtain baseline 
participant information. 
 
To record maternal 
anthropometric 
measurements. 
 
To assess maternal 
morbidity and mortality 
through questions 
regarding medication 
use, health seeking 
behavior etc 
Research Staff 
M-3  
Ultrasound 
assessment 
N/A: participants 
will be referred 
for ultrasound at 
baseline. This 
form will only be 
filled out for 
participants that 
have ultrasound 
completed 
To record details from 
the ultrasound 
assessment. 
Research Staff 
M-4 
Empowerment 
questionnaire 
 
 
1)MV1 
2)MV4 
To assess gender related 
variables focused on 
five main metrics: 
perceived maternal self-
efficacy, perceived 
social support, decision 
making, perceived stress 
in addition to socio-
economic factors, 
Research Staff 

 
                         
 
 
 
 
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including food and 
housing insecurity, and 
demographic data. 
M-5 
24 Hour dietary 
recall. 
1) MV1  
2) MV2 
3) MV6 
 
 
To link the microbiome 
to nutritional status and 
nutritional intake, with a 
focus on calories and 
macronutrients. 
Research Staff 
M-6 
Dietary diversity 
questionnaire 
 
 
MV4 
 
Participants will 
complete a dietary 
diversity score at the 3rd 
visit .  
 
Note the research staff 
will calculate the 
dietary diversity score 
from the dietary recalls 
filled out at MV1, MV2 
and MV6 
 
Research Staff 
M-7 
Maternal  
morbidity and 
anthropometric 
assessment form 
 
1)MV2 
2)MV4 
3)MV5 
4)MV6 
 
To record maternal 
anthropometric 
measurements. 
 
To assess maternal 
morbidity and mortality 
through questions 
regarding medication 
use, health seeking 
behavior etc. 
 
Research Staff 
M-8 
Post-Partum 
Questionnaire  
MV3 
To gather additional 
information, including 
birth characteristics 
(vaginal/ cesarean 
section), gestational 
age, birthweight and 
other measurements at 
birth (head 
circumference and 
length), placental 
insufficiency and 
antibiotic use. 
 
To collect maternal 
anthropometric 
measurements. 
Research Staff 
M-9 
Food Recipe 
Record Form 
1) MV1  
2) MV2 
3) MV6 
This form is used to 
record new recipes 
Research Staff 

 
                         
 
 
 
 
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observed during the 24 
hour dietary recall. 
 
Infant study 
visit forms (IV) 
 
 
 
 
IV-1 
Infant Study Visit 
Form 
 
 
1)IV2 
2)IV3 
3)IV4 
To assess the amount 
and mode of feeding, 
(ie, mother’s milk or 
formula; 
complementary 
feeding). 
 
To record infant 
anthropometric 
measurements. 
 
To assess infant 
morbidity and mortality 
through questions 
regarding medication 
use, health seeking 
behavior etc. 
 
IV4 visit only: 
To assess potential 
markers of inadequate 
or excessive intake and 
dietary imbalances 
through the use of the 
NutricheQ 
questionnaire. 
 
Research Staff 
Adverse Event 
(AE) 
 
 
 
 
SAE & AE 
Adverse Event 
Form 
 
 
 
As needed. 
To document 
serious/adverse events. 
Research staff 
 
 
2 
 
INTRODUCTION 
 
Nutritional status during pregnancy plays an important role in maternal health and birth 
outcomes[1, 2]. While few factors impacting nutritional status during pregnancy have been 
identified, studies of undernutrition in children have revealed a key role for the gut 
microbiome[3-9]. Remarkably, studies examining the dynamics of the maternal gut microbiome 

 
                         
 
 
 
 
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before and during pregnancy and its impact on birth outcomes are limited. Further, relatively 
little is known concerning the influence of enteric eukaryotic microbes, such as parasites, on the 
bacterial microbiome and host nutrition. Here our goal is to define the relationships between host 
nutritional status and microbiome dynamics during pregnancy and how they contribute to birth 
outcomes and growth in infancy. 
 
 
2.1 
STUDY RATIONALE  
 
This project represents the first systematic investigation of the impact of the microbiome on 
nutritional status during pregnancy in young women and directly aligns with global health 
initiatives focused on this vulnerable cohort. Here our goal is to define the relationships between 
host nutritional status and microbiome dynamics during pregnancy and how they contribute to 
birth outcomes. The gut microbiome has a profound influence on host nutritional status. 
Dysbiosis (loss of diversity/beneficial microbes and gain of pathobionts) has emerged as a major 
factor in the development of undernutrition. Despite the importance of nutrition during 
pregnancy, few studies have examined the role of the microbiome on maternal health and birth 
outcomes. Further, little is known concerning the influence of enteric eukaryotic microbes, such 
as parasites, on the bacterial microbiome and host nutrition.  
 
At the core of this study are two complementary cohorts of young women that provide an 
exceptional opportunity to obtain longitudinal samples to monitor the dynamic relationships 
between microbiome community structure and function with gut health and host nutritional 
status. This protocol will discuss the Pakistan cohort of the study, which builds off the 
partnership with Aga Khan University and Dr. Zulfiqar Bhutta at SickKids. This cohort is 
expected to be exposed to a multitude of nutritional risks as well as pathogens and parasites 
associated with intestinal disease. To better define our target demographic, we focus on young 
mothers, 17-24 years of age, in the Matiari district of Pakistan, who are especially vulnerable to 
the impact of undernutrition[10]. A second complementary cohort will be based in Toronto, 
focused on refugee and young pregnant women in Toronto. This project will yield unprecedented 
insights into the relationships between prokaryotic and eukaryotic microbes in the gut and their 
associations with maternal health and birth outcomes. 
 
 
2.2 
BACKGROUND  
 
2.2.1 UNDERNUTRITION BEFORE AND DURING PREGNANCY 
DRAMATICALLY IMPACTS MATERNAL HEALTH, BIRTH AND LIFE 
OUTCOMES 
 
Maternal and child undernutrition, is a tremendous burden on global health. Globally, 
undernutrition contributes to 45% of all deaths in children under 5 years[11]. Deficiencies in 
macro- (e.g. proteins, lipids and carbohydrates) and/or micronutrients (e.g. minerals and 
vitamins) can result in stunting (low height-for-age) and/or wasting (low weight-for-height) or 

 
                         
 
 
 
 
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thinness in adolescents and adults (low Body Mass Index – BMI). Pregnant women are 
particularly vulnerable due to the high nutrient demands during development of the fetus. 
Consequently undernutrition during pregnancy is associated with increased risk of poor birth 
outcomes, intra-uterine growth restriction of the fetus[12] and can result in complications that 
impact maternal morbidity and mortality[13-17]. In terms of perinatal outcomes, women with 
low BMI are at increased risk for stillbirths and neonatal deaths, preterm birth, low birth weight 
(LBW; <2500g) and delivering babies that are small for gestational age (SGA)[18]. 
Undernutrition prior to or during pregnancy may also have long term impact for the offspring 
(developmental origins of health and disease hypothesis)[19-21]. For example, studies of the 
1944 Dutch winter famine found women exposed to famine during late gestation did not gain 
weight in the third trimester and had babies with reduced birth weights, who were shorter and 
had smaller heads and placentas than unexposed babies[22]. Mothers exposed to famine only in 
early -mid gestation however, gained more weight than non-exposed mothers did. The offspring 
of mothers with mid gestation famine exposure were lighter, shorter and had smaller heads, 
whereas the babies that were exposed during early gestations were heavier and longer at birth. 
but had higher rates of obesity[23]. At significant risk are young women (17-24 years of age), 
the target demographic of this study.  
 
These findings broadly support the hypothesis that chronic diseases originate through adaptations 
made by the fetus in response to undernutrition. The long-term effects of intrauterine 
undernutrition, however, depend upon its timing during gestation and on the tissues and systems 
undergoing critical periods of development at that time. 
 
A woman’s nutritional status is important both prior to and during pregnancy, as it plays an 
important role in reproductive health and pregnancy outcomes[12, 24]. Prior to pregnancy, 
having sufficient nutrient intake and nutrient stores affect a woman’s ability to maintain 
obligatory physiological functioning, and support fetal development in the case of pregnancy. 
During the periconceptional period and throughout pregnancy, sufficient nutrient intake is 
necessary since different nutrients influence pregnancy outcomes by altering both maternal and 
fetal metabolism via roles in modulating oxidative stress, enzyme function, signal transduction, 
and transcription pathways[25]. Among women in LMICs, several micronutrient deficiencies 
often co-exist because a woman’s dietary intake is insufficient to meet her physiological 
requirements[12]. Pre-existing micronutrient deficiencies may be exacerbated during pregnancy 
as a result of the increased metabolic requirements[17]. Ultimately, maternal undernutrition 
during pregnancy often leads to fetal growth restriction, which increases the risk of neonatal 
deaths and childhood stunting by 2 years of age[11].  
 
Among young women, and especially among adolescents, ensuring sufficient nutrient intake is 
critical to facilitate the rapid physiological growth and maturation that occurs during the 
transition to adulthood [11]. Nutrition and growth in adolescence is particularly important to a 
one’s health and adult stature. Many of the risk factors that impact maternal and newborn health, 
such as nutritional deficiencies, exist from adolescence. Becoming pregnant during this sensitive 
life stage has been found to slow and stunt one’s growth[12].. In some countries, as many as half 
of adolescents are already stunted which further increases the risk of poor perinatal outcomes in 
their off spring[11]. Pregnancies that occur among adolescents are additionally associated with a 
higher risk of complication, maternal and child mortality, and poor birth outcomes than 

 
                         
 
 
 
 
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pregnancies in older women[26]. Specifically, adolescent pregnancy is associated with a 50% 
increased risk of stillbirths and neonatal deaths, and increased risk of preterm birth, LBW, and 
asphyxia [13, 14, 27]. The prevalence of anemia is suggested to be as high in adolescents as 
women 20–24 years of age. Collectively, this makes adolescent nutrition of substantial public 
health importance. 
 
2.2.2 THE GUT MICROBIOME IMPACTS MATERNAL AND INFANT 
NUTRITION 
 
While both genetic and environmental factors are known to impact BMI[28], our understanding 
on the interactions relating host genetics with the gut microbiome in the context of undernutrition 
remains limited[29, 30]. Indeed, twin based studies have identified few genetic factors that 
predispose to undernutrition[4]. Furthermore, undernutrition is not simply a consequence of food 
insecurity resulting in macro- and micronutrient[11, 31-33]. Instead, the intestinal microbiome 
has emerged as a key factor defining nutritional status, with impaired maturation driving 
undernutrition[3-9]. An immature microbiome may impact the extraction of critical nutrients 
such as vitamins and short chain fatty acids (SCFAs)[34-38], and/or result in the production of 
metabolites that inhibit metabolism or increase host cell turnover[4]. During pregnancy, dramatic 
changes in gut microbiota occur, with a decrease in individual (alpha) diversity but an increase in 
population (beta) diversity[39]. Relative to the 1st trimester, microbiota in the 3rd trimester 
exhibit higher abundances of Proteobacteria, typically associated with obesity in humans, and 
when transplanted to mice, result in increased adiposity and insulin insensitivity. Such 
adaptations may increase energy extraction from the diet to support pregnancy[40], raising 
interest in dietary supplements to improve pregnancy outcomes[41]. While these limited studies 
suggest an important role for the gut microbiome during pregnancy, there is a lack of data 
on gut community dynamics in pregnant women and how this impacts host nutrient 
acquisition. 
 
