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Abstract
Introduction Although evidence exists on the impact of microbiota on pregnancy outcomes in many high-resource settings, there is a lack of research in many low-resource settings like Ethiopia. This study aims to fill this gap by studying the gut and vaginal microbiota changes throughout pregnancy and assess how these changes relate to pregnancy outcomes among a cohort of pregnant women in eastern Ethiopia.
Methods and analysis Vaginal and stool samples will be collected using DNA/RNA Shield Collection kits three times starting at 12–22 weeks, 28–36 weeks and at birth (within 7 days). Postnatally, newborns’ skin swabs (at birth) and rectal swabs will be obtained until 2 years of age. Moreover, breast milk samples at birth and 6 months and environmental samples (water, indoor air and soil) will be collected at enrolment, birth, 6, 12 and 24 months post partum. DNA will be extracted using Roche kits. Metagenomic sequencing will be performed to identify metataxonomic profiling and assess variations in microbial profiles, and α and β diversity of the microbiota. Information on socioeconomic, behavioural, household and biological factors will be collected at enrolment. The collected data will be coded, entered into EpiData 3.1 and analysed using Stata 17.
Ethics and dissemination The Institutional Health Research Ethics Review Committee (Ref No. IHRERC/033/2022) of Haramaya University, Ethiopia has approved this study ethically. Written informed consent regarding the study and sample storage for biobanking will be obtained from all participants. Results will be published in international peer-reviewed journals, and summaries will be provided to the study funders. Clinical study data will be submitted to Data Compass (https://datacompass.lshtm.ac.uk/), and molecular profiles of the microbiome and whole-genome sequences will be submitted to the European Nucleotide Archive (https://www. ebi.ac.uk/ena). Requests for data should be directed to daberaf@gmail.com. The decision to share data will be made by the study steering committee under the College of Health and Medical Sciences, Haramaya University, Ethiopia.
- Pregnant Women
- Ethiopia
- Microbiota
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Strengths and limitations of this study
Enrolment of women in early pregnancy until 2 years will enable us to document the microbiome changes during pregnancy and post-pregnancy period.
Inclusion of maternal-infant dyad is essential to comprehensively characterise maternal and infant microbiome and its relationship and associated pregnancy outcomes.
The study is being conducted within the Health and Demographic Surveillance Sites, making it representative of other similar settings.
Since recruitment is in early pregnancy and at health facilities, women who are not aware of their pregnancy status or did not seek antenatal care might be missed.
Introduction
Despite many interventions, maternal and child health remains the unfinished agenda in many low-resource settings. Although the maternal mortality ratio was reduced to 267 per 100 000 in 2020, an estimated 10 000 maternal deaths occur every year in Ethiopia.1 2 While the two-third reduction in child mortality was achieved as part of the Minimum Development Goals (MDG), post-MDG, neonatal mortality remained unchanged, making the journey towards the achievement of the Sustainable Development Goals uncertain.3 4 Since most studies exploring the causes of the high rates of maternal and neonatal mortality and morbidity are often directed towards socioeconomic, nutritional and health service-related factors, the role of the microbiota in maternal and neonatal remains poorly understood, especially in lower middle income countries (LMIC).5 Hence, the need to study microbiota is emerging, given the importance of these factors on maternal and child health.6 7
Microbiota refers to the collection of microorganisms in each environment, including (parts of) the human body.8 The newborn birth process plays a vital role in colonising the infant’s gut and is highly influenced by the maternal microbiota. Moreover, the birth environment contributes to the newborns’ microbiota colonisation,9 10 and the diversity of the microbiota varies with the age of the newborn.11 The vaginal microbiota forms a homeostatic and mutualistic relationship with the human host and plays an important role in health and disease. The most dominant vaginal bacterial species, Lactobacillus spp.,12 13 for example, protects reproductive-age women from non-indigenous pathogens.12–14 The composition of the microbiota depends on its location and is influenced by several factors,15 16 such as age, vaginal pH, hormonal secretions, antibiotic use, body mass index, hormones and sexual activity, which mainly affect the vaginal microbiota.16 Even though vaginal microbiota during pregnancy may vary due to many factors, the members of the Lactobacillus genus and five of its species are significantly more abundant, and several genera, such as Prevotella, Atopobium, Acinetobacter and Sneathia, are substantially less abundant during healthy pregnancy.17 18
