Biomarkers of maternal environmental enteric dysfunction are associated with shorter gestation and reduced length in newborn infants in Uganda

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Study Justification:
This study aimed to investigate the relationship between maternal environmental enteric dysfunction (EED) and adverse birth outcomes in pregnant Ugandan women and their newborn infants. Adverse birth outcomes, such as preterm birth and stunting at birth, have long-term health implications. However, the link between these outcomes and chronic gastrointestinal inflammation (EED) is poorly understood. Understanding this relationship is important for identifying potential interventions to improve birth outcomes.
Study Highlights:
– The study was conducted in Mukono, Uganda, between February and November 2017.
– A total of 258 pregnant women were enrolled at their first prenatal visit.
– EED was measured using urinary lactulose:mannitol (L:M) ratio and serum concentrations of antibodies to bacterial components.
– Birth outcome data were recorded for 220 infants within 48 hours of delivery.
– Maternal anti-flagellin and anti-LPS IgG concentrations were associated with shorter gestation and reduced infant length at birth.
– Further research on the relationship between maternal EED and birth outcomes is warranted.
Recommendations for Lay Reader:
– The study found that maternal gastrointestinal inflammation may be linked to adverse birth outcomes, such as shorter gestation and reduced infant length at birth.
– This suggests that interventions to reduce gastrointestinal inflammation in pregnant women may improve birth outcomes.
– Further research is needed to better understand this relationship and develop effective interventions.
Recommendations for Policy Maker:
– Policies and programs should be developed to address maternal environmental enteric dysfunction (EED) in pregnant women.
– Interventions to reduce gastrointestinal inflammation in pregnant women should be explored to improve birth outcomes.
– Further research should be supported to better understand the link between EED and adverse birth outcomes and to develop evidence-based interventions.
Key Role Players:
– Researchers and scientists specializing in maternal and child health, gastrointestinal health, and nutrition.
– Healthcare providers, including obstetricians, gynecologists, and pediatricians.
– Public health officials and policymakers.
– Non-governmental organizations (NGOs) working in maternal and child health.
Cost Items for Planning Recommendations:
– Research funding for further studies on the relationship between maternal EED and birth outcomes.
– Funding for the development and implementation of interventions to reduce gastrointestinal inflammation in pregnant women.
– Training and capacity-building programs for healthcare providers to address EED in pregnant women.
– Monitoring and evaluation of interventions to assess their effectiveness.
– Public health campaigns and education materials to raise awareness about the importance of maternal gastrointestinal health during pregnancy.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study design is a prospective cohort study, which is a reliable method for examining associations. The sample size of 258 pregnant women is relatively large. The study measured biomarkers of maternal environmental enteric dysfunction (EED) and their association with adverse birth outcomes. The results showed that higher concentrations of anti-flagellin and anti-LPS antibodies were significantly associated with shorter gestation and reduced infant length at birth. However, the study did not find an association between the urinary lactulose:mannitol (L:M) ratio and any birth outcome. To improve the strength of the evidence, future studies could consider including a control group for comparison, conducting a randomized controlled trial, and adjusting for additional confounding factors such as maternal age, socioeconomic status, and nutritional status.

Background Adverse birth outcomes, including preterm birth and stunting at birth, have long-term health implications. The relation between adverse birth outcomes and chronic, asymptomatic gastrointestinal inflammation (environmental enteric dysfunction – EED) is poorly understood. Objective We aimed to examine the relation between maternal EED and adverse birth outcomes in a sample of pregnant Ugandan women and their newborn infants. Design We conducted a prospective cohort study in Mukono, Uganda. A total of 258 pregnant women were enrolled at their first prenatal visit (~18 weeks of gestation). EED was measured by urinary lactulose:mannitol (L:M) ratio and serum concentrations of antibodies to the bacterial components flagellin and LPS. Covariates were obtained from survey data collected at 2 time points. Associations were assessed through the use of unadjusted and adjusted simple linear regression models. Results Complete birth outcome data were recorded for 220 infants within 48 h of delivery. Mean ± SD gestational age was 39.7 ± 2.1 wk, and 7% were born preterm. Mean ± SD length and length-for-age z score (LAZ) at birth were 48.1 ± 3.2 cm and -0.44 ± 1.07, respectively. L:M ratio was not associated with any birth outcome. In adjusted models, higher concentrations of natural log-transformed anti-flagellin immunoglobin G (IgG) and anti-LPS IgG were significantly associated with shorter length of gestation (β: -0.89 wk; 95% CI: -1.77, -0.01 wk, and β: -1.01 wk; 95% CI: -1.87, -0.17 wk, respectively) and with reduced length (β: -0.80 cm; 95% CI: -1.55, -0.05 cm, and β: -0.79 cm; 95% CI: -1.54, -0.04 cm, respectively) and LAZ at birth (β -0.44 z score; 95% CI: -0.83, -0.05, and β: -0.40 z score; 95% CI: -0.79, -0.01, respectively). Conclusion Maternal anti-flagellin and anti-LPS IgG concentrations in pregnancy, but not L:M ratio, were associated with shorter gestation and reduced infant length at birth. Further research on the relation between maternal EED and birth outcomes is warranted.

