Different gut microbial profiles in sub-saharan african and south asian women of childbearing age are primarily associated with dietary intakes

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Study Justification:
This study aimed to compare and characterize the gut microbiota in women of childbearing age from sub-Saharan Africa (the Democratic Republic of the Congo, DRC) and South Asia (India) in relation to dietary intakes. The justification for this study is to understand the differences in gut microbial profiles between these two populations and how they are influenced by diet. This information can provide insights into the potential impact of diet on gut health and may have implications for maternal and child health.
Highlights:
1. The gut microbiota of women from sub-Saharan Africa (DRC) had higher alpha diversity compared to women from South Asia (India).
2. The gut microbial community structure was not significantly affected by demographic or environmental variables, such as maternal BMI, education, and water source.
3. Consumption of animal-flesh foods and fermented dairy foods were independently associated with the gut microbiota, suggesting that diet may have a stronger association with microbiota than demographic characteristics.
Recommendations for Lay Reader:
1. This study found that the gut microbiota of women from sub-Saharan Africa and South Asia differ in terms of diversity and composition.
2. Diet was found to have a stronger association with gut microbiota than demographic characteristics.
3. Consuming animal-flesh foods and fermented dairy foods may influence the gut microbiota.
Recommendations for Policy Maker:
1. Consider promoting dietary interventions that focus on improving gut health, particularly in women of childbearing age.
2. Encourage the consumption of a diverse range of foods, including plant-based and fermented dairy products, to support a healthy gut microbiota.
3. Invest in research and education programs to raise awareness about the importance of diet in maintaining gut health and its potential impact on maternal and child health.
Key Role Players:
1. Researchers and scientists specializing in microbiology and nutrition.
2. Public health officials and policymakers.
3. Nutritionists and dietitians.
4. Community health workers and educators.
Cost Items for Planning Recommendations:
1. Research funding for conducting further studies on the relationship between diet, gut microbiota, and maternal and child health.
2. Budget for educational programs and materials to raise awareness about the importance of diet in maintaining gut health.
3. Resources for training and capacity-building of healthcare professionals in the field of nutrition and microbiology.
4. Funding for the development and implementation of dietary interventions targeting women of childbearing age.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study design is well-described, and the sample size is adequate. The statistical analysis methods used are appropriate. However, the abstract lacks information on potential limitations of the study, such as any biases or confounding factors that may have influenced the results. Additionally, it would be helpful to include more details on the specific dietary intakes and their association with the gut microbiota. To improve the evidence, the authors could consider addressing these limitations and providing more comprehensive information on the dietary factors and their impact on the gut microbiota.

Background: To compare and characterize the gut microbiota in women of childbearing age from sub-Saharan Africa (the Democratic Republic of the Congo, DRC) and South Asia (India), in relation to dietary intakes. Methods: Women of childbearing age were recruited from rural DRC and India as part of the Women First (WF) preconception maternal nutrition trial. Findings presented include fecal 16S rRNA gene-based profiling of women in the WF trial from samples obtained at the time of randomization, prior to initiation of nutrition intervention and to conception. Results: Stool samples were collected from 217 women (DRC n = 117; India n = 100). Alpha diversity of the gut microbiota was higher in DRC than in India (Chao1: 91 ± 11 vs. 82 ± 12, P = 6.58E-07). The gut microbial community structure was not significantly affected by any demographical or environmental variables, such as maternal BMI, education, and water source. Prevotella, Succinivibrio, and Roseburia were at relatively high abundance without differences between sites. Bifidobacterium was higher in India (4.95 ± 1.0%) than DRC (0.3 ± 0.1%; P = 2.71E-27), as was Lactobacillus (DRC: 0.2 ± 0.0%; India: 1.2 ± 0.1%; P = 2.39E-13) and Faecalibacterium (DRC: 6.0 ± 1.7%; India: 8.4 ± 2.9%; P = 6.51E-7). Ruminococcus was higher in DRC (2.3 ± 0.7%) than in India (1.8 ± 0.4%; P = 3.24E-5) and was positively associated with consumption of flesh foods. Succinivibrio was positively associated with dairy intake in India and fish/insects in DRC. Faecalibacterium was positively associated with vitamin A-rich fruits and vegetables. Overall, these observations were consistent with India being primarily vegetarian with regular fermented dairy consumption and DRC regularly consuming animal-flesh foods. Conclusion: Consumption of animal-flesh foods and fermented dairy foods were independently associated with the gut microbiota while demographic variables were not, suggesting that diet may have a stronger association with microbiota than demographic characteristics.