Moreover, the gut microbiota also undergoes changes associated with different factors.19 The gut microbiota in pregnant women changes significantly to adapt to the changing metabolism and immunity, which is important in maintaining maternal health and foetal development.20 During gestation, the female body undergoes several changes, including hormonal, immunological and metabolic changes to support foetal growth and development.21 In a healthy pregnancy, the bacterial load and profound alterations in the composition of gut microbiota increase.22 23 Most gut microbes in a healthy pregnancy belong to one of the two phyla: Bacteroidetes and Firmicutes.16 24 25 While the composition of gut microbiota in the first trimester of pregnancy is like that of healthy non-pregnant women, it changes from the first to the third trimester of pregnancy, including an increased abundance of members of the Actinobacteria and Proteobacteria phyla, as well as a reduction in individual richness (alpha diversity).23
Investigating the interface between maternal/newborn complications and microbiota colonisation would minimise adverse pregnancy outcomes, child mortality, pregnancy-related complications and death.23 Maternal vaginal microbiota and its relationship with the health of infants have been shown in several studies.9 10 Alterations of the human microbiota are a known characteristic of various inflammatory disease states and have been linked to spontaneous preterm birth and other adverse pregnancy outcomes, such as preterm birth.26–28 Recent studies have examined the role of vaginal microbiota with possible associations with preterm birth.27 29 The healthy vaginal microbiota plays a role in preventing bacterial vaginosis, sexually transmitted infections, urinary tract infections and HIV. Protection is attributed to lactic acid-producing bacteria, mainly Lactobacillus spp.27
The gut microbiota of infants is influenced by multiple perinatal and early-life environmental factors, such as pregnancy, gestational age at birth, maternal health, mode of delivery, birth weight, and breast milk feeding duration,30 exposure to antibiotics before, during and after delivery, and exposure to pets.31 The maternal microbiota is an essential factor in maternal and child health outcomes, and a better understanding of the role of the microbiota and sources of pathogens will guide interventions to reduce adverse pregnancy outcomes.23 However, the microbiota’s effect during pregnancy and later life is being investigated, mainly in high-resource settings. As such, low-resource settings, like Ethiopia, have a limited contribution to the global knowledge of general microbiota-host-environment studies during pregnancy. Therefore, we are establishing a pregnancy and early-life cohort in eastern Ethiopia to fill this gap.
Aims (primary)
Characterise the metataxonomic profile and the changes across trimesters and post partum.
Assess the association of microbiota changes with pregnancy outcomes.
Identify the common microbiota in childhood until 2 years and their link with child growth and development.
Establish a microbiota biobank for future inquiries in eastern Ethiopia.
Identify determinants of adverse perinatal outcomes through a metagenomic approach.
Aims (secondary)
Assess the prevalence of sexually transmitted infections, bacterial vaginosis (BV), vaginal candidiasis (VC) and their associated factors among pregnant women.
Methods and analysis
Study settings
This study will be integrated into the Hararghe Health and Demographic Surveillance System (HDSS).32 The HDSS is an open cohort that maintains a continuous demographic and health data record for a known population within a defined geographic area. It regularly tracks vital events such as births, deaths, migration and marital status in urban, semiurban and rural districts. Individuals enter into the surveillance system through enumeration, birth or immigration and will leave the system through out-migration or death.32 Midwives trained in basic obstetric ultrasound techniques perform ultrasound scans, estimate gestational age and invite those in the early trimester (12–22 weeks) at each facility. Moreover, we will use village pregnancy detectors to identify pregnant women and refer them to facilities for ultrasound screening, thereby including them in the study.
