Plasma Choline Concentration Was Not Increased after a 6-Month Egg Intervention in 6-9-Month-Old Malawian Children: Results from a Randomized Controlled Trial

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Study Justification:
The study aimed to evaluate the effect of daily egg consumption on plasma choline concentrations in Malawian children aged 6-9 months. Eggs are a rich source of choline, an essential nutrient important for child growth and development. Previous studies in Ecuador showed that an egg intervention led to significant improvements in growth, partially mediated by increased plasma choline concentration. However, a similar trial in Malawi did not show the same positive effects on growth or development. Therefore, this study aimed to investigate the potential reasons for the lack of effect in the Malawian population.
Study Highlights:
– The study involved 660 Malawian children aged 6-9 months.
– Infants were randomly assigned to receive 1 egg per day (intervention group) or serve as a nonintervention control.
– Anthropometric, developmental, and dietary data were collected at baseline and after 6 months, along with a blood draw.
– Plasma choline, betaine, dimethylglycine, trimethylamine N-oxide (TMAO), and DHA were measured using ultrahigh performance liquid chromatography-tandem MS.
– The results showed that plasma choline, betaine, dimethylglycine, and DHA concentrations did not differ between the intervention and control groups after 6 months. However, plasma TMAO was significantly higher in the intervention group.
– The lack of increase in plasma choline and related metabolites, except TMAO, could partially explain the lack of effect on growth and development in the Malawian children.
Recommendations for Lay Reader and Policy Maker:
– The provision of 1 egg per day for 6 months did not result in increases in plasma choline or related metabolites in Malawian children aged 6-9 months.
– Additional interventions are needed to improve choline status, growth, and development in this population.
– The findings suggest that the impact of egg interventions on child growth and development may vary across different populations.
– Further research is needed to understand the factors influencing the response to egg interventions in different contexts.
Key Role Players:
– Researchers and scientists involved in nutrition and child development.
– Health professionals and practitioners working in Malawi’s healthcare system.
– Policy makers and government officials responsible for nutrition and child health programs.
– Community leaders and organizations involved in promoting child health and nutrition.
Cost Items for Planning Recommendations:
– Research and data collection expenses, including personnel salaries, travel costs, and equipment.
– Intervention costs, such as the procurement and delivery of eggs to the intervention group.
– Monitoring and evaluation costs to assess the impact of interventions.
– Training and capacity-building activities for healthcare professionals and community workers.
– Communication and dissemination costs to share research findings with stakeholders and the public.
– Potential costs associated with implementing additional interventions to improve choline status, growth, and development in the target population.
Please note that the provided cost items are general categories and may vary depending on the specific context and implementation strategies.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some limitations. To improve the evidence, the study could consider increasing the sample size, conducting a longer intervention period, and including a more diverse population.

Background: Eggs are a rich source of choline, an essential nutrient important for child growth and development. In a randomized trial of 1 egg/d in young children in Ecuador, an egg intervention led to significant improvements in growth, which were partially mediated by increased plasma choline concentration. A similar trial in Malawi (clinicaltrials.gov: NCT03385252) found little improvement in child growth or development. Objectives: We aimed to evaluate the effect of 1 egg/d for 6 mo on plasma choline concentrations in Malawian children enrolled in a randomized trial. Methods: Infants aged 6-9 mo in rural Malawi were randomly assigned to receive 1 egg/d (n = 331) or serve as a nonintervention control (n = 329) for 6 mo. Anthropometric, developmental, and dietary data were collected at baseline and 6-mo follow-up, along with a blood draw. Plasma choline, betaine, dimethylglycine, trimethylamine N-oxide (TMAO), and DHA were measured at both time points using ultrahigh performance liquid chromatography-tandem MS (n = 200 per group). Linear regression analysis was used to determine the difference in plasma choline and related metabolites between groups after 6 mo of intervention. Results: Plasma choline, betaine, dimethylglycine, and DHA concentrations did not differ between groups at 6-mo follow-up. Plasma TMAO was significantly (26%; 95% CI: 7%, 48%) higher in the egg intervention group in a fully adjusted model. Conclusions: Provision of 1 egg/d for 6 mo did not result in increases in plasma choline or related metabolites, except TMAO. This could partially explain the lack of effect on growth and development. Additional interventions are needed to improve choline status, growth, and development in this population.