Women of childbearing age were recruited from rural DRC (Equateur Province) and India (Belagavi, Karnataka) as part of the WF preconception maternal nutrition trial (Hambidge et al., 2014, 2019). Inclusion criteria were 16–35 years of age; parity 0–5; expectation to have first or additional pregnancy within next 2 years and without intent to utilize contraception. Enrollment occurred after screening, and informed consent was obtained by the home visitor research assistant if the potential participant was eligible. Findings presented in the current report include the gut microbiota of women at the time of randomization prior to the initiation of the nutrition intervention and at least 3 months prior to conception in the WF trial and represented participants from two of the WF sites, with distinctive ethnicity, diet, culture and geographical locations. Women were recruited from 12 villages in rural DRC and 9 villages from rural India. The project was approved by the Colorado Multiple Institutional Review Board, University of Colorado, the local and/or national ethics committees for each site (registered with the US Office of Human Research Protection and with Federal-wide Assurance in place). Written informed consent was obtained from all participants and the study was registered at ClinicalTrials.gov ({“type”:”clinical-trial”,”attrs”:{“text”:”NCT01883193″,”term_id”:”NCT01883193″}}NCT01883193). Questionnaires of demographic information were administered: cell phone (Yes/No), education (None vs. At least secondary), electricity (Yes/No), man-made floor (Yes/No), flush toilet (Yes/No), improved water (Yes/No), landline (Yes/No), motorcycle (Yes/No), fridge (Yes/No), worry no food (Yes/No), and open sewage near house (Yes/No). Improved water means the participant had access to filtered or treated water. Questionnaires were administered in the home by the local home visitor research assistant and were completed within 1 week of enrollment. A mobile assessment team obtained past medical history and height and weight, from which body mass index (BMI) was calculated. A stool sample was collected from each participant (DRC n = 117; India n = 100). A pre-labeled fecal bag, Ziploc bag, a black cryogenic pen, and a Styrofoam storage box containing ice or ice packs were provided to each participant. Stool was collected into the fecal bag using a sterile scoop and then placed into a second Ziploc bag. Participants then placed the bag into the Styrofoam storage container until picked up by the research team on the day the stool was passed. When receiving samples, the research team labeled the sample date and time of stool passage. The research team scooped about a teaspoon of stool and transferred the sample to a sterile stool storage tube with 3 ml RNAlaterTM (ThermoFisher Scientific Inc., Waltham, MA, United States), ensuring that the specimen was coated with RNAlaterTM and that the label was complete. The stool samples were then frozen at −20°C or colder. Samples were shipped to the University of Colorado on ice packs or at ambient temperature. Bacterial profiles were determined by broad-range amplification and sequence analysis of 16S rRNA genes following our previously described methods (Hara et al., 2012; Markle et al., 2013). In brief, DNA was extracted from 25 to 50 mg of stool using the QIAamp PowerFecal DNA kit (Qiagen Inc., Carlsbad, CA, United States), which employs chemical and mechanical disruption of biomass. PCR amplicons were generated using barcoded (Frank, 2009) primers that target approximately 450 basepairs of the V3V4 variable region of the 16S rRNA gene (338F: 5′ACTCCTACGGGAGGCAGCAG and 806R: 5′ GGACTACHVGGGTWTCTAAT) (Lane et al., 1985; Weisburg et al., 1991). PCR products were normalized using a SequalPrepTM kit (Invitrogen, Carlsbad, CA, United States) and then pooled. The amplicon pool was partially lyophilized to reduce its volume then purified and concentrated using a DNA Clean and Concentrator Kit (Zymo, Irvine, CA, United