Study design and population
This is a longitudinal multi-centre cohort study with embedded cross-sectional and case-control studies from August 2024 to January 2027. The primary cohort will consist of all women in early pregnancy (12–22 weeks) who will be followed throughout pregnancy up to 2 years post partum to study changes in the microbiota and the impact on pregnancy outcomes. Within this main cohort, a cross-sectional study, including repeated surveys, will be used to identify the microbiota, its changes and associated factors at a certain point in time. Additionally, we will use a case-control study to identify determinants of adverse pregnancy outcomes (stillbirth, preterm birth, neonatal death, congenital anomaly, low birth weight, macrosomia and small for gestational age).
The sample size is calculated based on the primary and secondary outcome variables. For the primary outcome variables, the calculation is done using the G* Power V.3.1.9.4 software with the assumption of effect size (Shannon diversity) of 0.25, 80% power and 95% CI, yielding a total sample size of 197. The primary outcome variable is the change in vaginal and gut microbiota from the first trimester of pregnancy to immediately post partum. To identify major determinants of adverse birth outcomes, the sample size is calculated using the OpenEpi (V.3) software by considering the prevalence of adverse birth outcomes, which was 27.47% from a previous study conducted in Tefera Hailu Memorial Hospital, Sekota Town, Northern Ethiopia33, 80% power, 95% CI and a case-to-control ratio of 1:1. A total sample size will be 38 (19 for adverse birth outcomes cases (stillbirth, preterm birth, neonatal death, congenital anomaly, low birth weight, macrosomia and small for gestational age) and 19 for controls (term birth outcomes).
For secondary outcome variables (ie, the prevalence of BV, VC and sexually transmitted infections), the sample size is estimated using a single population proportion formula for a proportion from the previous studies by using a 95% CI and 5% marginal error. For its associated factors, it is estimated by a double proportion formula using the EPI Info V.7 statistical software, with the assumptions of 95% CI, 80% power and a 1:1 ratio. The final sample size is 217, including a 10% loss to follow-up.
Inclusion and exclusion criteria
Inclusion criteria
Women within the reproductive age (18–49 years).
Between 12 and 22 weeks of gestational age (confirmed by obstetric ultrasound).
Residing in the selected sites for at least 3 months during the pregnancy.
Not planning to move out of the study area within the next 2 years.
Singleton pregnancy (foetus).
Exclusion criteria
Prolonged (more than 3 months) use of systemic antibiotics, antifungals, antivirals or antiparasitic therapy.
Observed abnormalities during enrolment via ultrasound, such as anencephaly and spina bifida.
Data collection and measurements
Data will be collected on Redcap by trained female research midwives proficient in the local languages (Afan Oromo and Amharic) and knowledgeable about the local cultural contexts. At enrolment, data will be collected on the current pregnancy and obstetric history, food and nutrition, and household conditions through interviewer-administered questionnaires and observation. The questionnaire and checklist are adapted from the Ethiopian Demographic and Health Survey and the WHO indicators for the health of women during pregnancy, labour and delivery.34 35 Initially prepared in English, local experts will translate the questionnaire into local languages (Afaan Oromo and Amharic) and back-translate it into English. The questionnaire will be pretested on 5% of the sample of pregnant women residing outside the study area. The research midwives will be trained on the standard operating procedure (SOP) and the overall aspects of the study. The questionnaire is found in online supplemental information 1 and solicits information on the following: livelihoods, wealth, animal ownership, management and disease, water, sanitation and hygiene, child health and nutrition and women’s empowerment. We will further collect data on substance use (including khat), dietary diversity, household food insecurity (HFIAS), antibiotic use and diet. Furthermore, comprehensive obstetric and reproductive health conditions will be obtained at enrolment (12–22 weeks gestational age), 28–36 weeks and birth (within 7 days). Details of the data to be collected at each phase are described in figure 1.