The Mazira Project (clinicaltrials.gov: {“type”:”clinical-trial”,”attrs”:{“text”:”NCT03385252″,”term_id”:”NCT03385252″}}NCT03385252) was a randomized trial of 660 Malawian children that took place from February 2018 to January 2019. The primary growth outcomes of the trial have been published previously (7). Infants aged 6–9 mo were individually randomly assigned to intervention or control for 6 mo. Randomization occurred using a 1:1 allocation ratio in blocks of 10, based on a random sequence generated by a researcher independent from the field team. Study staff read a description of the trial to all caregivers and then met individually with caregivers to review the consent statement and respond to questions. If the caregiver agreed to participate, staff invited caregivers to choose 1 sealed envelope, which contained the allocation code, under monitoring by an independent community member. Families in the intervention group received weekly egg deliveries, with instructions to feed 1 egg/d to the enrolled child. Eggs were procured from a local distributor. On average, eggs weighed 53 g and provided 126 mg choline (9). Families in the control group were asked to feed the child his or her typical diet. Descriptions of food and nutrient intakes for study participants have been published (9, 11); generally, complementary diets were maize-based, with some inclusion of green leafy vegetables but limited intake of animal-source foods. Both groups received instruction on food hygiene and handwashing during home visits. At each clinic visit, all participants received incentive gifts that included household goods, such as fabric for clothing, wash basins, buckets, and cooking utensils, as well as cash reimbursement for travel costs. In addition, at study completion, participants in the control group were given a package of goods of equal value to the 6 mo of eggs provided to the intervention group. After the study, results were communicated to participants at community meetings, and participants were given the opportunity to share their experiences. In addition, study results were presented to the Mangochi District Health Committee, nutrition leaders at the Ministry of Health, and the egg producer who supplied eggs to the project. Study protocols were reviewed and approved by institutional review boards at the University of California Davis and the College of Medicine in Malawi. Singleton infants aged 6–9.9 mo residing in the Lungwena Health Center and St Martin’s Rural Hospital in Malindi catchment areas in Mangochi District were eligible to participate. These areas are low-income with high rates of food insecurity, similar to other rural areas in Malawi. Study staff identified age-eligible infants using listings generated by community health workers and recruited them during household visits. Infants were excluded for egg allergy, history of serious allergic reactions requiring emergency care, congenital defects or conditions that affect growth and development, severe anemia (hemoglobin <5 g/dL),  low midupper arm circumference (MUAC; <12.5 cm), presence of bipedal edema, acute illness or injury warranting hospital referral, or if the family planned to leave the area in the next 6 mo. Infants with acute illness or injury, low hemoglobin or MUAC, or bipedal edema were referred to the local health center. Costs of care were paid by the study, and the infant was eligible for reassessment for entry to the study after the illness had resolved. Study staff, who were blinded to group assignment, collected an array of dietary, anthropometric, and sociodemographic data during study visits at enrollment and at 6-mo follow-up. Anthropometric data were converted to z-scores using WHO growth standards (12), with the cutoff of z-score ≤ −2 used to define stunting (length-for-age), underweight (weight-for-age), wasting (weight-for-length), and low head circumference (head-circumference-for-age). The Malawi Developmental Assessment Tool (MDAT) was administered at enrollment and 6-mo follow-up. This tool measures fine motor, gross motor, personal social, and language development using a series of pass/fail tasks. Developmental delay in each domain was defined as failing ≥2 tasks that 90% of children at the same age would pass, using a Malawian reference population. The MDAT has been validated for use in Malawi and has high sensitivity (97%) and specificity (82%) to detect neurodevelopmental impairment in this context (13). Soon after enrollment, staff visited the home to collect information about housing materials and animal ownership for the creation of a housing and asset index. During the same home visit, staff administered the Household Food Insecurity Access Scale (14). Additionally, caregivers reported morbidity symptoms at weekly home visits. Detailed information about data collection procedures is available in prior publications (7, 8). Study nurses collected venous blood into lithium heparin tubes at enrollment and 6-mo follow-up. Venous whole blood samples were used to identify anemia (Hemocue 201; HemoCue Inc; anemia defined as hemoglobin <11 g/dL) and malaria antigens (DF Bioline Malaria Ag P.f/Pan; Abbott Diagnostics). Afterwards, samples were centrifuged at room temperature at 3000 rpm for 15 min within a mean 28 ± 42 min of collection and aliquoted and stored in a local −20°C freezer within a mean 37 ± 14 min of centrifugation. Each afternoon, aliquots were transported for storage in a laboratory facility at −80°C. Plasma choline was quantitated using 2 independent assays. First, it was included in a semiquantitative metabolomics analysis by Metabolon Inc. For this analysis, 200 children per group (n = 400) with adequate blood samples at enrollment