States). Pooled amplicons was quantified using a Qubit Fluorometer 2.0 (Invitrogen, Carlsbad, CA, United States). Illumina paired-end sequencing was performed following the manufacturer’s protocol on the MiSeq platform using a 600 cycle version 3 reagent kit and versions v2.4 of the MiSeq Control Software. Illumina Miseq paired-end reads were aligned to human reference genome hg19 with bowtie2 and matching sequences discarded (Homo Sapiens Ucsc Hg19 Human Genome Sequence from iGenome: Illumina, 2009; Langmead and Salzberg, 2012). As previously described, the remaining non-human paired-end sequences were sorted by sample via barcodes in the paired reads with a python script (Markle et al., 2013). The sorted paired reads were assembled using phrap (Ewing and Green, 1998; Ewing et al., 1998). Pairs that did not assemble were discarded. Assembled sequence ends were trimmed over a moving window of five nucleotides until average quality met or exceeded 20. Trimmed sequences with more than 1 ambiguity or shorter than 350 nt were discarded. Potential chimeras identified with Uchime (usearch6.0.203_i86linux32) (Edgar et al., 2011) using the Schloss (Schloss and Westcott, 2011) Silva reference sequences were removed from subsequent analyses. Assembled sequences were aligned and classified with SINA (1.3.0-r23838) (Pruesse et al., 2012) using the 418,497 bacterial sequences in Silva 115NR99 (Quast et al., 2013) as reference configured to yield the Silva taxonomy. Operational taxonomic units (OTUs) were produced by clustering sequences with identical taxonomic assignments. The software package Explicet (v2.10.5) (Robertson et al., 2013) was used for microbial diversity analysis. For those participants who conceived after at least 3 months after randomization and entered the pregnancy phase of the trial, repeat 24-h dietary recalls were conducted in first trimester on a randomly selected subgroup of the study participants. For the current analysis, dietary data were obtained for 50 women at each site (DRC n = 50; India n = 50). In brief, two 24-h dietary recalls (Lander et al., 2017) were conducted 2–4 weeks apart once pregnancy was confirmed prior to 12-week gestation. The analysis reflected only the participants’ food intakes and did not include any contribution from the trial intervention nutrient supplement. No counseling regarding diet choices or quality was provided over the course of the trial. Dietary assessment training was provided for each of the site nutritionists by the lead study nutritionist. A unique food nutrient composition database was constructed at each site based on the food intake data collected from the dietary recalls to quantify intakes as nutrients and food groups (Lander et al., 2017). Values are presented as mean ± SD for continuous variables. Alpha diversity indices measured for the gut microbiota were tested using the Mann–Whitney statistic. Beta diversity was calculated using weighted UniFrac distances. To compare between DRC and India for differences of the gut microbial profiles, non-parametric Mann–Whitney tests were used. P < 0.05 was considered significant between DRC and India for the gut microbial profiles comparison. Within each site (e.g., DRC and India), permutation-based multivariate analysis of variance (PERMANOVA) tests were used to assess the associations between gut microbial community composition and demographical variables. Associations between the relative abundances of bacterial taxa and nutrients/food-groups were assessed by Spearman rank-order correlation tests. Results were visualized by plotting heatmaps of Spearman’s rho correlation coefficient using the heatmap.2 R function; hierarchical clustering and dendrograms were generated using the default parameters of heatmap.2/dist/hclust functions, using Euclidean distances. Nominal p-values not accounting for multiple testing are reported. Data were analyzed using R version 2.7.2 (R Foundation for Statistical Computing; Vienna, Austria).