Supplemental material
Diagrammatic presentation of the study flow chart and samples to be collected. EGA, estimated gestational age.
Specimen collection, transportation and storage
An SOP on specimen collection, transportation, storage and analysis is prepared for leading the work. At enrolment, three vaginal swabs and one stool sample will be collected. At visit two (28–36 weeks), one vaginal swab and one stool sample will be collected. At birth, one vaginal and breast milk sample from the mother, as well as one rectal and skin swab from the newborn, will be collected. Rectal swabs will be collected from the newborn at 6, 12 and 24 months. Environmental samples, including water, indoor air and soil, will be collected during enrolment at birth, 6, 12 and 24 months post partum at the participants’ home (figure 1).
Vaginal, rectal and skin swab samples will be collected using DNA/RNA Shield Collection (2 mL fill) swabs (Zymo Research, Irvine, CA, USA). The mother will lie down on the couch, and the sample will be collected by inserting the swab into the midpoint of the vagina.36–38 The skin swab of the newborn infant will be collected from the retro-auricular crease (ear)38 and the alar crease (nose).39
At enrolment, two additional vaginal swabs will be collected to investigate BV and VC using sterile Dacron swabs from the lateral and posterior vaginal fornices.40 One of the vaginal swabs will immediately be dipped into Amies transport media and labelled with the patient’s barcode number during collection before it is dispatched, then transported to the Hararghe Health Research (HHR) laboratory. The second vaginal swab will be used to perform a Gram stain for Nugent scoring and perform the whiff test.
The stool sample will be collected using the DNA/RNA Shield Fecal Collection Tube (Zymo Research). Place the plastic hat over the toilet seat to collect stool to prevent faeces from falling into the toilet water, ground/soil or urine to avoid sample contamination. If study participants are unable to produce the stool samples at health facilities, they will be provided with the collection kit and clear instructions for self-collection at home. All samples preserved in the DNA/RNA Shield reagent are stable and can be transported and stored at room temperature. Specimens received in the laboratory for nucleic acid detection will be stored at temperatures below −20°C (Zymo Research). Breast milk will be collected using a sterile container. The mothers will collect the sample by pump or manually following aseptic techniques. The collected sample will be stored immediately in a 2–8°C refrigerator and transported within 4 hours. Specimens received in the laboratory for nucleic acid detection will be stored at temperatures below −20°C.41
Approximately 200 g of soil samples will be collected from multiple points in the living compounds and indoors of the study participants. The samples will be the top or surface of 5–15 cm soil, where most of the microbial population is concentrated and their activity occurs. Using a sterilised spatula, the sample will be transferred to clean, dry and sterile polythene zip lock bags. To maintain accuracy and representativeness, wet patches, manure, urine areas, wood dumps burdening locations, fence rows, spoil banks, footpaths and the burn-row regions will be avoided.42 A 250 mL drinking water sample will be collected using a sterile bottle from the point of use.42 Moreover, indoor air samples will be collected to determine the concentration and diversity of fungus and bacteria-carrying particles in the living environments of pregnant mothers using a passive indoor air monitoring technique called a settling plate. The standard Petri dish containing MacConkey broth culture media will be exposed to the closed indoor air for 60 min. Multiple Petri dishes will be used based on the area of the house and 1 m3 of air volume.43
Sample processing
Two of the vaginal samples collected at enrolment will be used for screening BV and VC. One of the swabs will be used to perform Gram staining. The vaginal swab will be smeared, dried, heat-fixed and Gram-stained using the SOP.44 Next, it will be scored using the Nugent score system for laboratory diagnosis of BV.45 The other swab will be used to test VC using other laboratory methods. In summary, the vaginal swab will be initially inoculated on Sabouraud Dextrose Agar (Oxoid, Hampshire, UK) and incubated at 37°C for 24–48 hours. Candida species identification will be performed by subculturing the identified fungus onto the selective and differential Chromogenic candida agar medium, which enables us to differentiate candida species based on their colour on the media46; also, the conventional biochemical test and sugar fermentation will be used for fungal identification. Antifungal susceptibility testing will be performed using a modified disc diffusion method for all Candida isolates as per the Clinical and Laboratory Standards Institute guidelines by adding 2% glucose and 0.5 µg/mL methylene blue dye into Mueller-Hinton agar. Finally, the zones of inhibition will be measured and interpreted according to CLSI guidelines (M44-A2).47 The Gram staining and Candida species identification procedures will be conducted at the HHR laboratory (figure 2).