2.2.3 PATHOGEN EXPOSURE CONTRIBUTES TO ENVIRONMENTAL ENTERIC 
DYSFUNCTION 
 
Recently, data from the Malnutrition and Enteric Diseases multisite international study revealed 
that cumulative pathogen exposures confer a high risk for poor growth, with the prokaryotic 
enteroaggregative Escherichia coli and the protozoan Giardia lamblia most commonly 
detected[42, 43]. Exposure to pathogens such as these can contribute to EED, a condition 
characterized by chronic inflammation, poor nutrient absorption and reduced gut epithelial 
barrier function[44-46]. EED is thought to be triggered by dysbiosis (where beneficial microbes 
are replaced by pathobionts with a loss of diversity[47, 48]), initiated by nutrient deficiencies, 
antibiotic treatment and/or pathogen exposure. A ‘healthy’ microbiome normally limits pathogen 
invasion through: antimicrobial compounds; competition for nutrients; and direct stimulation of 
the immune system[49], but dysbiosis may exacerbate pathogen colonization, impair 
development of the mucosal immune system and disrupt, by as yet unknown mechanisms, 
metabolic processes that supply nutrients and energy for normal growth[6]. While maternal EED 
during pregnancy has been shown to adversely impact birth outcomes[50], our understanding 

 
                         
 
 
 
 
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of the impact of enteropathogens and dysbiosis on maternal gut community dynamics and 
nutritional status is limited. 
 
2.2.4 EUKARYOTIC MICROBES ARE A KEY COMPONENT OF THE 
MICROBIOME 
 
Typically, studies of the microbiome focus on bacterial components and tend to neglect the 
diverse array of eukaryotic microbiota. These include many considered to be parasites such as 
Giardia, Cryptosporidium and Entamoeba, each a significant global healthcare burden[51-54]. 
However not all parasitic infections cause disease; a study of Toronto daycares revealed a ~5% 
incidence of largely asymptomatic Giardia infections[55]. Thus, disease is a consequence of the 
complex interplay between the eukaryotic and bacterial microbiome and the host immune 
system[56, 57]. For example, Prevotella copri, is increased in patients with diarrheagenic E. 
Histolytica infections[54], while indole-producing bacteria such as E. coli CFT073 protect 
against Cryptosporidium[58]. Conversely, eukaryotic microbes may have neutral or even 
beneficial roles; Blastocystis, for example, has been associated with increased microbiome 
diversity[59, 60]. While current knowledge of the interactions between bacteria and 
eukaryotic microbiota and their host is limited, recent advances, discussed below, provide 
the molecular tools required to dissect these interactions. 
 
2.2.5 MARKER GENE SURVEYS COUPLED WITH ADVANCES IN NEXT 
GENERATION SEQUENCING PROVIDE UNPRECEDENTED INSIGHTS 
INTO MICROBIAL COMMUNITIES 
 
To date, microbiome studies have largely relied on marker gene surveys (e.g.16S rRNA 
sequences) to profile community structure and dynamics[61-63]. In a typical application, PCR 
reactions are used to amplify marker genes from a DNA 
sample. Sequencing is then performed to yield tens of 
thousands of sequences which are subsequently compared 
against a database of known marker genes to yield readouts 
of the abundance of each taxon in the sample. Key to this 
technology is the identification of a variable genetic region 
that allows unambiguous identification of taxonomy, 
flanked by highly conserved regions that allow universal 
primers to amplify the marker genes. For bacterial surveys, 
hypervariable regions of the 16S rRNA gene are typically 
targeted. Recently, Dr. John Parkinson and Dr. Robert 
Bandsma, targeted 18S and 28S rRNA genes to perform 
equivalent surveys for eukaryotic microbes (Table 3)[64]. In 
combination, 16S, 18S and 28S rRNA markers reveal the 
bacterial and eukaryotic components of the microbiome. 
 
Table 3. Protozoa in children with severe 
acute malnutrition from Malawi using 18S 
biomarker technology and clinical 
diagnostics. 

 
                         
 
 
 
 
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2.2.6 METAGENOMICS, METRANSCRIPTOMICS AND METABOLOMICS 
DELIVER READOUTS OF MICROBIOME FUNCTION 
 
Beyond sequence surveys, whole microbiome DNA and RNA sequencing (metagenomics and 
metatranscriptomics), together with metabolomics, offer more mechanistic insights, detailing the 
biochemical functionalities encoded and expressed by the microbiome[65-74]. For example, Dr. 
Parkinson used metatranscriptomics to reveal how changes in fat absorption in mice impact 
metabolic pathway expression in the gut microbiome[75]. Further, metabolomics has been 
transformed by improved technologies for the sensitive detection of metabolites. For example, 
our group recently revealed significant changes in metabolic pathways involved in lipid and 
protein metabolism in stunted and severely malnourished children relative to controls[76]. 
However, the underlying mechanisms explaining how the microbiome affects intestinal and 
whole-body metabolic function is just starting to be elucidated. These tools support the 
functional interrogation of microbiomes to identify microbial pathways that may modulate 
health and disease.  
2.2.7 GENDER IMPACTS NUTRITIONAL STATUS AND THE MICROBIOME 
 
It is increasingly apparent that biological sex impacts the gut microbiome, beginning in 
infancy[77]. While all pregnant individuals have the same biological sex, the experience of being 
a mother is related to gender roles and identity. Intriguingly, recent research is beginning to 
reveal how gender-related variables such as socio-economic status, food security, early marriage 
and maternal empowerment contribute to nutritional status and the development of a healthy 
intestinal microbiome[78-80]. For example, in low-income settings, maternal empowerment may 
play a role in improving breastfeeding, diversity and quality of diet in children, setting up the 
opportunity for a healthy microbiome[81]. Addressing gender roles, specifically with respect to 
maternal empowerment, has the capacity to directly impact intestinal health in infants.  
 

 
                         
 
 
 
 
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Figure 2: Conceptual Model of Maternal Socio-Economic Conditions Impact on the Infant 
Microbiome in Pakistan (Adapted from Herd et al, 2018[82]). 
 
2.3 
RISK AND BENEFITS   
 
This is an observational study. Potential risks to participants include the general risks of giving 
blood, including: light-headedness, bruising, minor bleeding where the needle is inserted into the 
arm, pain, and a very low chance of infection.  Participants may feel discomfort providing a stool 
sample, detailed instructions and research staff will be available to provide support. The stool 
samples are an essential component of the study, and as such are a required component of 
participation.  
 
Participants may also experience psychological and emotional distress when completing 
questionnaires and/ or answering questions about their socio-economic status, their health, their 
infants’ health. In addition, some participants may go through postpartum issues that may impact 
their mental state during the study period. Participants will have the option to leave questions 

 
                         
 
 
 
 
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blank/unanswered if they are uncomfortable, research staff members will be on hand if a 
participant experience’s physiological and/ or emotional distress during a study visit or during 
study procedures. If this occurs, the study staff members will refer and transport the participant 
to the government health care facility. 
 
Participants will also have the option to consent to future genetic testing of their serum samples. 
Genetic information can never be fully de-identified. Although procedures will be put in place to 
de-identify genetic information, there is a risk that the information gained from genetic research 
can be linked to participants. While this is very unlikely at this time, rapid scientific advances 
mean that re-identification may be more likely in the future. There is also a risk of unintentional 
release of information that could lead to loss of privacy and to possible future discrimination 
against participants and/ or their biological relatives. The potential future use of genetic 
information is unknown and therefore not all potential future risks are known.  The potential 
psychological and social risk of participating and receiving genomic information is not fully 
known at this time. It may be upsetting to learn about genetic causes and medically actionable 
findings.  
 
There are no direct benefits to participants in the study. However, all participants will be 
assessed for anemia, and ongoing interaction with the Lady Health Worker Program (LHWs) 
will be promoted. This provides a small benefit to the study population. All study participants 
will benefit from receiving appropriate standard of care, which may be better than the average 
care received in the health system. Participants identified with severe anemia will be referred to 
nearest public health facility for further evaluation and treatment.  
 
The societal benefits of the proposed study are that it is expected to yield insights into the 
relationships between prokaryotic and eukaryotic microbes in the gut and their association with 
maternal health and birth outcomes. The study results are expected to have an impact beyond the 
study setting. The actual risk of harm caused by a non- interventional trial is expected to be low, 
and the potential benefits in terms of knowledge generation outweigh the theoretical risks. 
 
 
3 
STUDY OBJECTIVES 
 
Table 4: Study Objectives 
 
OBJECTIVES 
ENDPOINTS 
JUSTIFICATION FOR 
ENDPOINTS 
Primary 
 
 
To assess if alterations of the 
microbiota in the maternal gut 
(dysbiosis) is associated with maternal 
gestational weight gain.   
The primary endpoint will be 
maternal gestational weight gain 
(GWG) during pregnancy, measured 
between the first (10-14 weeks post-
conception) and second time point 
(30-34 weeks post conception).  
 
To monitor microbiome dynamics, 
16S and 18S rDNA surveys will be 
Weight gain is a key 
predictor of adverse 
birth outcomes[83, 84]. 
 
 

 
                         
 
 
 
 
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OBJECTIVES 
ENDPOINTS 
JUSTIFICATION FOR 
ENDPOINTS 
applied to the maternal stool samples 
to monitor the bacterial and 
eukaryotic components of the 
microbiome. 
To determine the association between 
maternal microbiome dysbiosis during 
pregnancy and birth outcomes, infant 
growth, nutritional status and health 
status in the first year of life. 
The specific birth outcomes evaluated 
will include preterm birth, birth 
weight, SGA, LGA, head 
circumference at birth, mortality, 
morbidity.  
 
The specific infant outcomes 
evaluated will include the following 
growth and nutritional status 
parameters; WHO z-scores for 
weight, length, head circumference 
and MUAC during the first year. 
 
Birth characteristics will be collected, 
including information on vaginal or 
cesarean birth (elective, emergency), 
placental insufficiency and antibiotic 
usage.  
 
