Prenatal dexamethasone exposure induces changes in nonhuman primate offspring cardiometabolic and hypothalamic-pituitary-adrenal axis function

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Study Justification:
– The study aimed to examine the long-term effects of prenatal exposure to glucocorticoids (specifically dexamethasone) on offspring cardiometabolic and hypothalamic-pituitary-adrenal axis function.
– Glucocorticoid administration during pregnancy is common in obstetric practice, and this study sought to determine the clinical relevance of such programming effects.
Study Highlights:
– The study used a nonhuman primate model (African vervet monkeys) to mimic prenatal glucocorticoid exposure in humans.
– Different doses of dexamethasone were administered to pregnant monkeys, and the effects on maternal and offspring health were assessed.
– Dexamethasone dose-dependently reduced maternal cortisol levels without affecting other maternal parameters.
– Offspring exposed to higher doses of dexamethasone showed impaired glucose tolerance, hyperinsulinemia, and increased blood pressure.
– These offspring also exhibited an exaggerated cortisol response to mild stress, indicating hypothalamic-pituitary-adrenal axis programming.
– The findings suggest that glucocorticoid programming may have long-term effects in primates, including humans.
Recommendations for Lay Reader and Policy Maker:
– The study highlights the potential risks associated with prenatal exposure to glucocorticoids, specifically dexamethasone.
– Policy makers should consider the long-term effects of glucocorticoid therapy during pregnancy and evaluate the benefits versus risks.
– Pregnant individuals and healthcare providers should be aware of the potential impact of glucocorticoid exposure on offspring health.
– Further research is needed to better understand the mechanisms underlying glucocorticoid programming and to develop strategies for mitigating its negative effects.
Key Role Players:
– Researchers and scientists specializing in reproductive health, endocrinology, and developmental biology.
– Obstetricians and gynecologists involved in prenatal care and prescribing glucocorticoids.
– Policy makers and regulatory bodies responsible for setting guidelines and regulations regarding prenatal care and medication use.
Cost Items for Planning Recommendations:
– Research funding for conducting further studies on the long-term effects of prenatal glucocorticoid exposure.
– Resources for monitoring and assessing the health outcomes of offspring exposed to glucocorticoids.
– Education and awareness campaigns for healthcare providers and pregnant individuals about the potential risks and benefits of glucocorticoid therapy during pregnancy.
– Development and implementation of guidelines and protocols for the appropriate use of glucocorticoids in obstetric practice.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it presents findings from a study conducted on nonhuman primates, which are closely related to humans. The study used multiple doses of dexamethasone and examined various outcomes such as glucose tolerance, blood pressure, and hypothalamic-pituitary-adrenal axis function. The study also compared the results to human clinical scenarios. To improve the evidence, the abstract could include information about the sample size, statistical analysis methods, and potential limitations of the study.

Prenatal stress or glucocorticoid administration has persisting “programming” effects on offspring in rodents and other model species. Multiple doses of glucocorticoids are in widespread use in obstetric practice. To examine the clinical relevance of glucocorticoid programming, we gave 50, 120, or 200 μg/kg/d of dexamethasone (dex50, dex120, or dex200) orally from mid-term to a singleton-bearing nonhuman primate, Chlorocebus aethiops (African vervet). Dexamethasone dose-dependently reduced maternal cortisol levels without effecting maternal blood pressure, glucose, electrolytes, or weight gain. Birth weight was unaffected by any dexamethasone dose, although postnatal growth was attenuated after dex120 and dex200. At 8 months of age, dex120 and dex200 offspring showed impaired glucose tolerance and hyperinsulinemia, with reduced (approximately 25%) pancreatic β cell number at 12 months. Dex120 and dex200 offspring had increased systolic and diastolic blood pressures at 12 months. Mild stress produced an exaggerated cortisol response in dex200 offspring, implying hypothalamic-pituitary-adrenal axis programming. The data are compatible with the extrapolation of the glucocorticoid programming hypothesis to primates and indicate that repeated glucocorticoid therapy and perhaps chronic stress in humans may have long-term effects.