Laboratory analysis and sample storage flow chart. AHRI, Armauer Hansen Research Institute; BV, bacterial vaginosis; HHR, Hararghe Health Research.
Environmental sample analysis
The drinking water collected from households will be analysed for its physicochemical and bacteriological quality. Physicochemical quality parameters such as pH, temperature, dissolved oxygen, nitrate, ammonia, hardness, fluoride and residual chlorine will be determined. Bacteriological quality indicators mainly fecal coliform and total coliform will be conducted using a membrane filtration technique and incubated at 37°C for total coliform and 44°C using proper media membrane lauryl sulfate. The results will be reported as colony-forming units (CFUs). To analyse the microbial composition of the soil sample, a serial dilution of 10 to 1 will be used.42
The index of microbial air contamination from the indoor air sample will be explored through a 48-hour incubation at 37°C and reported as counts of CFUs. The possible bacteria and fungi that cause indoor air quality problems will be identified through Gram stain and biochemical analysis.43
Nucleic acid extraction and sequencing
DNA extraction from the vaginal, stool, breast milk, rectal and skin samples will be conducted using the Roche extraction kits, following the standard manufacturer’s protocol at the HHR laboratory. We will appropriately document sample batches to control batch effects in our analysis. The DNA concentration will be measured using the Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA). For those samples with a low load of DNA yield, we will amplify and sequence the 16S rRNA gene.
Library preparation will be performed using Nextera DNA Flex Library Preparation Kits. To allow pooled sample analysis, the samples will be indexed using IDT Illumina Nextera DNA Unique Dual Indexes. Pair-end sequencing will be done using the Illumina NextSeq2000 at the Ghent University Laboratory for Bacteriology Research and the Armauer Hansen Research Institute (AHRI). We will use rarefaction curves to determine the required sequencing depth to provide adequate coverage of microbial communities for taxonomic profiling. We will aim for a minimum yield of 2–5×106 read-pairs per sample. For samples of very low microbial load, that is, skin and breast milk, we will compute diversity indexes setting the minimum depth at 0.5 million reads. To identify potential environmental and laboratory contaminants, we will sequence negative controls and positive controls of mock communities along with the samples.
Metataxonomic profiling of the different microbiota
Obtained sequences will be trimmed, and sequencing adaptors will be removed using Trimmomatic.48 Assembly will then be performed using SOAPdenovo2.49 To filter and remove taxa that are found in a small number of samples and have small counts, we will use PERFect,50 which, besides filtering, can be used to define and quantify loss due to filtering. Host sequences will be removed by mapping against the human genome with Bowtie2.51 Reads will then be assigned to the most appropriate taxonomic level using MetaphlAn2.52 Phylogenetic analysis will be performed using RAxML V.8.53
Integration of multi-omic microbiome data and disease prediction
To evaluate changes and interrelatedness of the breast milk, gut, skin and vaginal microbiome, we will use NetMoss.54 The presence or absence of species across the range of microbiomes will be used to identify shared species. We will use SNP calling to detect shared species.55 We will also apply phyLoSTM,56 which is a deep learning framework that uses a combination of convolutional neural networks and long short-term memory networks to analyse the temporal dependency in our longitudinal microbiome sequencing data and environmental and clinical factors for the prediction of outcomes, including stillbirth, preterm birth, low birth weight and small for gestational age (SGA).