Infant morbidity and mortality will be 
assessed at follow-up visits, with 
questions regarding care-seeking, 
hospitalizations and treatments for 
any morbidity; including antibiotic 
usage. 
 
For the infants an assessment of 
amount and mode of feeding will be 
obtained at 3-months, 6-months and 
12-months. 
 
To monitor microbiome dynamics, 
16S and 18S rDNA surveys will be 
applied to the maternal stool samples 
to monitor the bacterial and 
eukaryotic components of the 
microbiome. 
This will allow for an 
examination of the 
association between the 
maternal microbiome 
and birth outcomes.  
Secondary 
 
 
To link the maternal microbiome to 
dietary intake, with a focus on calories 
and macronutrients. 
The maternal participants will 
complete a 24 hour dietary recall at 
both time points during pregnancy 
and at 1-year post-partum.  
 
Quantitative measures 
of nutritional status will 
provide parameters to 
better inform 
microbiome analyses. 

 
                         
 
 
 
 
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OBJECTIVES 
ENDPOINTS 
JUSTIFICATION FOR 
ENDPOINTS 
Food insecurity will be assessed 
through the use of a food insecurity 
questionnaire administered at time of 
recruitment and 3 months post-
partum. 
 
To assess the quality of the diet 
through dietary diversity scores. 
 
To integrate maternal anthropometric 
factors, morbidity and mortality with 
microbiome data to reveal key 
modulators (microbial taxa and 
metabolites) of dietary intake during 
pregnancy and the post-partum period.   
Maternal morbidity will be assessed 
at follow-up visits, with questions 
asked about illness or complications 
during pregnancy, childbirth and the 
post-partum period, care-seeking, 
hospitalizations and treatments for 
any morbidity; including antibiotic 
usage. 
 
Maternal anthropometric 
measurements will include maternal 
height, weight, MUAC, and SFT.  
Integration with clinical 
information will allow 
for the analysis of 
microbiome data in the 
context of patient 
nutritional status and 
health outcomes. 
To determine the impact of the 
maternal microbiome during 
pregnancy, including the exposure to 
pathogens and parasites, on the 
development of the infant 
microbiome. 
Maternal and child stool and serum 
samples will be collected and 
analyzed for microbiome 
composition, micronutrients in the 
serum and stool macronutrients. 
These analyses will 
inform on the role of 
the maternal 
microbiome during 
pregnancy to impact 
the developing child’s 
microbiome. Our 
hypothesis is that 
exposure to pathogens 
and/or parasites during 
pregnancy will 
negatively impact the 
development of the 
child’s microbiome 
with downstream 
consequences for 
nutrient uptake. 
 
To investigate the maternal 
microbiome’s exposure to pathogens 
and parasites, and the association with 
intestinal inflammation. 
The maternal stool samples will be 
examined for markers of intestinal 
mass, inflammation, and gut 
permeability. 
 
 
To monitor the bacterial and 
eukaryotic components of the 
microbiome, 16S and 18S rDNA 
These markers will 
reflect gut health and 
pathogen challenge. 

 
                         
 
 
 
 
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OBJECTIVES 
ENDPOINTS 
JUSTIFICATION FOR 
ENDPOINTS 
surveys will be conducted on the stool 
samples. 
 
Exploratory  
 
 
To explore the role of the maternal gut 
microbiome during pregnancy and to 
identify gut community dynamics in 
pregnant women and how this impacts 
differences between dietary intake and 
nutritional status. 
 
 
To gain more mechanistic insights 
into the relationship between 
microbiome function and maternal 
health and birth outcomes, 
metatranscriptomics, metabolomics 
and markers of inflammation will be 
selectively deployed on the stool 
samples.  
 
The output of these 
analyses are 
concentrations of 
metabolites that are 
expected to correlate 
with pathway 
expression data; linked 
to readouts of microbial 
gene expression 
detailing biochemical 
activity and the taxa 
responsible. 
 
To investigate socio-economic factors; 
including gender, poverty, exclusion 
and empowerment, and their influence 
on the health of a mother’s 
microbiome (assessed by alpha and/or 
beta diversity, and absence of 
pathogens). 
Participants will answer 
questionnaires at two time points: 
baseline and 3-months post-partum. 
The questionnaire will assess gender 
related variables focused on five main 
metrics: perceived maternal self-
efficacy, perceived social support, 
decision making, perceived stress in 
addition to socio-economic factors, 
including food and housing 
insecurity, and demographic data. 
 
 
 
The use of previously 
validated 
questionnaires within 
this population, will 
allow for comparison 
between the Pakistan 
and Toronto cohorts, to 
account for gender 
related variables that 
impact maternal and 
infant gut health. 
To explore the role of the human 
microbiota on nutritional status by 
performing fecal microbiota 
transplants in germ free mice and 
sterile piglets. 
The pregnancy rates, birth outcomes, 
microbiome dynamics, health 
outcomes, host nutritional status, 
inflammation and barrier integrity 
will be collected and assessed.  
 
 
 
Exploiting animal 
models will allow us to 
define causal 
interactions between 
diet, microbiome, 
pathogen exposure and 
nutritional status during 
pregnancy. Through 
recapitulating patient 
phenotypes with fecal 
microbiome 
transplants, our animal 
studies will set the 
stage for developing 
new therapeutic 
strategies that promote 
gut health. 

 
                         
 
 
 
 
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4 
STUDY DESIGN  
 
4.1 
STUDY HYPOTHESIS  
 
The central hypothesis of the study is that alterations of the microbiota in the maternal gut 
(dysbiosis) exacerbated by nutritional status or pathogen exposure during pregnancy, impacts 
weight gain during pregnancy because of impaired nutrient absorption, leading to corresponding 
negative birth outcomes. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4.2 
STUDY DESIGN 
 
The study will be a prospective, longitudinal observational study to investigate the impact and 
relationship between prokaryotic and eukaryotic microbes in the gut and their association with 
maternal health and birth outcomes among young women, 17-24 years of age in Matiari District, 
Pakistan. The study will aim to recruit 400 women into two groups based on BMI at time of 
recruitment (Normal: BMI between 18.5 and 24.9 kg/m2 and Low: BMI less than 18.5 kg/m2, as 
defined by WHO guidelines[85]). Our goal is to recruit 200 participants with normal BMI and 
200 participants with low BMI into the study. However, in the event that we have difficulty 
reaching this recruitment target, we will recruit more women into the normal weight BMI. The 
study will follow women and their infants over the course of their pregnancy and for a year post-
partum, collecting stool and blood samples, nutritional information, heath assessments, 
anthropometric measurements and empowerment metrics at different time points. 
 
4.3 
END OF STUDY DEFINITION 
 
A participant is considered to have completed the study if she has completed all phases of the 
study including the last visit or the last scheduled procedure shown in Section 1.3, Schedule of 
Activities (SoA). The duration of participation for each individual participant will vary depending 
on how long they are in the pregnancy monitoring cohort, upon conception the participants will 
Figure 3: Project Hypothesis. During the course of pregnancy a shift to an ‘unhealthy’ microbiome, perhaps 
triggered by exposure to pathogens, antibiotics or poor diet, results in impaired nutrient absorption, low weight 
gain and poor birth outcomes.  

 
                         
 
 
 
 
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be followed for approximately 17-19 months in duration, from 10-14 weeks into their pregnancy, 
until one year post-partum.  
 
It is estimated that it will take approximately 6 -8 months for the study to reach its recruitment 
target, and approximately 3 years from when the study opens to the final participant study visit. 
 
5 
STUDY POPULATION 
 
This study will examine young, married women, including newlyweds, 17-24 years of age, living 
in Matiari District, Pakistan. We will focus on this younger demographic due to our lack of 
knowledge on the microbiome of young women, and their vulnerability to undernutrition[10]. 
This will be the first systematic investigation of the impact of the microbiome on nutritional 
status during pregnancy in young women and directly aligns with global health initiatives 
focused on this vulnerable cohort.  
 
Upon identification of pregnancy, participants will be screened for eligibility and their pregnancy 
will be confirmed. If the woman is between 10-14 (+ 2 weeks) post-conception she will be 
invited to join the study. If she agrees to participate written informed consent will be obtained. 
Only one woman per household will be eligible to participate in the study. 
 
5.1 
INCLUSION CRITERIA 
 
In order to be eligible to participate in the pregnancy surveillance cohort phase, an individual 
must meet all of the following criteria: 
1. Consent provided 
2. Married female aged 17-24 years 
3. In good general health, with no comorbidities 
4. Absence of COVID19 symptoms  
5. Women who are 14 weeks post-conception or less (+ 2 weeks) 
6. Intend to comply with study procedures and follow up 
 
5.2 
EXCLUSION CRITERIA 
 
An individual who meets any of the following criteria will be excluded from participation in this 
study at the pregnancy surveillance cohort phase of the study: 
1. Women who do not meet the enrollment age criteria  
2. Women participating in interventional clinical trials 
3. Women who intend to leave the study area 
4. Women who cannot comply with study procedure’s and follow-up 
5. Illness and other co-morbidities 
6. Signs of potential COVID19 infection 
7. BMI higher than 24.9 kg/m2 
8. Women who already have a member of their household participating 
9. Women who have taken antibiotics within the past 3 months 
10. Women who are past 16 weeks post- conception 

 
                         
 
 
 
 
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5.3 
STUDY SETTING 
 
This study will be conducted in the rural settings of Matiari District in Sindh province, Pakistan. 
Matiari is situated in North Eastern part of Sindh, about 200 km away from Karachi. Including 
1,418 villages and a population of about 776,542, around 78,000 residents are young women 15-
24 years (based on regular surveillance data). Approximately 20% of these women are married. 
The study setting has a well-established community and health system liaison, basic 
demographic surveillance, and field centers for research. This site has also been selected because 
it has been the location of several large cluster randomized trials, meaning that households are 
well mapped with household level GIS. This district is representative of typical conditions in 
Pakistan, and there is a close working relationship with community, civic society leaders, and 
public health department facilities. Select epidemiological features of the Matiari site are 
highlighted below in (Table 4) and findings to date from the household survey have been 
provided in (Table 5). These tables were taken from the MaPPS trial protocol. The current 
MaPPS baseline household listing data shows that the proportion of young women 15-19 and 20-
24 years each comprise 5% of the population. The MaPPS trial was a prospective, cluster 
randomized evaluation of the effectiveness of supplementation with multiple micronutrients and 
life skills development education provided from preconception on health and birth outcomes 
among young, reproductive-age Pakistani women (15-24 years).  
 