We performed a prospective cohort study between February and November 2017 in Mukono District, Central Region, Uganda. Mukono is a semiurban district situated 20 km east of the capital city, Kampala. The study was based at Mukono Health Center IV (MHC IV), a public outpatient health facility located in the center of Mukono Town. Pregnant women were recruited during their first prenatal visit at MHC IV, which occurred at ∼18 weeks of gestation. Eligible women were 18–45 y of age, residing within 10 km of Mukono Town, and carrying a singleton pregnancy. Women were excluded from the study if they were 45 y old, HIV positive (verified via routine rapid HIV test conducted at the first prenatal visit), severely malnourished [defined as BMI (kg/m2) <16.0], severely anemic (defined as hemoglobin <7 g/dL), or planning to move away from Mukono District before delivery. The study was approved by the Tufts Health Sciences Institutional Review Board in Boston, MA; the Mengo Hospital Research Ethics Committee in Kampala, Uganda; and the Uganda National Council for Science and Technology in Kampala, Uganda. Before enrollment, written consent in either Luganda or English was obtained from each participant. Participation in the study involved 4 visits over a 4–6-mo period: enrollment visit, immediately after the first prenatal visit (MHC IV); L:M test visit, within 1 wk of the first visit (participant's residence); follow-up visit, 3 wk before the expected date of delivery (also at the participant's residence); and postdelivery visit, within 48 h of delivery (either at the participant's residence, MHC IV, or another health facility). An ultrasound scan was performed by a trained professional at MHC IV to both confirm a singleton pregnancy and determine participants’ estimated date of delivery. Hemoglobin was measured with a portable hemoglobinometer (HemoCue Hb 301; HemoCue, Inc., Brea, CA). A venous blood draw was performed by the phlebotomist at MHC IV (BD Vacutainer, Becton Dickinson, Durham, NC). Systolic and diastolic blood pressure (DBP) measurements were taken with a digital upper arm blood pressure monitor (Omron 10 Series, Omron Healthcare, Kyoto, Japan). All anthropometry measurements were performed in triplicate and the mean was used for analysis. Weight was measured to the nearest 0.1 kg with the use of a digital weight scale (Seca 874, Hanover, MD). Height was measured to the nearest 0.1 cm with the use of a portable, rigid height board (Infant/Child/Adult ShorrBoard, Shorr Production, Olney, MD). These measurements were used to calculate BMI. Midupper arm circumference (MUAC) was measured to the nearest 0.1 cm with the use of a standard tricolored, nonstretch adult MUAC tape. Finally, a questionnaire was administered by the study nurse that included questions related to demographics, prior pregnancies, health status, diet, food security [using the Household Food Insecurity Access Scale (HFIAS)] (27), and water, sanitation, and hygiene practices (see Supplemental File 1 for the complete questionnaire). Within 1 wk of the enrollment visit, a household visit was conducted to perform a L:M dual sugar absorption test. Participants with diarrhea (≥3 loose stools/d) in the last 2 wk had their test rescheduled for a different day. After urination to void the bladder and an observed 1-h fast, participants consumed a 50-mL solution containing 5 g of lactulose (Lactulose Solution; Mckesson, San Francisco, CA) and 2 g of mannitol (D-mannitol powder; Sigma-Aldrich, St. Louis, MO) completely dissolved in sterile water. Urine was collected for a period of 4 h in a 2-L plastic collection bottle containing 0.05 mL of 50% thimerosal (Sigma-Aldrich, St. Louis, MO) as a preservative. Water intake was permitted ad libitum 1 h after ingestion of the solution, and women were encouraged to drink a minimum of 500 mL of water during the test to ensure sufficient urine output. A final urine sample was collected at the 4-h time-point and total urine volume was measured to the nearest 1.0 mL with the use of a graduated cylinder in the field. Samples were frozen at −20°C at the MHC IV laboratory before being transferred to a −80°C freezer in Kampala. A second household visit was conducted 3 wk before participants’ estimated delivery date, which consisted of a follow-up survey with questions related to pregnancy risk factors. In addition, weight and MUAC measurements were taken following identical procedures to those used at the enrollment visit. Finally, participants were asked to provide a sample of water from their drinking water storage container for the purposes of a water quality test. Infant characteristics (live birth, date and time of