All animals selected for this study were healthy adult vervet monkeys (Chlorocebus aethiops), also known as African green monkeys, and part of a captive-bred indoor colony at the Primate Unit of the South African Medical Research Council. Forty animals were multiparous females with an average weight of 3.35 ± 0.42 kg, and 13 were males with an average weight of 4.78 ± 0.92 kg. All were selected for normal blood pressures and glucose tolerance with the use of reference data established for this colony. The monkeys were permanently identified with numbers in ink tattoo. For the duration of the study, all monkeys were maintained under identical housing conditions as described previously (76, 77). In short, the air was changed between 15 and 20 times each hour, the temperature was kept at 25°C, the humidity was 50%, and light and dark phases both lasted 12 hours. To enable mating, the animals were maintained in pairs, and then the females were housed singly during pregnancy and lactation. All individually housed animals had access to exercise cages and foraging material 3 times per week. Bird sounds were piped into every room. After weaning at 8 months, the offspring were kept in pairs for behavioral observations, then placed in peer groups of 6 same-sex individuals from 10 months to 12 months of age. Females were paired with males regardless of the stage of the menstrual cycle, and pregnancies were confirmed, dated, and monitored ultrasonographically as described by Seier et al. (78) for vervet monkeys. To diagnose pregnancy, the females were examined once a month under ketamine anesthesia at 10 mg/kg body weight. A gestational sac 2 mm in width is the first sign of pregnancy in vervet monkeys and indicates an embryo of about 33 days’ menstrual age (78). The gestational sac dimension was measured with electronic calipers, and these measurements, as well as features such as size and date of first appearance of the yolk sac and first appearance of fetal heartbeat, were used as a basis for calculating the menstrual age of the pregnancy. This method was also used to determine a date for the start of the glucocorticoid treatment. The gestation period in vervet monkeys is about 5.5 months (79, 80). The gestational age was determined by previously established intrarectal digital and ultrasonographic techniques for vervet monkey pregnancies (77, 78). Pregnant vervets were monitored ultrasonically at weeks 12 and 22 to determine thickness of placenta, fetal heart rate, abdominal diameter, and femur length. Also at 12 and 22 weeks, blood samples were taken to determine anions and cortisol. The females were randomly divided into 4 groups, 3 of which were given dexamethasone at 50, 120, or 200 μg/kg/d (denoted as dex50, dex120, and dex200) from mid-gestation onward. Dexamethasone was discontinued after birth. The dose of dexamethasone is not dissimilar to doses used in human pregnancy, which vary from 20 μg/kg/d in congenital adrenal hyperplasia (33, 48, 53) to 100–500 μg/kg/d in threatened preterm labor (30, 31, 35, 36, 45, 46). The dexamethasone was administered daily in food, which consisted of precooked maize meal containing micro-macronutrient supplementation, as described previously (81). The diet has supported normal reproduction and development for 2 generations (76). Compliance was determined indirectly by determination of the anticipated suppression of maternal plasma cortisol by dexamethasone twice during pregnancy (as described below). Weight, head circumference, head length, biparietal diameter, crown-heel length, crown-rump length, abdominal circumference, subcutaneous fat thickness, femur length, tibia length, forearm length, and hip width were measured at birth with the use of tape and calipers, and at 2, 4, 6, 8, and 12 months of age. The ponderal index and Brazelton score were determined at birth. Blood was sampled from infant offspring via the femoral vein under ketamine anesthesia (10 