Table 4. Epidemiological information from Matiari district (estimated from regular surveillance 
data) 
Characteristic 
Matiari-specific observations 
Population size  
776,542 
Villages 
1,418 
Households 
112,542  
Number of women 15-45 years of age 
178,993 
Number of women 15-24 years of age 
77,740 
Number of females 15-19 years of age 
39,417 
Mean (±SD) age at 1st pregnancy (years) 
20.3 ± 3.2 
Mean (±SD) age at marriage (years) 
17.7 ± 2.8 
Number births in last year to women 15-45 years of age 
27,178  
Number of births to women 15-24 years of age 
5979 
Number of births to women 15-19 years of  age 
715 
Birth rate (per 1000 population) 
35 
 
Table 5. Key statistics to date from the MaPPS household survey (from data collected up to end of 
March 2017, including population size of 158,019) 
Characteristic 
MaPPS catchment area-specific observations 
15-24 years 
15-19 years 
19-24 years 
Number of women 
17,164 
9,286 
9,054 
Number of pregnant women 
983 
226 
813 
Pregnancy rate (%) 
6 
2 
9 
Ratio of women to pregnancies 
17.5 
41.1 
11.1 
 
 
 
 

 
                         
 
 
 
 
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5.4 
STRATEGIES FOR RECRUITMENT AND RETENTION 
 
This study builds off the MaPPS trial partnership between Aga Khan University in Pakistan and 
Dr. Bhutta at SickKids. Led by Principal Investigator Dr. Bhutta, this trial is an exciting 
partnership between SickKids’ Centre for Global Child Health and the Institute for Global 
Health and Development, AKU. Here we will leverage the clinical network established by the 
MaPPS trial, to extend the trial to recruit hundreds of young mothers to investigate host-
microbiome interactions in pregnancy. 
 
The study will aim to recruit 400 married women into two groups based on BMI at time of 
recruitment  (Normal: BMI between 18.5 and 24.9 kg/m2 and Low: BMI less than 18.5 kg/m2, as 
defined by WHO guidelines[[85]). Our goal is to recruit 200 women with normal BMI and 200 
women with low BMI. However, in the event that we are unable to reach this recruitment target, 
we will recruit more women in the normal BMI category. With an annual birth rate of 2% and 
Matiari Surveillance data revealing proportions of normal and low BMI individuals to be 49.7% 
and 29.8% respectively, we anticipate meeting our target sample size in the first year of the 
study. 
5.4.1 METHOD FOR MONITORING NEW PREGNANCIES 
 
From the MaPPS trial we found that women typically report their pregnancy to study personnel 
near the end of the first trimester or start of their second trimester. Field team shall implement 
multiple strategies to identify new pregnancies: 
 
1) The MaPPS study was implemented in the LHW-covered area within the district, and 
LHWs are required to visit their catchment population once a month. LHWs are required 
to meet with their respective lady health supervisors (LHSs) in the first week of each 
month to report pregnancies and share data on several other indicators. Research field 
staff will join this meeting and get information about new pregnancies.   
2) Research field staff will get contact numbers of women/families and make regular calls 
inquiring as to whether they think that they might be pregnant.  
3) Research field staff will also identify volunteers from within local villages and primary 
health care facilities to notify them about new pregnancies. A communication card of 
Rs.100 will be provided to the volunteer against a valid pregnancy notification. 
4) Research field staff will also employ random check of households and villages and meet 
with LHWs and volunteers in the field to get information on pregnancies.   
 
Once potential pregnancies are identified by LHWs and volunteers, eligible individuals will be 
asked by them to provide verbal consent to have a research staff member contact them about the 
study. If the individual provides this verbal consent to contact, they will be contacted by study 
personnel at their home to confirm their eligibility. This will include the administration of a 
pregnancy test to confirm the pregnancy, questions to determine the gestational age (if the 
getstational age is 16+ weeks the individual will be excluded) and the collection of 
anthropometric measurements to confirm their BMI is equal to or below 24.9 kg/m2. The aim 
will be to recruit participants during the 10-14 week period post-conception, however, women 

 
                         
 
 
 
 
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can be enrolled in the study up to 16 weeks post-conception. Only one participant per household 
can be enrolled in the study. 
5.4.2 OBTAINING INFORMED CONSENT 
 
The investigators at AKU have well established protocols for informed consent and will share 
information about the project with local government leaders, health workers, and members from 
all participating villages, as appropriate.  
 
The study staff will obtain a verbal consent to take the potential participant’s height and weight, 
this will be taken to determine eligibility for the study (BMI below 24.9 kg/m2) and to conduct a 
pregnancy test to confirm pregnancy. If the participant meets the eligibility criteria, the research 
staff member will verbally explain the study to the participant; participation components; 
potential benefits and harms; and obtain informed consent, Women will be asked to consent to 
(1) provide stool samples at four time points during the study, and upon delivery for her infant to 
provide stool samples at 2 times points (2) provide blood samples at 3 time points during the 
study and upon delivery for her infant to provide a blood sample at 1 time point (3) have 
anthropometric measurements conducted, and upon delivery upon her infant as well (4) 
participate in periodic study follow-up including health assessments and study questionnaires, 
and, upon delivery, assessment of both herself and infant. Participants will be provided with 
information about the study both verbally and through a written consent form.  
 
Following an objective assessment of understanding of the project, the women will be invited to 
join the study and to provide written informed consent. In all instances, participants will be 
informed of the right to withdraw from the study without prejudice. 
 
Participants will have the option sign an additional section of the consent form for future genetic 
testing of their blood samples. Participants will have the option to opt out of this future testing. If 
they sign this section of the consent form, the samples will be stored for future genetic testing to 
further dissect patterns of microbiome genotype associated traits.  
5.4.1 STUDY HONOURARIUM 
 
To thank participants for their time and effort in the study, participant’s will be given a thank- 
you gift three times during the study. 1) Non-food items i.e. suit for mother at enrollment 2) baby 
hygiene kit at birth 3) toys for baby at the end of participation. Within the available budget these 
items can be changed if families would prefer other items of equal value.  
 
6 
DISCONTINUATION AND WITHDRAWAL 
 
Participants are free to withdraw from participation in the study at any time upon request. 
An Investigator may discontinue or withdraw a participant from the study for the following 
reasons: 
 
• Withdrawal of informed consent (participant can withdraw for any reason) 

 
                         
 
 
 
 
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• If any clinical Adverse Event (AE), laboratory abnormality, or other medical condition or 
situation occurs such that continued participation in the study would not be in the best 
interest of the participant. 
• Significant study non-compliance  
• If the participant meets an exclusion criterion (either newly developed or not previously 
recognized) that precludes further study participation 
The reason for participant discontinuation or withdrawal from the study will be recorded in the 
study file. The data from participants who are withdrawn or discontinued from the study will be 
used in the analysis unless the participant requests otherwise. 
 
6.1 
LOST TO FOLLOW-UP 
 
A participant will be considered lost to follow-up if they are unable to be contacted by the study 
site staff.  If a participant misses one of the scheduled visits, they will still be included/ contacted 
for future study visits.  
 
The following actions must be taken if an attempt to contact participants at their homes for a 
required study visit fails: 
• The study staff will attempt to contact the participant and reschedule the missed visit and 
counsel the participant on the importance of maintaining the assigned visit schedule and 
ascertain if the participant wishes to and/or should continue in the study. 
• Before a participant is deemed lost to follow-up, the Principal Investigator or designee 
will make every effort to regain contact with the participant, where possible, including 3 
telephone calls and, if necessary, an in-person visit. These contact attempts should be 
documented in the participant’s study file.  
• Should the participant continue to be unreachable, they will be considered to have 
withdrawn from the study with a primary reason of lost to follow-up. 
 
6.2 
REMOVAL OF PARTICIPANTS 
 
Efforts will be made to obtain complete follow-up data from enrolled participants. If a 
participant experiences a serious adverse event, follow-up and scheduled data collection will 
continue to the extent that is possible. However, study activities will be stopped for a participant 
when any one of the following events occurs: 
• Consent for follow-up is withdrawn. 
• Participant moves residences and is lost to follow-up. Loss to follow-up will be 
considered to have occurred if study personnel receive information that the participant 
has moved away and will not return to follow-up at any time during the remainder of the 
study.  
 
 
 

 
                         
 
 
 
 
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7 
STUDY PROCEDURES 
 
7.1 
STUDY VISITS  
7.1.1.1 
 MV1: 10-14 WEEKS POST-CONCEPTION BASELINE ASSESSMENT:  
 
Research staff will administer the screening form. If the participant is eligible, the research staff 
member will obtain written, informed consent. In the case that a participant cannot write her 
name, ink will be used to get an imprint of her left thumb. Once consent is obtained, the 
following forms will be administered: the baseline visit form, 24-hour dietary recall and the 
empowerment questionnaire. The research staff will obtain anthropometric measurements, a 
blood sample and give participants the stool collection kit. Study participants will arrange with 
study field staff a day to collect stool sample, and the field team will facilitate the transport of the 
sample to the field office-based laboratory.   
7.1.1.2 
MV2: 30-34 WEEKS POST-CONCEPTION 
 
During this visit, research staff will administer the 24-hour dietary recall and the maternal 
morbidity and anthropometric assessment form. The research staff will obtain anthropometric 
measurements, a blood sample and give participants the stool collection kit. Study participants 
will arrange with study field staff a day to collect stool sample, and the field team will facilitate 
the transport of the sample to the field office-based laboratory. 
7.1.1.3 
MV3/IV1: POST-PARTUM 24-72 HOURS POST DELIVERY 
 
Study research staff will aim to complete this visit with 24-48 hours after birth but can complete 
the visit up to 72 hours post-delivery. During this visit the research staff will administer the 
maternal morbidity and anthropometric assessment form and the post-partum questionnaire, 
obtain birth characteristics and will obtain infant and maternal anthropometric measurements.  
7.1.1.4 
MV4/IV2: 3 MONTHS POST PARTUM 
 
The research staff will administer the maternal morbidity and anthropometric assessment form, 
the empowerment questionnaire, the dietary diversity questionnaire, and the infant study visit 
form. The research staff will obtain anthropometric measurements from both the mother and 
infant; and will provide the stool sample kit to both. Study participants will arrange with study 
field staff a day to collect stool sample, and the field team will facilitate the transport of the 
sample to the field office-based laboratory.  
 
7.1.1.5 
MV5/IV3: 6 MONTHS POST PARTUM 
 
Study staff will collect infant and maternal anthropometric measurements and will administer the 
maternal morbidity and anthropometric assessment form and the infant study visit form. 
 