delivery, sex, weight, length, and head circumference) were collected within 48 h of delivery. Birth weight was measured to the nearest 0.1 kg with the use of a digital weigh scale (Seca 874, Hanover, MD), and birth length was measured to the nearest 0.1 cm with the use of a portable, rigid height board (ShorrBoard, Shorr Production, Olney, MD). Head circumference was measured to the nearest 0.1 cm with a flexible measuring tape. All anthropometry measurements were taken in triplicate and averaged. In the case of a stillbirth or neonatal death, only birth date, time, and infant sex were recorded. Urine samples were analyzed for concentrations of lactulose and mannitol with the use of previously described HPLC methods at the Shulman Laboratory at Baylor College of Medicine (28). The L:M ratio was calculated by dividing the urinary lactulose concentration by the urinary mannitol concentration. Lactulose (%LE) and mannitol excretion ratios were calculated from the measured amount of each in urine (concentration × total urine volume) relative to the initial dose of each sugar. Blood samples were centrifuged at 1900 × g for 5 min at room temperature, and serum was divided into aliquots in 2.0-mL clear plastic cryovials. Anti-flagellin and anti-LPS Ig concentrations (IgA and IgG) were measured at the Gewirtz Laboratory at Georgia State University via previously described ELISA methods (19). Concentrations of serum biomarkers are reported as optical density units throughout. Water quality was assessed in the field through the use of a compartment bag test (Aquagenx, Chapel Hill, NC). One hundred mL of drinking water from each household was mixed with an Escherichia coli chromogenic growth medium and incubated for a period of 48 h inside a sealed plastic bag containing 5 compartments of varying volumes. Risk categories were determined by noting which, if any, compartments changed from yellow to green/blue during the incubation period and matching that to a most probable number table based on the WHO guidelines (29). Because there is no safe level of E. coli contamination, a dichotomous (safe/unsafe) water variable was created, corresponding to no E. coli detected and any E. coli detected, respectively. Based on studies of maternal idiopathic inflammatory bowel diseases and adverse birth outcomes, the RR of maternal EED and subsequent preterm birth was assumed to be 2.0 (30, 31). Assuming 80% power, 0.05 significance, a 5% frequency of preterm birth, and 15% loss to follow-up, a sample size of 258 allowed for the detection of an RR of 2.0 within 50% of the true risk parameters. All analyses were carried out with the use of STATA 15 software (Stata Corps, College Station, TX). Weight and length measurements were converted to z scores for weight-for-age (WAZ), LAZ, and weight-for-length (WLZ) with the use of the WHO standards (1). Outliers were defined as WAZ +5, WLZ +5, and LAZ +6. On this basis, 7 observations were excluded from WLZ analyses; none of the other measures had any outliers. Stillbirth was defined as fetal death after 20 weeks of gestation, and preterm birth was defined as a live birth before 37 weeks of gestation. Low birth weight (LBW) was defined as weighing <2500 g at birth, and SGA was defined as birth weight for gestational age <10th percentile via sex-specific INTERGROWTH-21st standards (32). Stunted and wasted at birth were defined as LAZ <−2 and WHZ <−2, respectively. Before analysis, distributions of biomarker values were assessed for outliers and normality. Because of their skewed distribution, L:M ratios, %LE, and serum biomarkers were natural log transformed (ln) in all regression models. Associations between EED biomarkers (continuous, independent variables) and birth outcomes [i.e., gestational age, length, and LAZ (continuous, primary outcomes) and weight, WAZ, WLZ, and head circumference (continuous, secondary outcomes)] were assessed via simple unadjusted and adjusted linear regression models. For adjusted models, covariates were selected via bivariate analyses, with gestational age at birth and birth length as dependent variables, and a P value <0.25. Before inclusion in the model, correlation among covariates was assessed through the use of Pearson correlation coefficients, and the absence of multicollinearity was verified with the use of the variance inflation factor. t Tests were used to assess differences in maternal biomarker means for infants born stillborn, preterm, LBW, SGA, stunted, and wasted compared with not. Pearson correlations were calculated to evaluate agreement between ln L:M ratio and ln serum biomarkers. In all cases, statistical significance was determined by a P value <0.05.