mg/kg body weight, intramuscularly). Ketamine was delivered under brief physical restraint in the home cage. An adapted Brazelton score based on various individual measures was used as an indication of motor function at birth. These measures, followed by the score measures, included the following: (a) Palmar grasp response: place index finger in palm; 0, no grasping reflex; 1, partial grasp; 2, strong reflexive grasp without voluntary release. (b) Plantar grasp response: place index finger under foot; 0, no grasping reflex; 1, partial grasp; 2, strong reflexive grasp without voluntary release. (c) Body righting: time noted for the infant to turn from supine to prone; 0, partial righting; 1, righting in 5 seconds; 2, righting in less than 5 seconds. (d) Resistance to passive movement: degree of resistance to passive flexion and extension of limbs; 0, barely discernible resistance; 1, moderate resistance; 2, strong resistance. (e) Active power: strength of muscles when actively contracting; 0, some strength but cannot withstand slight resistance; 1, withstands moderate resistance; 2, extremely strong. (f) Tactile response: response to tactile stimulus to the 4 extremities (with a cotton swab); 0, no response; 1, barely discernible response; 2, easily apparent response. (g) Spontaneous locomotion: quality of locomotion within 1 minute; 0, none; 1, a weak attempt at crawling; 2, coordinated crawling noted. (h) Motor activity: observation of motor activity throughout the observation period. The ponderal index relates weight (in grams) to height (in cm). The index is calculated as (weight/height3) × 100. Glucose tolerance was determined in the offspring at 8 months of age. Serial blood sampling was obtained via an i.v. catheter inserted into the saphenous vein under aseptic conditions and connected to a drip set containing physiological saline to maintain patency. After obtaining a baseline blood sample for glucose and insulin, 1 ml/kg of 50% dextrose was administered i.v. Further sampling for blood glucose occurred 5, 10, 15, 20, 40, and 60 minutes after dextrose. A sample for plasma insulin was taken at 10 minutes. Glucose was analyzed by an enzymatic essay (Bayer T01-1825-56) and insulin by microparticle enzyme immunoassay (AxSYM B2D010; Abbott Diagnostics). The suppression test was conducted in the offspring at 8 and 12 months. A basal blood sampling at 10:00 pm was followed by an i.v. administration of 0.026 mg/kg dexamethasone. A second sample was obtained at 7:00 am. All samples were analyzed for cortisol as described above. At 12–14 months, vervets were killed, and part of the liver and pancreas, and the whole heart, kidney, adrenal, brain, pituitary, and thymus, were removed to determine organ weight and for further examination. At 12–14 months, part of the liver was snap-frozen to determine PEPCK activity as described previously (26, 82). In short, liver tissue was homogenized and the cytosolic fraction obtained. The cytosolic fraction (1 mg) was assayed in 1 ml buffer containing 50 mM HEPES (pH 6.5), 50 mM sodium bicarbonate, 1 mM MgCl2, 0.25 mM NADH, 1.0 mM phosphoenolpyruvate, and 1.5 U malate dehydrogenase. The absorbance of NADH was determined at 340 nm at 30°C. After a stable signal was obtained, the reaction was initiated by addition of 0.15 mM 2′-deoxyguanosine 5′diphosphate. After 3 minutes of incubation, the decrease in absorbance was measured at a wavelength of 340 nm. The assay was performed in duplicate for all samples. A reaction mixture without bicarbonate was used as a negative control. RNA was extracted from frozen liver aliquots with the use of TRI zol (Invitrogen). The isolated RNA was reverse-transcribed to obtain cDNA with the use of a Reverse Transcriptase kit (Reverse Transcriptase [RT] system; Promega). PEPCK and GRα mRNA were quantified with RT-PCR primer-probe sets with the use of the ABI PRISM 7900 Sequence Detection System (PE Applied Biosystems) with the following primers and probes: GRα: 5′-CATTGTCAAGAGGGAAGGAAACTC-3′ (forward), 5′-GATTTTCAACCACTTCATGCATAGAA-3′ (reverse), and 5′-6-FAM-TTTGTCAGTTGATAAAACCGCTGCCAGTTCT-TAMRA-3′ (probe). 