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Based on the information provided, here are some potential innovations that could be used to improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide information and resources related to maternal health, including nutrition, prenatal care, and breastfeeding. These apps can be easily accessible to women in rural areas, providing them with valuable information and guidance.

2. Telemedicine: Implement telemedicine programs that allow pregnant women to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to quality prenatal care, especially in areas with limited healthcare facilities.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal care, education, and support to pregnant women in rural areas. These workers can act as a bridge between the community and healthcare facilities, ensuring that women receive the necessary care and resources.

4. Nutritional Interventions: Develop targeted nutritional interventions based on the findings of the study mentioned. This could involve providing specific dietary recommendations and supplements to pregnant women to improve their gut microbiota and overall health.

5. Public Health Campaigns: Launch public health campaigns to raise awareness about the importance of maternal health and the available resources. These campaigns can be conducted through various channels, such as radio, television, and community gatherings, to reach a wide audience.

6. Maternal Health Clinics: Establish dedicated maternal health clinics in rural areas, equipped with trained healthcare professionals and necessary facilities. These clinics can provide comprehensive prenatal care, including regular check-ups, screenings, and vaccinations.

7. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to pregnant women for accessing maternal health services. These vouchers can cover the costs of prenatal care, delivery, and postnatal care, ensuring that women can afford the necessary healthcare services.

8. Partnerships with Non-Governmental Organizations (NGOs): Collaborate with NGOs that specialize in maternal health to leverage their expertise and resources. These partnerships can help expand access to maternal health services and improve the overall quality of care.

It is important to note that the specific implementation of these innovations would require further research, planning, and collaboration with relevant stakeholders.
AI Innovations Description
The study described in the provided text aimed to compare and characterize the gut microbiota in women of childbearing age from sub-Saharan Africa (the Democratic Republic of the Congo, DRC) and South Asia (India) in relation to dietary intakes. The findings showed that the gut microbiota diversity was higher in DRC compared to India. The study also found that the gut microbial community structure was primarily associated with dietary factors rather than demographic or environmental variables.

Based on these findings, a recommendation to improve access to maternal health could be to develop innovative interventions that focus on improving dietary practices and promoting a diverse and balanced diet among women of childbearing age. This could include:

1. Nutrition education programs: Implementing programs that provide women with information and resources on the importance of a healthy diet during pregnancy and the postpartum period. These programs can include workshops, counseling sessions, and educational materials to promote awareness and understanding of the link between diet and maternal health.

2. Community-based interventions: Engaging local communities and healthcare providers to promote healthy eating habits and provide support for women in accessing nutritious foods. This can involve setting up community gardens, organizing cooking demonstrations, and establishing support groups to share knowledge and experiences related to maternal nutrition.

3. Policy changes: Advocating for policies that support and promote access to nutritious foods, such as subsidies for fruits and vegetables, improved food labeling, and regulations on the marketing of unhealthy foods. These policy changes can help create an enabling environment for women to make healthier food choices.

4. Technology-based solutions: Utilizing technology, such as mobile applications or text messaging, to deliver personalized nutrition advice and reminders to women. These tools can provide real-time information on healthy eating, meal planning, and access to local food resources.

By implementing these recommendations, it is possible to improve access to maternal health by addressing the role of diet and gut microbiota in women of childbearing age.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Increase awareness and education: Implement programs to educate women of childbearing age about the importance of maternal health and the role of proper nutrition in pregnancy. This can be done through community health campaigns, workshops, and educational materials.

2. Improve access to healthcare facilities: Enhance the availability and accessibility of healthcare facilities in rural areas, particularly in sub-Saharan Africa and South Asia. This can involve building new clinics, improving transportation infrastructure, and providing mobile healthcare services.

3. Strengthen healthcare workforce: Invest in training and deploying skilled healthcare professionals, including doctors, nurses, midwives, and community health workers, to provide quality maternal healthcare services. This can be achieved through scholarships, incentives, and targeted recruitment programs.

4. Enhance nutrition interventions: Develop and implement nutrition interventions specifically tailored to the dietary needs of women in sub-Saharan Africa and South Asia. This can include providing fortified foods, supplements, and counseling on balanced diets during pregnancy.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators to measure the impact of the recommendations, such as the number of women accessing prenatal care, the percentage of women with adequate nutrition during pregnancy, and the reduction in maternal mortality rates.

2. Data collection: Collect baseline data on the selected indicators from the target regions. This can involve surveys, interviews, and analysis of existing health records.

3. Model development: Develop a simulation model that incorporates the identified indicators and their interrelationships. This model should consider factors such as population demographics, healthcare infrastructure, and the effectiveness of the proposed recommendations.

4. Parameter estimation: Estimate the parameters of the simulation model using the collected baseline data. This can involve statistical analysis and expert input.

5. Scenario testing: Simulate different scenarios by varying the parameters related to the recommendations. For example, test the impact of increasing the number of healthcare facilities or improving nutrition interventions.

6. Analysis and interpretation: Analyze the simulation results to assess the potential impact of the recommendations on improving access to maternal health. Compare the different scenarios and identify the most effective strategies.

7. Policy recommendations: Based on the simulation results, provide evidence-based policy recommendations to stakeholders, policymakers, and healthcare providers. These recommendations should prioritize the strategies that have the highest potential for improving access to maternal health.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and available data.

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