and follow-up were randomly selected for inclusion. More than 800 other metabolites were measured, including plasma betaine, DMG, TMAO, and DHA. After precipitation of proteins with methanol, addition of recovery standards, and centrifugation, sample extracts were inserted onto a C18 column (Waters UPLC BEH C18-2.1 × 100 mm, 1.7 µm) and flushed with water, methanol, 0.05% perfluoropentanoic acid, and 0.1% formic acid. Samples were dried and then reconstituted in solvent containing standards at fixed concentrations. Ultrahigh performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) was performed using a Waters ACQUITY ULPC with a Thermo Scientific Q-Exactive high-resolution mass spectrometer, paired with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer. Injection order of the samples was randomized on the instrument and quality of the run was monitored using evenly spaced control samples and internal and recovery standards. Metabolites were identified by Metabolon Inc using authenticated standards. For data measured over several days, the median was set to 1.00 for each run-day block, and data points were normalized proportionally to adjust for day-to-day instrument variation. Missing values were imputed with the minimum. These data are semiquantitative and reported in “relative intensity” units, which provide information about the distribution of plasma choline as well as within-study comparisons. However, because these data are not reported in absolute concentrations, they cannot be compared with other studies. To determine the absolute concentrations, a second, targeted quantitative assay of plasma choline, betaine, and TMAO was performed in a subsample (n = 60) of participants randomly chosen from the metabolomics sample. These analyses were performed at the USDA Western Human Nutrition Research Center using LC-MS/MS as described by Wang et al. (15) with modifications. DMG was not included in these analyses. Briefly, 20-µL plasma samples were aliquoted to a 2-mL Eppendorf tube and mixed with 80 µL 10 µM surrogate standard (deuterated analytes in methanol) then vortexed for 30 s and centrifuged at 18,000 × g at 10°C for 10 min. Supernatant was transported to glass inserts in HPLC vials. Similarly, standards were produced from 0 µM to 100 µM of nondeuterated analytes in methanol. Then, 20 µL of each standard and 80 µL of 10 µM surrogate standard were transferred to 150-µL glass inserts in HPLC vials. All standards were purchased from Sigma-Aldrich. All reagent solvents were purchased from Fisher Scientific and were MS grade. Samples (5 µL) were injected onto a silica column (2.0 × 150 mm, 5-µm Luna silica; Cat. No. 00F-4164-B0; Phenomenex) at a flow rate of 0.25 mL/min using a Waters Acquity UPLC outfitted with an API 4000 Q-TRAP mass spectrometer (AB SCIEX). Then, a discontinuous gradient of 0.1% acetic acid in water with 0.1% acetic acid in methanol at varying ratios was introduced. Electrospray ionization in positive-ion mode with multiple reaction monitoring was used to measure analytes. Integration and quantification of values was completed using Analyst 1.6.2 software. Linear regression models were used to calculate standard linearity. These data were used to estimate the mean concentration of plasma choline, betaine, and TMAO in our study. However, due to the small sample size, they were not used for intervention group comparisons. Additionally, plasma C-reactive protein (CRP) and α-1 acid glycoprotein (AGP) were measured using ELISA by the VitMin lab in Germany (16). A detailed statistical analysis plan was developed prior to analysis and posted publicly (https://osf.io/azf7q/). The main outcome was the difference in mean plasma choline between groups after 6 mo of intervention, as calculated in linear regression models using intention-to-treat analysis. Minimally adjusted analyses included baseline plasma choline values as a covariate. Fully adjusted analyses were adjusted for sociodemographic factors, including: child age, sex, and birth order; maternal age, height, education, occupation, literacy, marital status, tribe, and religion; housing and asset index; animal ownership (chickens, cows, and goats); food insecurity score; distance to water source; number of children aged <5 y in the household; and village location (Lungwena compared with Malindi health center catchment areas). Additionally, month of blood sample collection and time since last intake of foods other than breast milk, water, or tea were considered. Covariates were retained in the model if they were associated with plasma choline with P 20% of samples missing were excluded from the analysis; in general, these were metabolites of medications or other chemicals that were expected to be missing in this infant population. Normality was checked using a Shapiro–Wilk test statistic cutoff of 0.95. Nonnormal metabolites were log transformed, and ranked data were used if normality was still not achieved. Outcomes were summarized with geometric means and CIs regardless of transformation used for analysis. Fold changes were calculated as the ratio of geometric means. Volcano plots and P-value histograms were used to assess the overall intervention effect, and heatmaps were used to explore patterns of significance among metabolic pathways. Although there are a large number of outcomes, only the uncorrected P values were reported because these analyses were exploratory in nature. The statistical analysis plan for the semiquantitative metabolomics analysis was developed a priori and is published at https://osf.io/r6amq/. All analyses were 2-sided tests with an α of 0.05. Given the number of exploratory tests, significant P values should be interpreted with caution.