18S ribosomal RNA primers and probes (Applied Biosystems) were used to normalize the transcript levels. A standard curve for each primer-probe set was generated in triplicate by serial dilution of cDNA that was pooled from several subjects. Each sample was run in duplicate, and the mean values of the duplicates were used to calculate the transcript level from the standard curve. The results are expressed as arbitrary units. Pancreata were placed in 4% paraformaldehyde overnight and processed in an automated tissue processor (Shandon Citadel 1000; Thermo Scientific) through ascending concentrations of ethanol from 70%–100%, followed by xylene. The tissue was embedded in paraffin wax (Paraplast Plus; Monoject Scientific). Sections 4 μm thick were cut on a rotary microtome and mounted on slides coated with 3-aminopropyl­triethoxysilane. Serial sections were incubated for 5 minutes in 0.228% periodic acid to inhibit endogenous peroxidases. In each section, α cells were immunolabeled first, with a polyclonal glucagon antibody (Dako) for 30 minutes at room temperature followed by 0.05% diaminobenzidine tetrahydrochloride. Then β cells were labeled with a monoclonal insulin antibody, overnight at 4°C (Sigma-Aldrich), followed by avidin-biotin-peroxidase complex (VECTASTAIN; Vector Laboratories). Fuchsin (Dako) was used to reveal the immunolabeled insulin-secreting β cells. In the method controls, primary antibody was omitted. All sections were counterstained with hematoxylin. Blood pressure measurements were obtained with a Dinamap pediatric blood pressure machine as previously used in primates (83). Measurements were taken at 8 and 12 months of age, and the average of 5 measurements was used as a measure for blood pressure. These were obtained under ketamine anesthesia (10 mg/kg, intramuscularly); the monkeys were in a supine position with a cuff around their left upper arm. Urine was collected for 24 hours by placement of a funnel covering the entire floor area of the cage. Cortisol and its metabolites were measured in urine by electron impact gas chromatography/mass spectrometry following Sep-Pak C18 extraction (Waters Corp.), hydrolysis with β-glucuronidase, and formation of the methoxime-trimethylsilyl derivatives. Epicortisol and epi-tetrahydrocortisol were used as internal standards. Total cortisol metabolite excretion was calculated as tetrahydrocortisols (THFs) + tetrahydrocortisone (THE) + cortols + cortolones. Relative metabolism by 5- and 5β-reductases was inferred from the 5β-THF/5-THF ratio. A-ring reduction of cortisol was inferred from the ratios of THFs to cortisol and 5β-reductase activity from the ratio of THE to cortisone. Whole-body equilibrium between cortisol and cortisone, determined by the balance of tissue-specific activities of 11β-reductase and 11β-dehydrogenase, was inferred from the ratio of THFs to THE. Renal 11β-dehydrogenase activity was inferred from the urinary free cortisol/cortisone ratio (84). The experiments were performed in accordance with the National Code for Animal Use in Research, Education, and Diagnosis and Testing of Drugs and Related Substances in South Africa. The study was approved by the Ethics Committee for Research on Animals of the South African Medical Research Council and by the Ethical Review committee of the University of Edinburgh. Data were analyzed by 1- or 2-way ANOVA with repeated measures where appropriate. Post hoc tests involved either Bonferroni’s multiple-comparison test or Fisher’s least significant difference method for factor as clearly indicated. Live versus still births were analyzed by χ2. Kruskal-Wallis nonparametric testing was used to analyze Brazelton scores, followed by Mann-Whitney U testing. P < 0.05 was regarded as significantly different.