 
                         
 
 
 
 
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7.1.1.6 
MV6/IV4: 12 MONTHS POST PARTUM 
 
This will be the final time point for both the mother and infant. The research staff will administer 
the maternal morbidity and anthropometric assessment form and the infant study visit form and 
the 24-hour recall. The research staff will obtain the following from both the mother and infant; 
anthropometric measurements, blood samples and will provide the stool sample kit. Study 
participants will arrange with study field staff a day to collect the stool sample, and the field 
team will facilitate the transport of the sample to the field office-based laboratory. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
                         
 
 
 
 
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7.2 
STUDY ASSESSMENTS AND SAMPLE COLLECTION 
 
Figure 4: Overview of Sampling, Analyses and Surveys 
 

 
                         
 
 
 
 
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7.2.1.1 
BLOOD SAMPLE COLLECTION 
A venous blood specimen of 5 mL will be collected from the mother at enrolment, 30-34 weeks 
post conception and 12-months post-partum. The blood will be divided into two tubes, 0.6 ml in 
SST Serum, for ferritin and CRP, the remaining sample will be collected in EDTA tube for HB 
and MCV, and for storage for future analysis. The infants will have a venous blood specimen of 
3 mL collected into two tubes, 0.6 ml in SST Serum, for ferritin and CRP, the remaining sample 
will be collected in EDTA tube for HB and MCV, and for storage for future analysis at 12 
months.  
 
All blood specimens will be coded to link with an individual subject’s records using a specimen 
ID system. Blood specimens from individuals who did not complete the study will be processed 
and their data analyzed, as possible, for selected outcomes. Blood samples will be analyzed for 
Ferritin, CRP and HB + MCV. Blood samples will be flash frozen and stored at -80oC. Samples 
will be transported to the Nutrition Research Lab at the AKU main campus on dry ice, where 
they will be further stored at -80oC until analysis.  
 
See section 8 for more information on blood sample analysis and refer to the blood sample 
collection SOP. 
7.2.1.2 
STOOL SAMPLE COLLECTION 
 
Stool samples will be requested prior to visits by field staff, with families provided with stool 
container, cold box and ice packs. At data collection visits, study participants will be provided 
with a stool container, cold box, and ice pack to collect the stool sample. They will arrange with 
study field staff a day to collect stool sample, and the field team will facilitate the transport of the 
sample to the field office-based laboratory. 
 
For maternal stool samples, the participants will be asked to hand over freshly passed stool in 
sterile container to field staff. The samples will be transported to the field office lab on ice packs 
and transferred to a -80oC freezer. Samples will be transported to the Nutrition Research Lab at 
the AKU main campus on dry ice, where they will be further stored at -80oC. 
 
For infant stool samples, freshly soiled diapers will be collected from infants and transferred into 
a Ziploc bag and quickly transported to field lab with ice packs (4oC), there with the help of 
sterile applicator two cryovials will be three quarter filled and immediately transferred to -80oC 
freezer. 
 
From the Matiari field lab stored samples will be shipped in batches on dry ice to NRL, AKU. 
They will be stored in a -80 freezer until they are subjected to nucleic acid extraction. The stool 
samples will be coded to link with the individual’s subject records using a specimen ID system.  
 
See section 8 for more information on stool sample analysis and refer to the stool sample SOP. 
 
 
 

 
                         
 
 
 
 
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7.2.1.3 
ANTHROPOMETRIC DATA COLLECTION 
 
Maternal height and weight will be measured using a digital floor scale and stadiometer; MUAC 
will be determined using a measuring tape. A triceps skinfold thickness (SFT) measurement will 
be taken using a skinfold caliper. All measurements will be conducted in duplicate by two study 
personnel using standardized procedures, as specified in the anthropometric data collection SOP. 
The average (mean) of acceptable paired measures will be used in analysis. 
 
Infant anthropometry will include measurement of weight, length, MUAC, and head 
circumference; these measurements will be conducted using standardized procedures: a digital 
weight scale, infantometer, and measuring tape. All infant measurements will be collected in 
duplicate, by two study personnel, using standardized procedures, as specified in the 
anthropometric data collection SOP. The average (mean) of acceptable paired measures will be 
used in analysis. 
 
Refer to the infant and maternal anthropometric measurement SOP. 
7.2.1.4 
QUESTIONNAIRES 
 
For all structured interviews with the study participants, study personnel will use tablet-based 
questionnaires to guide data collection. The information collected will depend on the visit. A 
summary of all the study questionnaires and visit-specific forms that will be used throughout the 
study is provided in Table 2. 
 
The forms will include information on participant’s baseline demographics, anthropometrics and 
health assessments that will collect participant information regarding the participant’s age, 
gender, sex, number of pregnancies, number of children, marital status, the language spoken in 
the home, languages spoken in the home, religion, country of origin, income, # of people this 
income supports, education and housing. The morbidity assessment section of the forms will 
document mortality, morbidity and medication usage.  
 
Participants will fill out an empowerment questionnaire at baseline and 3-months post-partum. 
The five main metrics will be: perceived maternal self-efficacy, perceived social support, 
decision making, perceived stress in addition to socio-economic factors, including food and 
housing insecurity, and demographic data. These metrics align with major social constructs 
contributing to infant feeding practices and suspected microbiome health (Figure 4). These 
constructs will be measured using validated questionnaires that have been previously translated 
and utilized with this study population. Questions pertaining to perceived decision-making, and 
demographic data including maternal education, income, housing and gender norms are adapted 
from the Pakistan Demographic and Health Survey (PDHS)[86]; food insecurity will be assessed 
using the Household Food Insecurity Access Scale (HFIAS)[87]. Self-efficacy will be measured 
using the Generalized Self-Efficacy scale, developed by Schwarzer and Jerusalem[88]. Perceived 
social support will be measured using the Multi-dimensional Scale of Perceived Social Support 
(MSPSS), developed by Zimet and colleagues[89]. Perceived parental stress will be measured 
using the Perceived Stress Scale (PSS-10)[90].  
 

 
                         
 
 
 
 
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The birth and labour history questionnaire will be administered at the post-conception visit, 24-
72 hours post-partum and will gather additional information including: birth characteristics 
(vaginal/cesarean section), gestational age, birthweight and other measurements at birth (head 
circumference and length), placental insufficiency, antibiotic use, among other birth 
characteristics.   
 
For the infant, the study forms will assess breastfeeding and infant feeding as well as a health 
assessment’s to assess infant mortality, morbidity and medication usage. 
7.2.1.5 
DIETARY RECALLS AND INFANT FEEDING PRACTICES 
 
To link the microbiome to nutritional status and nutritional intake, with a focus on calories and 
macronutrients, a semi-quantitative 24-hour paper-based dietary recall will take place at both time-
points during pregnancy and at 1-year post-partum. This will be done based on an interactive 24-
hour recall method[91], that will be administered by the research staff, and aided with a food kit.  
 
At the 3 months post-partum visit, maternal participants will complete a dietary diversity 
questionnaire using the Minimum Dietary Diversity Score for Women (MDD-W). The MDD-W 
is a population level indicator for dietary diversity for women aged 15-49, based on 10 food 
groups[92]. The MDD-W will reflect what they have eaten over the previous 24 hours, and they 
will be asked at the end of the questionnaire whether this reflects their diet over the previous 3 
months. The research team will calculate the MDD-W from the dietary recalls completed at 
baseline, 30-34 weeks post-conception and at 12 months. 
 
For the infant, an assessment of initiation, amount and mode of breastfeeding, as well as an 
assessment of young child feeding, including questions to address the introduction of solid, semi-
solid and soft foods, the minimum dietary diversity, meal frequency and acceptable diet will be 
administered at 3 and 6 months and 1 year post-partum. The questionnaire will be designed based 
on the guidance developed by the WHO in 2010, in the “Indicators for assessing infant and young 
child feeding practices (Part 2 Measurement)”[93] 
 
At the 12 month visit, the research staff will administer the NutricheQ questionnaire, a tool 
designed for toddlers aged 1 to 3 years of age, with a focus on markers for inadequate or excessive 
intake and dietary imbalances[94]. The mother and infant will both answer two food insecurity 
questions, that will assess their annual food insecurity[87].  
 
7.3 
CONTACT WITH STUDY PARTICIPANTS 
 
The majority of the study visits will occur at the participants’ homes. The expected duration of 
visits is detailed in the table below. 
 
Table 7: Overview of Study Visits and Expected Visit Duration 
Visit  
Description of Visit 
Visit Duration 
MV1: Baseline post-conception 
visit 10-14 weeks post-
conception 
Confirmation of eligibility and 
written informed consent to 
participate in the study will be 
conducted. The participant will 
~1.5-2 hours 

 
                         
 
 
 
 
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complete the study forms 
specified in table 2, the 24-hour 
paper-based dietary recall, 
anthropometry, fecal sample and 
blood draw. 
MV2: 30-34 weeks post 
conception 
The participant will complete 
the study forms specified in 
table 2, maternal anthropometric 
measurements will be collected. 
The maternal participant will 
complete a 24-hour dietary 
recall and a stool and blood 
sample will be collected.  
1-1.5 hours 
MV3/IV1: 24-72 Hours post-
delivery 
The participant will complete 
the study forms specified in 
table 2, infant and maternal 
anthropometric measurements 
will be collected. 
20-30 minutes 
MV4/IV2: 3-Months post-
partum 
The participant will complete 
the study forms specified in 
table 2, infant and maternal 
anthropometric measurements 
will be collected. 
 
A stool sample will be collected 
from both the mother and infant. 
1-1.5 hours 
MV5/IV3: 6- Months post-
partum 
The participant will complete 
the study forms specified in 
table 2, infant and maternal 
anthropometric measurements 
will be collected. 
30-40 minutes 
MV6/IV4: 12-Months post-
partum 
The participant will complete 
the study forms specified in 
table 2, infant and maternal 
anthropometric measurements 
will be collected. 
 
The maternal participant will 
complete a 24-hour dietary 
recall. A stool and blood sample 
will be collected from both the 
mother and infant. 
1-1.5 hours 
 
7.4 
SAFETY AND ADVERSE EVENTS 
 
Safety will be monitored by study personnel tri-monthly throughout the study progression. This 
includes, but is not limited to, death, hospitalizations, miscarriages, abortions, stillbirths, and 
maternal and newborn deaths.  
 

 
                         
 
 
 
 
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Although study-related serious adverse events and adverse events are not anticipated, the 
participants may encounter pregnancy-related or other unrelated adverse events. These events 
will be recorded in the form: AE-10 Adverse Event Form. Women who experience clinical events 
during study participation will be referred to the appropriate public health facility. Adverse 
events will be reported to the AKU and Hospital for Sick Children REBs according to their 
respective institutional requirements.  
 
Serious adverse events include the following events, irrespective of their association with study 
procedures: 
• Death 
• Life-threatening complication, such that death was averted by medical or surgical 
interventions (the term "life-threatening" in this context refers to an event in which the 
patient was at risk of death at the time of the event; it does not refer to an event, which 
hypothetically might have cause death, had it been more severe) 
• Inpatient hospitalization or prolongation of an existing hospitalization 
• Persistent or significant disability/incapacity following the resolution of the acute event. 
• Reporting of certain events (e.g., suspected child abuse) is mandatory because of the 
study population. 
 