Pathogen detection
A multiplex PCR will be run for selected vaginal pathogens at the HHR and the AHRI.
Establishing the biobank
All nucleic acid extracts will be stored in a freezer at −80°C at the HHR laboratory for biobanking. All samples in the biobank will be archived according to strict standard practice protocols that will protect the participants’ identities. The biobank will be managed according to the principles of transparency, ethics and national laws to support sustainability. Microbiome research is an extensive undertaking and requires most samples to be archived in the biobank.
Data processing and analysis
All data captured through the Redcap software will be exported and analysed using Stata 17. Descriptive data will be presented using frequency tables and figures. Categorical variables will be summarised using frequencies and proportions, while continuous variables will be described using means with SD and medians with the IQRs as appropriate. To compare the differences in proportions, we will use the χ2 test, while Student’s t-test will be used to compare the mean difference for normally distributed continuous data. A generalised linear mixed model that is flexible to address discrete variables with repeated observations will be used to assess the relationship between categorical outcomes and associated factors. We will use generalised estimating equations or mixed effects regression to analyse the regression relationship between normally distributed, repeatedly observed continuous outcome variables and covariates. Moreover, we will use a generalised linear model (binary logistic regression, Poisson regression or binomial) to assess the association between binary categorical variables measured once and covariates. Non-parametric variables will be considered for comparing data that are not normally distributed. The strength of association will be determined by calculating the OR or prevalence/incidence ratio or rate and their 95%CIs based on the types of outcome variables under consideration.
For the statistical differences between the vaginal and gut microbiota throughout gestation and post partum, the Statistical Analysis of Metagenomics Profile software package will be used. A linear mixed model regression analysis will be used to find the statistical significance of microorganism abundance and community state types (CSTs) during pregnancy and post partum. A linear regression analysis will be performed for each microorganism to measure whether there is a significant change in bacterial species abundance and whether CSTs change significantly throughout pregnancy and post partum. For postpartum changes in microorganism abundance, we will create a binary time variable to distinguish between measurements taken during pregnancy and those collected post partum. To assess whether CSTs change significantly during pregnancy, we will conduct linear regression for each CST. Similarly, to examine CST changes during the puerperium, we will establish a binary time variable for measurements from both pregnancy and post partum, followed by linear regression for each CST. To identify the association between the composition of the vaginal and gut microbiota during pregnancy and post partum, a bivariate analysis will be done. The logistic regression model will also be used to test the significance of the interaction between other biobehavioural factors, sociodemographic factors and microbiota compositions. Multiple testing corrections will be applied using the Bonferroni Correction, which adjusts the significance level by dividing the desired alpha level (eg, 0.05) by the total number of tests performed.
Ethics and dissemination
Ethical approval was obtained from the Institutional Health Research Ethics Review Committee (Ref No. IHRERC/033/2022) and renewed (Ref No. IHRERC/103/2024) at Haramaya University, Ethiopia. The study subjects will be briefed on the purpose of the study, and their participation will be entirely voluntary, with the right to withdraw from the study at any point. Each participant will be assigned a participant identification number (code), and all information will be stored in a locked cabinet to maintain confidentiality. The mother/guardian will give consent on behalf of their children to participate in the study. Informed consent will be obtained from all study participants before data collection, as found in online supplemental information 2. Study participants will be asked to consent to storing samples and data as necessary for potential future usage. All samples in the biobank will be collected and archived according to a strict standard of practice, which will protect the participants’ identity. The biobank will be managed according to the principles of transparency, ethics and national laws to support sustainability. Results will be published in international peer-reviewed journals, and summaries will be provided to the study funders. Clinical study data will be submitted to Data Compass (https://datacompass.lshtm.ac.uk/), and molecular profiles of the microbiome and whole-genome sequences will be submitted to the European Nucleotide Archive (https://www. ebi.ac.uk/ena). No new data have been created or analysed to date, as this is a study protocol, and therefore data sharing is not applicable. Once the data are collected and the biobank is established, all enquiries for data should be directed to Dr Abera Kenay (email: daberaf@gmail.com). Data-sharing decisions will be made by the project steering committee of the College of Health and Medical Sciences, Haramaya University, following the university’s data-sharing guidelines.