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Based on the provided description, it is not clear what specific innovations are being discussed or requested. However, based on the information provided, here are some potential recommendations for innovations to improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can improve access to maternal health by allowing pregnant women to consult with healthcare professionals remotely, reducing the need for in-person visits and increasing convenience, especially for those in remote or underserved areas.

2. Mobile health (mHealth) applications: Developing mobile applications that provide educational resources, appointment reminders, and personalized health information can empower pregnant women to take control of their health and access important maternal health services.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, education, and support to pregnant women in their communities can help improve access to maternal health services, particularly in areas with limited healthcare infrastructure.

4. Maternal health clinics: Establishing dedicated maternal health clinics that offer comprehensive prenatal care, including regular check-ups, screenings, and counseling, can ensure that pregnant women have access to the necessary healthcare services in a supportive and specialized environment.

5. Transportation support: Providing transportation support, such as subsidized or free transportation services, can help overcome barriers to accessing maternal health services, particularly for women in rural or low-income areas who may face challenges in reaching healthcare facilities.

6. Maternal health awareness campaigns: Implementing targeted awareness campaigns to educate pregnant women and their families about the importance of prenatal care, early detection of complications, and available maternal health services can help increase utilization and access to these services.

7. Financial assistance programs: Developing and implementing financial assistance programs, such as health insurance coverage or subsidies, can help alleviate the financial burden associated with maternal healthcare and improve access for women who may otherwise face financial barriers.

8. Partnerships with community organizations: Collaborating with local community organizations, NGOs, and other stakeholders can help identify and address specific barriers to accessing maternal health services, tailoring interventions to the unique needs of the community.

It is important to note that these recommendations are general and may need to be adapted to specific contexts and healthcare systems. Additionally, further research and evaluation are necessary to determine the effectiveness and feasibility of these innovations in improving access to maternal health.
AI Innovations Description
Based on the provided description, the recommendation to improve access to maternal health would be to conduct further research and clinical trials to assess the long-term effects of prenatal glucocorticoid administration on maternal and offspring health. This would involve studying the impact of different doses of glucocorticoids on maternal cortisol levels, birth weight, postnatal growth, glucose tolerance, insulin levels, blood pressure, and hypothalamic-pituitary-adrenal axis function in both animal models and human subjects. The research should also investigate the potential programming effects of glucocorticoid therapy and chronic stress on long-term health outcomes in humans. This information would contribute to the development of evidence-based guidelines for the use of glucocorticoids in obstetric practice and inform strategies to mitigate any potential risks associated with their use.
AI Innovations Methodology
To improve access to maternal health, here are two potential recommendations:

1. Telemedicine: Implementing telemedicine services can greatly improve access to maternal health, especially in remote or underserved areas. Telemedicine allows pregnant women to consult with healthcare professionals remotely, reducing the need for travel and increasing access to prenatal care, monitoring, and advice. This can help identify and address potential complications early on, leading to better maternal and fetal outcomes.

2. Mobile health (mHealth) applications: Developing and promoting mHealth applications specifically designed for maternal health can also enhance access to care. These apps can provide educational resources, track pregnancy progress, offer reminders for appointments and medication, and enable communication with healthcare providers. By utilizing smartphones, which are increasingly accessible globally, mHealth apps can reach a wide range of pregnant women and provide them with essential information and support.

Methodology to simulate the impact of these recommendations on improving access to maternal health:

1. Define the target population: Identify the specific population that would benefit from improved access to maternal health. This could include pregnant women in rural areas, low-income communities, or regions with limited healthcare infrastructure.

2. Collect baseline data: Gather data on the current state of maternal health in the target population, including indicators such as maternal mortality rates, prenatal care utilization, and access to healthcare facilities. This will serve as a baseline for comparison.

3. Develop a simulation model: Create a simulation model that incorporates the potential recommendations (telemedicine and mHealth apps) and their expected impact on improving access to maternal health. This model should consider factors such as the number of healthcare professionals available, technological infrastructure, and user adoption rates.

4. Input relevant data: Input data into the simulation model, including the number of healthcare professionals available for telemedicine consultations, the coverage and usage rates of mHealth apps, and the expected reduction in travel time and costs.

5. Run simulations: Run multiple simulations using different scenarios and assumptions to assess the potential impact of the recommendations on improving access to maternal health. This could include variations in the number of healthcare professionals, the level of technological infrastructure, and the level of user adoption.

6. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. This could include evaluating changes in maternal mortality rates, increased utilization of prenatal care, and improved health outcomes for both mothers and infants.

7. Refine and validate the model: Refine the simulation model based on feedback and validation from experts in the field of maternal health. Incorporate additional data and adjust assumptions as necessary to improve the accuracy and reliability of the model.

8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, including policymakers, healthcare providers, and community organizations. Highlight the potential benefits of implementing the recommendations and advocate for their adoption to improve access to maternal health.

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