The study mentioned in the description is focused on evaluating the effect of providing 1 egg per day for 6 months on plasma choline concentrations in Malawian children. The results of the study showed that the egg intervention did not lead to increases in plasma choline or related metabolites, except for trimethylamine N-oxide (TMAO). This lack of increase in plasma choline could partially explain the lack of effect on growth and development observed in the study.

Based on this information, here are some potential recommendations for innovations to improve access to maternal health:

1. Nutritional interventions: Explore and develop interventions that provide pregnant women and new mothers with access to nutrient-rich foods, such as eggs, that can improve maternal and child health outcomes.

2. Education and awareness: Implement programs that educate pregnant women and new mothers about the importance of proper nutrition during pregnancy and postpartum, including the benefits of specific nutrients like choline. This can help improve dietary choices and overall maternal health.

3. Community-based interventions: Develop community-based programs that provide support and resources to pregnant women and new mothers, including access to nutritious foods, healthcare services, and education on maternal health.

4. Technology solutions: Utilize technology, such as mobile applications or telemedicine, to provide remote access to maternal health services and information. This can help overcome barriers to accessing healthcare in remote or underserved areas.

5. Policy changes: Advocate for policy changes that prioritize maternal health and improve access to healthcare services, including prenatal and postnatal care. This can involve increasing funding for maternal health programs, improving healthcare infrastructure, and addressing social determinants of health.

6. Partnerships and collaborations: Foster partnerships between government agencies, non-profit organizations, healthcare providers, and community stakeholders to work together in addressing maternal health challenges and implementing innovative solutions.

It is important to note that these recommendations are based on the general goal of improving access to maternal health and may not directly address the specific findings of the study mentioned. Further research and evaluation would be needed to determine the effectiveness and feasibility of these recommendations.
AI Innovations Description
Based on the provided description, the recommendation to improve access to maternal health would be to conduct additional interventions to improve choline status, growth, and development in the population. The study found that providing 1 egg per day for 6 months did not result in increases in plasma choline or related metabolites, except for trimethylamine N-oxide (TMAO). This lack of effect on growth and development could be partially explained by the insufficient increase in plasma choline concentration. Therefore, it is important to explore other interventions that can effectively improve choline status and subsequently enhance maternal health outcomes.
AI Innovations Methodology
The study you provided focuses on the impact of a 6-month egg intervention on plasma choline concentration in Malawian children. The findings suggest that the provision of 1 egg per day for 6 months did not result in significant increases in plasma choline or related metabolites, except for trimethylamine N-oxide (TMAO). This lack of increase in plasma choline may partially explain the limited improvement in child growth and development observed in the study.

To improve access to maternal health, here are some potential recommendations:

1. Mobile Clinics: Implementing mobile clinics that travel to remote areas can help improve access to maternal health services. These clinics can provide prenatal care, vaccinations, and other essential services to pregnant women who may not have easy access to healthcare facilities.

2. Telemedicine: Utilizing telemedicine technologies can enable pregnant women to receive medical consultations and advice remotely. This can be particularly beneficial for women in rural or underserved areas who may have limited access to healthcare facilities.

3. Community Health Workers: Training and deploying community health workers can help bridge the gap between healthcare facilities and pregnant women. These workers can provide education, support, and basic healthcare services to pregnant women in their communities.

4. Health Education Programs: Implementing health education programs that focus on maternal health can help raise awareness and empower women to take control of their own health. These programs can provide information on prenatal care, nutrition, and safe delivery practices.

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

1. Define the Key Metrics: Identify the key metrics that will be used to measure the impact of the recommendations. This could include metrics such as the number of pregnant women accessing healthcare services, the reduction in maternal mortality rates, or improvements in prenatal care coverage.

2. Data Collection: Collect relevant data on the current state of maternal health in the target population. This could include data on healthcare facility locations, population demographics, and existing healthcare infrastructure.

3. Modeling: Use modeling techniques to simulate the impact of the recommendations on the key metrics. This could involve creating a simulation model that takes into account factors such as the number of mobile clinics deployed, the coverage area of telemedicine services, or the number of community health workers trained and deployed.

4. Scenario Analysis: Conduct scenario analysis to assess the potential impact of different combinations of recommendations. This could involve running simulations with varying parameters, such as the number of mobile clinics or the coverage area of telemedicine services, to determine the most effective combination of interventions.

5. Evaluation: Evaluate the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. This could involve comparing the simulated outcomes to the baseline data collected in step 2 and identifying any significant improvements.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions on implementing the most effective interventions.

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