Supplemental material
Patient and Public Involvement (PPI)
There will not be patient or public involvement in the study.
Discussion
In this study, we aim to establish a novel system to continuously register the microbiota data and its changes throughout pregnancy in eastern Ethiopia. Through enrolling women in the Hararghe Demographic Surveillance, we will collect primary socioeconomic, obstetric and faeto-maternal conditions, obstetric and medical history, and stool and vaginal samples throughout pregnancy and within 2 years post partum.
Many complications during pregnancy, such as gestational diabetes, preeclampsia, digestive disorders, susceptibility to certain infectious diseases and autoimmune diseases, are related to disruptions of gut microbiota.57 58 The microbiota in the mothers’ gastrointestinal tract is closely related to the infants’ microbiota, contributing to chronic illnesses of the gastrointestinal system, such as Crohn’s disease and irritable bowel syndrome.59 60 Moreover, the microbiota in the mother’s breast milk and vagina are closely related to the colonisation of microbiota in the early life of infants.61 A sharp decline in the total number of beneficial microbes, that is, Lactobacillus, and an increase in the concentration of anaerobic bacteria resulted in BV.62–65 The BV is associated with different adverse pregnancy outcomes, including spontaneous abortion, recurrent pregnancy losses, preterm labour, preterm delivery, preterm pre-labour rupture of membranes, low birth weight, amniotic fluid colonisation, postpartum endometritis, increased risk of pelvic inflammatory disease and facilitated transmission of sexually transmitted infections (STIs).66 67
Despite understanding the multifaceted health problems associated with the microbiota during pregnancy, Africa, in general, and Ethiopia, in particular, are lagging in contributing to this evidence. Given that the maternal microbiota is diverse and is highly influenced by the environment, diet, health, mode and place of delivery, and genetics, it is essential to study this in a low-resource setting, such as Ethiopia. Evidence from longitudinal clinical studies and animal models revealed that disruption in the gut microbiota in early life could programme later-life disease. A microbiota biobank would facilitate answers to many questions relevant to human development in a country with a high burden of communicable diseases and an emergence of non-communicable diseases. The EthiOMICS biobank would provide abundant evidence in the future. Identifying the dynamics of microbes in pregnant women and their children and their association with maternal and child health will guide policy that improves maternal and child health in Ethiopia.
Findings from this project will be essential to unravelling contextual host-agent-environment interactions. With global research leaning towards big data and bioinformatics, the EthiOMICS biobank would place Haramaya at the forefront of future genomics and big data in Ethiopia. Like the prominent UK Biobank—which produces large datasets68 and generates revenue—EthiOMICS would be a unique data source owned by Haramaya University. It could generate income for the university through its genome database and further expand the research work.
Ethics statements
Patient consent for publication
References
Footnotes
Contributors The study concept and design were conceived by AKT, DAA, TW, AG, TAY, MD, FW, KTR and NA. FW, AKT, ASN, TT, MD, TAY, NA, YMD, DAA, AAN, TGE, AA, AG, TW, KTR, PC and TNA refined the study questionnaires and study design, and supervised the data collection and sample processing. Analyses will be conducted by FW, AKT, PC, AAN, TGE and ASN with the help of statisticians. FW prepared the first draft with close support from AKT. AKT is the guarantor. All authors critically revised the review and intellectual content of the final protocol.
Funding This study is funded by Haramaya University (grant number: HUGG_2021_02_04_26) and co-funded by Ghent University and Armauer Hansen Research Institute (AHRI). This work was supported by Universitaire Stichting (Belgium). The funding body has no role in the design of the study, data collection, data analysis, or interpretation.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.