7.4.1 REPORTING EVENTS TO PARTICIPANTS  
 
Participants will be informed in a timely manner of any new information, including safety 
information, that is relevant to that participant’s willingness to continue participation. The 
communication of this information will be documented through a revised REB approved 
Informed Consent Form, where possible, based on the timeliness of the information. 
 
In the event that a study procedure detects a new clinically important secondary 
finding/incidental finding, the qualified physician will notify the Most Responsible Physician 
(MRP) at AKU or request the participant’s family doctor’s name and contact information in 
order to arrange medical follow-up to interpret the significance of the findings. 
 
In the event of discovering a medically actionable incidental finding, or if any new clinically 
important information about the participant’s health is obtained as a result of participation in the 
study and where a second sample is already available, the test result will be validated clinically 
in an accredited laboratory. For this study, the presence of a pathogen might be identified, in the 
event of this finding, the follow up would include clinical microbiology. The qualified physician 
will work with the participant, their family physician and MRP at AKU to arrange referral to the 
appropriate specialist as needed.  
 
 
 
 
 
 
 

 
                         
 
 
 
 
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8 
STOOL AND BLOOD SAMPLE ANALYSIS  
 
  
Time-points 
Blood 
Stool 
Hemoglobin 
Mother 
Child 
Mother 
Child 
Mother 
Child 
MV1: 10-14 Weeks Post-Conception 
yes 
- 
yes 
- 
yes 
- 
MV2: 30-34 Weeks Post- Conception 
yes 
- 
yes 
- 
yes 
- 
MV4/ IV2: 3-months post-partum 
- 
- 
yes 
yes 
- 
- 
MV6/ IV4: 12-Months Post-Partum 
yes 
yes 
yes 
yes 
yes 
- 
Overview of Sample Collection Time Points  
 
Sample type 
Times 
Mother Spot Hemoglobin 
X 3 
Mother Blood Samples 
X 3 
Mother Stool Samples 
X 4 
Child Blood Samples 
X 1 
Child Stool Samples 
X 2 
Overview of Collection of Sample Types  
 
 
Assay 
Sample size 
Subject 
Whole blood 
Hemoglobin + MCV 
400 X 4 
3 X Mother + 1 X child 
Hemoglobin 
400 X 3 
Mother 
Serum 
CRP 
400 X 4 
3 X Mother + 1 X child 
Ferritin 
400 X 4 
3 X Mother + 1 X child 
 
Stool 
 
Extraction of DNA 
400 X 6 
4 X Mother + 2 X child 
Extraction of RNA 
400 X 2 
2 X Mother 
Calprotectin 
200 x 2 
2 X 200 Mothers 
Lipocalin 
200 x 2 
2 X 200 Mothers 
Claudin 15 
200 x 2 
2 X 200 Mothers 
Overview of Sample Analysis to be Conducted at AKU 
  
8.1 
PROFILING MICROBIAL COMMUNITY STRUCTURE AND FUNCTION 
 
8.1.1 STOOL SAMPLE PROCESSING  
 
Stool samples will be extracted for DNA and RNA; maternal stool samples from all 4 time points 

 
                         
 
 
 
 
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(M1, M2, M4 and M6) will be extracted for DNA and infant stool samples from both time points 
(IV2 and IV4) will be extracted for DNA. RNA will be extracted from the maternal stool samples 
collected during the two pregnancy visits (MV1 and MV2). Stool will be stored at AKU in a -80 
freezer until analysis. Stool samples will be analyzed for inflammatory markers at AKU. The stool 
samples and extracted DNA and RNA will then be batch shipped to the Parkinson lab on dry ice 
for downstream analysis. The stool samples will be biobanked at SickKids.  
 
8.1.2 PROFILING MICROBIAL COMMUNITY STRUCTURE  
 
Microbial communities will be analyzed through 16S and 18S rDNA surveys using established 
methods that target the V4 region of the 16S rRNA gene to capture bacterial taxa [95-97] and the 
V4V5 region of the 18S rRNA gene to capture eukaryotic taxa. DNA library preps will include 
error-correcting barcodes[98] for multiplexing of samples. Sequencing will be performed to 
generate ~50,000 2x300bp paired end reads generated per sample. To define taxonomic diversity, 
species profiles from 16S and 18S rDNA data will be clustered to identify differences in 
community structure across samples. We will utilize the QIIME2 platform[99], MOTHUR[100], 
multivariate approaches such as Permutation Multivariate Analysis of Variance (i.e. 
PERMANOVA-S a method that can associate microbiome changes with outcome measures while 
accounting for confounders[101]) among other state of the art tools. Differences between groups 
in microbiome community structure will be tested by analysis of similarities (ANOSIM) and co-
occurrence analysis[102]. To better define bacterial pathogen burden, we will apply TaqMan array 
card technology for the simultaneous detection of 19 common enteropathogens[103]. 
 
8.1.3 PROFILING MICROBIAL COMMUNITY FUNCTION 
 
After total RNA extraction and rRNA depletion (RiboZero Gold Kit, Illumina, San Diego, Ca, or 
equivalent), libraries will be constructed and Illumina-based sequencing will be performed to 
generate ~30 million 2x150bp paired end reads per sample (our rarefaction analyses have 
previously shown such sequencing depth is sufficient to identify the vast majority of species and 
enzymes present in the samples[75]). Reads will be processed for quality and contaminants using 
established pipelines (e.g. [66, 68, 104]). Reads will be assembled using appropriate state of the 
art tools such as SPAdes[105] and subsequently annotated with taxonomic and functional 
assignments Expression will be normalized to Reads per Kilobase of transcript per million 
mapped reads. Annotations are mapped onto biochemical pathways and complexes such as those 
defined by the Kyoto Encyclopedia of Genes and Genomes [104]. The output of these analyses 
are readouts of microbial gene expression detailing biochemical activities as well as the taxa 
responsible. 
 
8.1.4 PROFILING OF PATIENT AND MICROBIOME METABOLITES AND 
INFLAMMATORY MARKERS 
 
To gain more mechanistic insights into the relationship between microbiome function and 
maternal health and birth outcomes, we will deploy metatranscriptomics, metabolomics and 
markers of inflammation. 
 
We will obtain host readouts that reflect gut health and pathogen challenge, by examining 
markers in the stool for Calprotectin, Lipocalin and Claudin 15. These markers will be examined 

 
                         
 
 
 
 
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in the maternal stool samples collected during the 2 pregnancy visits (MV1 and MV2) for 200 of 
the participants. We will target this analysis at the 200 participants representing the two extremes 
of weight gain; the 100 participants with the highest and 100 participants with the lowest WHZ 
scores. 
 
Stool samples will be sent from SickKids to The Metabolomics Innovation Centre (TMIC) for 
analysis of general metabolites (e.g. SCFAs, amino acids, intermediates in glycolysis and 
nucleotide metabolism, among other metabolites), using the TMIC Prime Metabolomics 
platform[105]. This analysis will be targeted at the maternal stool samples collected during 
pregnancy (MV1 and MV2); with a focus on the 200 participants at the two extremes of weight 
gain: the 100 participants with the highest and 100 participants with the lowest WHZ scores. 
 
Blood samples will be analyzed at AKU for Hemoglobin + MCV, Ferritin, and CRP. Blood 
samples will be shipped on dry ice to SickKids and then to the TMIC Centre, where they will be 
analyzed for Metallomics using the TMIC Metallomics platform[106]. The output of these 
analyses will be concentrations of metabolites that are expected to correlate with pathway 
expression data. Blood samples will be biobanked at SickKids. 
 
Blood proteomics will allow for a deeper investigation into the relationship between the maternal 
microbiome and maternal weight gain and the effects on offspring. Proteomics will be conducted 
at the Sickkids SPARC (Sickkids Proteomics, Analytics, Robotics and Chemical Biology 
Centre)[107] 
 
8.2 
POTENTIAL PROBLEMS, MITIGATIONS AND EXPECTED RESULTS 
 
We and others have already established the feasibility of obtaining DNA, proteomics and 
metabolomics datasets from LMIC settings (e.g.[4-6, 76, 108]). With the outstanding facilities 
available at AKU, we are therefore confident we will generate these datasets. In the unlikely event 
we find RNA yields are poor, we will revert to performing whole shotgun DNA sequencing 
(metagenomics), which also has the capacity to provide functional insights.  
 
Our expectation is that: 1) study participants exhibiting positive maternal health and birth 
outcomes will be associated with more diverse communities than those exhibiting poor maternal 
health and/or birth outcomes; 2) pregnancies associated with poor nutritional status at the start of 
pregnancy, will exhibit a greater incidence of dysbiosis (e.g. loss of microbial diversity and 
decrease in otherwise dominant stool taxa e.g. Firmicutes and/or Bacteroidetes) throughout 
pregnancy; 3) pregnancies associated with pathogen exposure will result in dysbiosis; and 4) 
poor nutritional health will correlate with altered fecal metabolite profiles and changes in the 
expression of metabolic pathway enzymes encoded by the microbiome. Since analysis of 
microbiome data is a rapidly evolving field, to ensure data analysis is in line with best practices, 
we will adopt current state of the art bioinformatics tools for sequence processing, assembly, 
annotation and analysis. In case of delays or other issues that may be experienced at the 
Metabolomics Innovation Centre, we may rely on the SPARC Biocentre, hosted by the Hospital 
for Sick Children, to perform equivalent analyses. 
 
As previous[39], we may expect specific taxa (prokaryotic or eukaryotic) will directly correlate 

 
                         
 
 
 
 
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with nutritional status during pregnancy and birth outcomes. The core deliverables will be an 
unprecedented view of dynamic changes in microbial communities that contribute to nutritional 
status during pregnancy, together with the prediction of biochemical pathways and taxa 
responsible. 
 
9 
STATISTICAL CONSIDERATIONS  
 
9.1 
POWER CALCULATION 
 
The adequacy of the sample size was verified using the ‘pwr’ package (version 1.2-2) in R 
(version 3.6.1). Calculations were based on the correlation between α-diversity (Shannon index) 
and weight gain during pregnancy. Assuming 400 participants are recruited into the study, and a 
type I error rate of 0.05, there will be 80% power to detect a correlation coefficient (r) >0.14. 
This is conventionally considered a small effect size[109]. Thus, we expect to be powered to 
reveal significant association between weight gain during pregnancy and diversity. Further, a 
study of 123 lean and 24 obese individuals, revealed 96% power to detect differences in 
Bacteroidetes abundance and 80% power for Firmicutes[110], while a cohort of 44 Malawian 
malnourished children was sufficient to detect significant differences in bacterial α-diversity in 
the presence and absence of Blastocystis.  
 