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with access to information and resources related to maternal health, including prenatal care, nutrition, and hygiene practices. These apps can also provide reminders for prenatal visits and medication schedules.

2. Telemedicine: Implement telemedicine programs that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls. This can help overcome geographical barriers and provide access to specialized care.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and basic healthcare services to pregnant women in their communities. These workers can help identify and address maternal health issues early on, reducing the risk of complications.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access prenatal care, delivery services, and postnatal care. This can help overcome financial barriers and ensure that women receive the necessary healthcare services.

5. Maternal Health Clinics: Establish dedicated maternal health clinics that offer comprehensive services, including prenatal care, delivery services, and postnatal care. These clinics can be equipped with skilled healthcare professionals and necessary medical equipment to provide quality care.

6. Health Education Programs: Develop and implement health education programs that focus on maternal health, targeting both pregnant women and their families. These programs can raise awareness about the importance of prenatal care, nutrition, and hygiene practices, and empower women to make informed decisions about their health.

7. Transportation Support: Provide transportation support to pregnant women who face challenges in accessing healthcare facilities. This can include arranging transportation services or subsidizing transportation costs to ensure that women can reach healthcare facilities in a timely manner.

8. Maternal Health Monitoring Devices: Develop and distribute wearable devices or home monitoring kits that allow pregnant women to monitor their health parameters, such as blood pressure, heart rate, and fetal movements. These devices can provide real-time data to healthcare providers, enabling early detection of potential complications.

9. Maternal Health Hotlines: Establish toll-free hotlines staffed by trained healthcare professionals who can provide information, support, and guidance to pregnant women. These hotlines can be available 24/7 and serve as a resource for women seeking immediate assistance or advice.

10. Public-Private Partnerships: Foster collaborations between government agencies, healthcare providers, and private organizations to improve access to maternal health services. These partnerships can leverage resources, expertise, and technology to develop innovative solutions and expand healthcare infrastructure.

It’s important to note that the specific implementation of these innovations would require careful planning, stakeholder engagement, and consideration of local context and resources.
AI Innovations Description
Based on the description provided, the recommendation to improve access to maternal health is to conduct further research on the relationship between maternal environmental enteric dysfunction (EED) and adverse birth outcomes. This research should focus on understanding the mechanisms through which EED affects gestational age and infant length at birth. Additionally, it is important to explore potential interventions or strategies to prevent or mitigate the impact of EED on maternal and infant health. This could involve implementing interventions to improve maternal nutrition, hygiene practices, and access to clean water, as these factors have been associated with EED. By addressing the underlying causes of EED and implementing targeted interventions, it may be possible to improve birth outcomes and maternal health.
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 community-based programs to educate pregnant women and their families about the importance of maternal health, including the impact of environmental enteric dysfunction (EED) on birth outcomes. This can be done through workshops, health campaigns, and the distribution of informational materials.

2. Strengthen antenatal care services: Enhance the quality and accessibility of antenatal care services by ensuring that pregnant women have regular check-ups, receive appropriate screenings and tests, and have access to necessary medications and supplements. This can be achieved by training healthcare providers, improving infrastructure, and increasing the availability of essential resources.

3. Improve nutrition and hygiene practices: Promote healthy eating habits and proper hygiene practices among pregnant women to reduce the risk of EED and improve birth outcomes. This can be done through nutrition counseling, provision of nutritious food, and the promotion of clean water and sanitation facilities.

4. Enhance healthcare infrastructure: Invest in the development and improvement of healthcare facilities, particularly in rural areas, to ensure that pregnant women have access to quality maternal healthcare services. This includes the availability of skilled healthcare providers, medical equipment, and essential medications.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the target population: Identify the specific group of pregnant women who would benefit from the recommendations, such as those living in a particular region or facing specific challenges in accessing maternal healthcare.

2. Collect baseline data: Gather information on the current state of maternal health in the target population, including key indicators such as maternal mortality rates, birth outcomes, and access to healthcare services.

3. Develop a simulation model: Create a mathematical or statistical model that incorporates the various factors influencing access to maternal health, such as distance to healthcare facilities, availability of resources, and knowledge levels. This model should be based on available data and evidence from similar contexts.

4. Input the recommendations: Introduce the proposed recommendations into the simulation model and adjust the relevant parameters accordingly. For example, increase the availability of antenatal care services, improve nutrition and hygiene practices, and enhance healthcare infrastructure.

5. Simulate the impact: Run the simulation model to estimate the potential impact of the recommendations on improving access to maternal health. This could include outcomes such as reduced maternal mortality rates, improved birth outcomes, and increased utilization of healthcare services.

6. Analyze the results: Evaluate the simulation results to assess the effectiveness of the recommendations in improving access to maternal health. Identify any potential challenges or limitations that may arise and consider adjustments to the recommendations or the simulation model if necessary.

7. Refine and implement the recommendations: Based on the simulation findings, refine the recommendations to optimize their impact and feasibility. Develop an implementation plan that includes strategies for monitoring and evaluating the progress of the interventions.

It is important to note that the methodology described above is a general framework and may need to be adapted to the specific context and available data. Additionally, involving relevant stakeholders, such as healthcare providers, policymakers, and community members, in the simulation process can help ensure the accuracy and relevance of the results.

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