9.2 
SUBGROUP ANALYSIS 
 
To reduce study complexity and to avoid confounding effects due to micronutrient deficiencies, 
we will focus specifically on interactions between macronutrient deficiencies with host 
microbiome and health. Given the high prevalence of micronutrient deficiencies experienced 
within the study population, the effect of micronutrient deficiencies of public health importance 
(i.e. anemia, iron deficiency, iron deficiency anemia, vitamin A deficiency and vitamin D 
deficiency) will be explored within a subgroup analysis. We will achieve this by monitoring for 
anemia as well as serum mineral content when participants are recruited into the study.  
 
9.3 
STATISTICAL ANALYSIS 
 
We will work with the research core in Calgary to apply multivariate analyses and other 
integrative methods to analyze microbiome data in the context of patient nutritional status and 
health outcomes. Our initial strategy is to apply the Similarity Network Fusion framework[111] 
to combine the various datatypes (i.e. metabolomics, microbiome, and clinical). In this approach 
networks are first constructed from each datatype, with nodes representing patients and links 
representing similarities between participants (e.g. based on correlations between microbiome or 
metabolite profiles). Networks are then fused using a method based on message-passing theory 
to identify links shared between patients supported by multiple datatypes, eliminating poorly 
supported links and strengthening links supported by multiple datatypes. This allows the 
integration of all available datasets to uncover their global substructures that can be associated to 
relevant outcomes. In an alternative approach, we will also employ Random Forests to identify 
combinations of predictors (microbial and/or metabolic factors) that best correlate with our 
clinical outcomes. Beyond integrative methods we will also apply simple statistical tests (e.g. 

 
                         
 
 
 
 
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Wilcoxon, t-test, Spearman’s rank correlation, PERMANOVA) to examine pairwise interactions 
(e.g. presence of pathogens vs. bacterial diversity and/or abundance of specific taxa; metabolic 
pathway expression vs. inflammation; and stool metabolites vs. serum metabolites). Further, we 
will also attempt to include additional quantitative measures of nutritional status as well as 
gender-related variables from the empowerment questionnaires, as covariates where appropriate. 
These analyses will reveal relationships between taxonomic composition, microbiome diversity 
(both prokaryotic and eukaryotic), metabolomics and the primary and secondary outcome 
measures gathered. 
 
9.4 
PLAN FOR STATISTICAL ANALYSIS 
 
The following details the plans for evaluating the aims of this study. Table 8 lists the variables to 
be considered in the analyses. 
Table 8. List of variables collected in the study to be utilized in analysis 
Clinical Variables 
1. 
Maternal BMI, MUAC and SFT (continuous variables) 
2. 
Maternal gestational weight gain (continuous) 
3. 
Maternal markers of inflammation (continuous) 
4. 
Infant sex 
5. 
Infant morbidity and mortality 
6. 
Infant weight, height, head circumference (continuous) 
7. 
Infant gestational age  
8. 
Infant birth weight, Kg (continuous) 
9. 
Breastfeeding duration (continuous) 
10. 
Maternal age years 
11. 
Maternal use of antibiotics 
12. 
Infant use of antibiotics 
13. 
Maternal calorie and macronutrient intake 
14. 
Maternal incidence of pathobionts 
15. 
Infant incidence of pathobionts 
Microbiome Variables 
1. 
Maternal gut bacteria profile 
2. 
Maternal eukaryotic microbe profile 
3. 
Infant gut bacteria profile 
4. 
Infant eukaryotic microbe profile 
5. 
Maternal metabolomic profile of stool 
6. 
Maternal metabolic pathway expression profile 
7. 
Maternal bacterial gene expression profile 
 
Gender-related Variables 
1. 
Food insecurity 
2. 
Self-efficacy 

 
                         
 
 
 
 
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3. 
Perceived decision making 
4. 
Perceived social support 
5. 
Perceived parental stress 
 
 
9.4.1 ADDRESSING THE PRIMARY OBJECTIVES 
This study has two primary objectives: 1) To assess if alterations of the microbiota in the 
maternal gut (dysbiosis) are associated with maternal gestational weight gain; and 2) To 
determine the association between maternal microbiome dysbiosis during pregnancy and birth 
outcomes, infant growth, nutritional status and health status in the first year of life. 
The first objective will be addressed through the following approaches. First, maternal gut 
bacteria and eukaryotic profiles will be used to calculate Bray-Curtis dissimilarity metrics 
between individual samples which will be leveraged in principal co-ordinate analyses to 
determine the extent samples collected at the first or third trimester, exhibiting similar gestational 
weight gains, co-cluster. Permutational multivariate analysis of variance (PERMANOVA) tests 
will assess the degree of overlap between samples exhibiting low gestational weight gain versus 
samples exhibiting high gestational weight gain. A lack of clustering in the first trimester, 
followed by clustering of samples on the basis of similar gestational weight gain would indicate 
that alterations in the maternal microbiota during the course of pregnancy, impacts gestational 
weight gain. Next, we will attempt to correlate changes in the alpha diversity (as measured by the 
Shannon and Simpson indices) of the gut microbiome samples between the first and third 
trimester, with gestational weight gain. A decrease in alpha diversity (indicative of dysbiosis) 
associated with low gestational weight gain, would again indicate that dysbiosis is associated 
with maternal weight gain. To examine the influence of individual taxa on gestational weight 
gain, we will perform bivariate analyses that examine the correlation (Pearson, Spearman) of 
each taxon with gestational weight gain. Correlations will examine taxon abundance in first and 
third trimester, in addition to the change in abundance between the two trimesters. The 
Benjamini-Hochberg procedure will be applied to correct p-values while controlling for false-
discovery rates. 
To complement these analyses, we will also undertake an integrative modeling strategy based on 
Similarity Network Fusion framework[112] to analyze the contribution of each variable (clinical, 
microbiome and gender-related) on gestational weight gain. In this approach we construct a 
series of networks (one for each variable) in which nodes represent patients and links represent 
similarities between patients (e.g. based on correlations between e.g. microbiome or metabolite 
profiles). Networks are then fused using a method based on message-passing theory to identify 
links shared between patients supported by multiple datatypes, eliminating poorly supported 
links and strengthening links supported by multiple datatypes. This allows the integration of all 
available datasets to uncover their global substructures that can be associated with gestational 
weight gain. In an alternative approach, we will also employ Random Forests to identify 

 
                         
 
 
 
 
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combinations of variables (clinical, microbiome and gender-based; Table 8) that correlate with 
gestational weight gain. 
 
9.4.2 ADDRESSING THE SECONDARY OBJECTIVES 
This study has the following secondary objectives: 1) To link the maternal microbiome to dietary 
intake, with a focus on calories and macronutrients; 2) To integrate maternal anthropometric 
factors, morbidity and mortality with microbiome data to reveal key modulators (microbial taxa 
and metabolites) of dietary intake during pregnancy and the post-partum period; 3) To determine 
the impact of the maternal microbiome during pregnancy, including the exposure to pathogens 
and parasites, on the development of the infant microbiome; and 4) To investigate the maternal 
microbiome’s exposure to pathogens and parasites, and the association with intestinal 
inflammation. 
To assess these secondary objectives, we will employ general linear models. In a first approach 
we will perform PERMANOVA and probe the association of microbial community structure 
with the clinical variables. Pair-wise differences in microbiome structure between patient 
samples at similar time-points are defined by abundance-weighted pair-wise differences using 
the Bray-Curtis dissimilarity metric. DESeq2, a method for differential analysis of count data, 
will subsequently be applied to investigate both associations of specific taxa with the clinical 
variables, together with the strength of those associations. DESeq2 will utilize default settings, 
and q-values calculated with the Benjamini-Hochberg procedure to correct p-values while 
controlling for false-discovery rates. These analyses will reveal which clinical variables 
(including exposure to pathogens and parasites) correlate with the maternal microbiome from a 
taxonomic perspective. 
To probe the association of clinical variables with the function of the microbiome, 
PERMANOVA and DESeq2 analyses will be performed replacing microbial community 
structure metrics with microbial community function metrics, specifically: 1) metatranscriptomic 
profiles; and 2) stool metabolomic profiles. Profile differences will be defined through Spearman 
rank differences for metatranscriptomic data and Euclidean distance metrics for metabolomic 
profiles. These analyses will reveal microbial genes, metabolites and pathways that correlate 
with clinical variables. 
 
9.4.3 ADDRESSING THE EXPLORATORY OBJECTIVES 
This study has the following exploratory objectives: 1) To explore the role of the maternal gut 
microbiome during pregnancy and to identify gut community dynamics in pregnant women and 
how this impacts differences between dietary intake and nutritional status; 2) To investigate 
socio-economic factors; including gender, poverty, exclusion and empowerment, and their 
influence on the health of a mother’s microbiome (assessed by alpha and/or beta diversity, and 
absence of pathogens); 3) To explore the role of the human microbiota on nutritional status by 
performing fecal microbiota transplants in germ free mice and sterile piglets. 

 
                         
 
 
 
 
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For the first exploratory objective, we will apply the same statistical framework as we utilized 
for the secondary objectives here, however, we are concerned with changes in the microbiome 
within a patient from the first to third trimester. Consequently, microbiome structural and 
functional profiles will be generated from the difference in: 1) taxonomic abundances; 2) gene 
expression; and 3) metabolite concentrations, between the first and third trimesters. Profile 
differences will then be utilized in the PERMANOVA and DESeq2 approaches as described 
above to identify associations between clinical variables and changes in microbiome structure 
and function. 
 
9.5 
FUTURE USE OF STORED SPECIMENS AND DATA 
 
Data collected for this study will be analyzed and stored at AKU and SickKids. After the study is 
completed, the de-identified, archived data will be transmitted to and stored at SickKids. 
Permission to transmit data to SickKids will be included in the informed consent.  
 
With the participant’s approval and as approved by the local Research Ethics Boards (REBs), de-
identified biological samples will be biobanked at SickKids.  Samples will be analyzed at AKU, 
SickKids and sent to research collaborators outside Pakistan. Samples will not be sold. These 
samples will be used to research the causal relationships between microbiome dynamics, 
pathogen exposure, nutritional status during pregnancy, undernutrition, its complications and 
other conditions for which study participants are at increased risk, and to improve treatment. 
SickKids will also be provided with a code-link that will allow for the linking of the biological 
specimens with the phenotypic data from each participant, maintaining the blinding of the 
identity of the participant. 
 
Established animal models will be used to study the casual relationships between microbiome 
dynamics, pathogen exposure and nutritional status during pregnancy and to examine whether 
manipulation of the microbiome can improve nutritional status. The gnotobiotic facilities are 
located at the University of Toronto and the University of Alberta. Human fecal microbiome 
transplants (FMTs) have been effectively applied to animals to study the role of human 
microbiota on nutritional status. Here we will perform FMT into germ-free mice and sterile 
piglets to first, recapitulate the disease-associated phenotypes in animals and second, to set the 
stage for therapeutic intervention strategies. We will investigate 10 patient microbiomes: 
correlated with positive and negative nutritional status, as indicated by primary and secondary 
outcomes. This is consistent with previous animal studies that identified key taxonomic 
associations with undernutrition[4]. Extracted microbes will also be sent to our collaborator Dr. 
Michael Grigg, the Chief of Molecular Parasitology at the NIH, Bethesda, and an expert at host-
parasite interactions. Dr. Grigg will oversee mouse experiments with the extracted microbes to 
investigate the parasite-microbiome interactions. Extracted microbes will also be sent to 
Dalhousie to investigate parasite-microbiome interactions.  
 
In addition to publishing findings in open access journals, we will ensure all sequences and 
metabolomics datasets are deposited in appropriate public repositories. SOPs, pathogen samples 
and statistical methods developed through this project will be shared with the IMPACTT 
research core. 

 
                         
 
 
 
 
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9.5.1 FUTURE GENETIC TESTING  
 
Participants will have the option to sign an additional consent form for future genetic testing of 
their blood samples and their infant’s blood samples. Participants will have the option to opt out 
of this future testing, or to have genetic testing on their own samples but not on their infant’s. If 
they sign the consent form, the samples will be stored for future genetic testing to further dissect 
patterns of microbiome genotype associated traits.  
 
The focus of the current application is on the investigation of interactions between the bacterial 
and eukaryotic components of the microbiome and their downstream impact on host nutritional 
status. However, with the availability of host DNA, opportunities will exist to examine the 
interactions between host genetic factors and gut microbial communities. For example, defects in 
these interactions can result in imbalances in the microbial community, such as the loss of 
beneficial commensal microbes and also lead to impaired pathogen clearance[113]. In our own 
studies applying metatranscriptomics to fecal samples from mice, we showed that knockout of 
the gene encoding Perilipin-2, a protein involved in lipid absorption, in mice results in significant 
shifts in the expression of microbial genes involved in energy production [75]. Another study 
focused on gene-microbiome interactions in healthy individuals used genotyping to identify 58 
single nucleotide polymorphisms associated with the relative abundance of 33 taxa in their gut 
microbiomes[114]. Although not funded as part of this study, we envision that the availability of 
DNA samples from the mothers and children enrolled in this study, provides an unparalleled 
opportunity to apply whole genome sequencing or targeted resequencing methodology, to 
examine how the interactions between host genetic factors and the gut microbiome contribute to 
host nutritional status and birth outcomes.  Such studies would form the basis of future grant 
applications. 
 
During the conduct of the study, an individual participant can choose to withdraw consent to 
have biological specimens stored for future research. However, withdrawal of consent with 
regard to biosample storage may not be possible after the study is completed.  
 
 
10 SUPPORTING DOCUMENTATION AND OPERATIONAL CONSIDERATIONS 
 
10.1 REGULATORY, ETHICAL, AND STUDY OVERSIGHT CONSIDERATIONS 
 
Research ethics committee/institutional review board approval will be obtained from the Aga 
Khan University, The National Bioethics Committee in Pakistan and the Hospital for Sick 
Children. All standard procedures related to consent will be followed. 
 
Informed consent will be obtained from all study participants. The trial will be verbally 
explained to participants by study personnel, with formal consent being taken following a 
standardized interview with the team supervisor/medical officer to ensure prospective 
participants’ understanding of the project and its implications. Written informed consent will 
then be obtained and participants will be informed that they have the right to withdraw from the 

 
                         
 
 
 
 
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study at any time without any penalty in terms of care. Withdrawal from the study or refusal to 
participate will not in any way affect their ability to receive any services offered to the 
community by the study. All signed informed consent forms will be retained in the study files 
and a copy supplied to the Ethics Review Committee of AKU. 
 
10.2 STUDY DISCONTINUATION AND CLOSURE 
 
This study may be temporarily suspended or prematurely terminated if there is sufficient 
reasonable cause.  Written notification, documenting the reason for study suspension or 
termination, will be provided by the suspending or terminating party to study participants, the 
Investigator, the CIHR, and regulatory authorities.  If the study is prematurely terminated or 
suspended, the PI will promptly inform study participants and the REB and will provide the 
reason(s) for the termination or suspension.  Study participants will be contacted, as applicable, 
and be informed of changes to the study visit schedule. 
  
Circumstances that may warrant termination or suspension include, but are not limited to: 
• Determination of unexpected, significant, or unacceptable risk to participants 
• Insufficient compliance to protocol requirements 
• Data that is not sufficiently complete and/or evaluable 
 
Study may resume once concerns about safety, protocol compliance, and data quality are 
addressed, and satisfy the PI and REB 
 
10.3 CONFIDENTIALITY AND PRIVACY 
 
Confidentiality of all the data collected from the population will be guaranteed through existing 
systems of data storage and de-identified of forms. Participant privacy and confidentiality in 
electronic and printed data, publications, and reports during and following completion of the 
study will be fully ensured. The field site offices and central storage facilities at AKU are fully 
secure with electronic access and monitoring around the clock.   
 
Participant confidentiality and privacy is strictly held in trust by the participating Investigators 
and their staff. This confidentiality is extended to cover testing of biological samples and genetic 
tests in addition to the clinical information relating to participants. Therefore, the study protocol, 
documentation, data, and all other information generated will be held in strict confidence. Any 
research information obtained about the patient in this study will be kept confidential. A patient 
will not be identified by name, only by unique study ID number. The patient’s name or any 
identifying information will not appear in any reports published as a result of this study.  
 
All research activities will be conducted in as private a setting as possible. 
 
The study auditor, authorized representatives of CIHR, and representatives of the Research 
Ethics Board (REB), may inspect all documents and records required to be maintained by the 
Investigator, including but not limited to, medical records for the participants in this study. The 
clinical study site will permit access to such records. 

 
                         
 
 
 
 
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Study participant research data, for the purposes of statistical analysis and scientific reporting, 
will be transmitted to and stored at SickKids. This will not include the participant’s contact or 
identifying information. Rather, individual participants and their research data will be identified 
by a unique study identification number. The study data entry and study management systems 
used by clinical sites and by SickKids research staff will be secured and password protected. At 
the end of the study, all study databases will be de-identified and archived at SickKids. 
 
 
10.4 QUALITY ASSURANCE AND QUALITY CONTROL 
 
To ensure proper implementation the field, supervisors will make spot checks and will arrange 
refresher group sessions for the study personnel, in which the problems encountered will be 
discussed and resolved. In addition, the data collection activity will be further monitored by field 
supervisors who will perform a check on a subset (5%) of households. These quality checks 
would occur at least once per month for LHWs and research staff. Similarly, the quality of all 
laboratory procedures will be assessed by periodically rechecking results and ensuring the 
standardization of procedures. The Micronutrient Research Laboratories at AKU has a quality 
assurance system in place in collaboration with the biochemistry laboratories of icddr,b and with 
the Q-Rad system in UK. 
 
10.4.1 DATA HANDLING AND RECORD KEEPING  
 
Each participating site will maintain appropriate medical and research records for this trial, in 
compliance with ICH GCP and regulatory and institutional requirements for the protection of 
confidentiality of participants.  
 
10.4.1.1 DATA COLLECTION AND MANAGEMENT RESPONSIBILITIES  
 
Data collection is the responsibility of the clinical trial staff at the site under the supervision of 
the site Investigator. The Investigator is responsible for ensuring the accuracy, completeness, 
legibility, and timeliness of the data reported. All source documents should be completed in a 
neat, legible manner to ensure accurate interpretation of data.   
 
Data collection tablets are collected from the study personnel on a daily basis so that the 
questionnaire data can be uploaded to the AKU data management unit. The tablets include built 
in logic and range checks to ensure data quality. Paper-based questionnaire forms will be 
visually checked by field site supervisors for completeness before being sent to the AKU data 
management unit on a weekly basis for entry into the database. Double data entry is used to 
reduce data entry errors and manual checking of frequencies during data cleaning (e.g., 
consistency, range, and internal validity checks) as applicable. All collected data is kept under 
lock and key and de-identified through the use of nine-digit participant identification codes. Data 
entered into the database is password protected. 
 
 
 

 
                         
 
 
 
 
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10.4.1.2 STUDY RECORDS RETENTION  
  
To enable evaluations and/or audits, the Principal Investigator agrees to keep records, including 
the identity of all participating patients (sufficient information to link records, CRFs and hospital 
records), all original signed informed consent forms, copies of all CRFs, source documents, and 
detailed records in a secure location for a minimum of 7 years in accordance with SickKids 
policy.  
 
 
If the Principal Investigator relocates, retires, or for any reason withdraws from the study, then 
the study records must be transferred to an acceptable designee, such as another Investigator or 
another institution. 
 
10.5 PROTOCOL DEVIATIONS 
 
A protocol deviation is any noncompliance with the study protocol. The Principal Investigator 
will assure that no deviation from, or changes to the protocol will take place without documented 
approval from the Research Ethics Board (REB), except where necessary to eliminate an 
immediate hazard(s) to the trial participants. The noncompliance may be either on the part of the 
participant, the Investigator, or the study site staff. As a result of deviations, corrective actions 
are to be developed by the site and implemented promptly. All protocol deviations will be 
documented; the Principal Investigator will assess each protocol deviation to determine the 
impact to the patient’s rights, safety or welfare, study efficacy and data integrity. Protocol 
deviations must be sent to the reviewing REB in accordance with their policies. The Principal 
Investigator is responsible for knowing and adhering to the reviewing REB requirements. 
 
 
10.6 CONFLICT OF INTEREST 
 
The research team declares no conflict of interest. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
                         
 
 
 
 
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10.7 ABBREVIATIONS 
 
AE 
Adverse Event 
AKU 
Aga Khan University 
ANCOVA 
Analysis of Covariance 
CIOMS 
Council for International Organizations of Medical Sciences 
CLIA 
Clinical Laboratory Improvement Amendments 
CONSORT Consolidated Standards of Reporting Trials 
CRF 
Case Report Form 
eCRF 
Electronic Case Report Forms 
GCP 
Good Clinical Practice 
ICH 
International Council on Harmonisation  
ICMJE 
International Committee of Medical Journal Editors 
LHW 
Lady Health Worker 
MOP 
Manual of Procedures 
MRP 
Most Responsible Physician 
MUAC 
Mid Upper Arm Circumference 
PI 
Principal Investigator 
QA 
Quality Assurance 
QC 
Quality Control 
REB 
Research Ethics Board 
SAE 
Serious Adverse Event 
SFT 
Triceps Skinfold Thickness  
SoA 
Schedule of Activities 
SOP 
Standard Operating Procedure 
 
 
 

 
                         
 
 
 
 
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