Modeling Insulin and Glucose Dynamics and Metabolic Adaptions During Pregnancy under Two Testing Conditions: Oral Glucose Tolerance Test and Hyperinsulinemic-Euglycemic Clamp

Modeling Insulin and Glucose Dynamics and Metabolic Adaptions During Pregnancy under Two Testing Conditions: Oral Glucose Tolerance Test and Hyperinsulinemic-Euglycemic Clamp | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling Insulin and Glucose Dynamics and Metabolic Adaptions During Pregnancy under Two Testing Conditions: Oral Glucose Tolerance Test and Hyperinsulinemic-Euglycemic Clamp Larissa Calancie, Mohammad S. Jalali, Ali Akhavan, Taysir Mahmoud, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4145532/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Maternal metabolism has important short- and long-term implications for mothers and their infants. Elevated levels of circulating maternal glucose and insulin are associated with large for gestational age infants and increased neonatal adiposity, both of which can have negative health effects. Assessing maternal glucose and insulin dynamics during pregnancy is important for identifying women in need of intervention and has the potential for informing personalized prenatal care. Methods We developed a novel system dynamics simulation model that estimates plasma insulin and glucose levels in early (12–16 weeks) and late (34–36 weeks) pregnancy under two clinical testing conditions: a 3-hour 75g fasted oral glucose tolerance test, and 3-hr fasted hyperinsulinemic-euglycemic clamp conditions. Results Model output closely resembled research data collected from 28 racially and ethnically diverse participants at both time points (e.g., OGTT glucose R 2 in early pregnancy: 0.97, OGTT insulin R 2 in early pregnancy: 0.98). The late pregnancy model includes five known metabolic adaptations that occur over the course of gestation, which contribute to the development of maternal insulin resistance. This physiologic insulin resistance in pregnancy facilitates nutrient availability to support fetal growth as gestation progresses. Conclusion This study is an initial step toward developing a personalized tool for monitoring maternal glucose dynamics to improve prenatal care, especially for pregnancies complicated by obesity and/or GDM. The novel simulation model shows how a combination of metabolic adaptations during pregnancy can explain the observed development of insulin resistance the occurs between early to late pregnancy. We included key delays in insulin action, an innovative approach to model glucose intake during an OGTT, and used several testing conditions to inform and validate the model. The model output aligned with plasma insulin and glucose in early and late pregnancy among participants (N = 28) under measured OGTT and simulated hyperinsulinemic-euglycemic clamp conditions. gestation pregnancy gestational diabetes mellitus system dynamics glucose maternal metabolism systems biology in silico observational cohort Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Maternal metabolism during pregnancy can have profound short- and long-term health effects on women and their offspring ( 1 ). Persistently high maternal glucose and insulin levels that are characteristic of gestational diabetes mellitus (GDM) can drive fetal growth, specifically adipose tissue, which may lead to large-for-gestational age infants as described by the Pedersen hypothesis ( 2 – 5 ). Large-for-gestational age neonates (weight ≥ 90th percentile) have a higher risk of birth injury and congenital abnormalities, and neonates with high levels of adiposity are more likely to develop obesity and associated health conditions including diabetes and hypertension compared to peers ( 6 , 7 ). GDM and sub-clinical glucose intolerance during pregnancy also significantly increases pregnant individuals’ risk of complications and cardiometabolic disease after pregnancy ( 8 , 9 ). Incidence of GDM and sub-clinical glucose intolerance during pregnancy is ~ 7–15% and projected to rise significantly in the United States (US) and globally as obesity prevalence and severity among women of reproductive age increases ( 10 ). Standard prenatal care in the US includes a two-step test for GDM so cases can be managed to avoid negative outcomes associated with excess fetal growth and adiposity. Women receiving prenatal care are screened as a first step between 24–28 weeks of gestation using a one-hour glucose challenge test ( 11 ). If patients screen positive on their initial test, they will undergo a 3-hour oral glucose tolerance test (OGTT) to diagnose GDM. The American Congress of Obstetricians and Gynecologists defines GDM as plasma glucose values of ≥ 140mg/dL during a 3-hour OGTT ( 12 ). Glucose challenge tests, like most medical screening tools, can produce false negative results. A systematic review of GDM screening tests reported sensitivity of 70–88% for the glucose challenge test ( 13 ). Moreover, a lesser-known hypothesis – the “fetal glucose steal hypothesis” – posits that women with impaired glucose tolerance might not be correctly identified with a glucose challenge test or even an OGTT because their developing fetus is rapidly draining the maternal glucose stock over the course of the test ( 14 ). Interactions between maternal glucose supply and fetal demands create complex dynamics that fluctuate hourly and adapt as gestation progresses. Given the rising incidence of obesity and GDM and the potential for missing pregnancies at risk for excess fetal fat accrual, novel approaches for monitoring maternal glucose dynamics and fetal growth over the course of pregnancy are needed. Approaches should be scalable, patient-centered, and make use of minimally invasive technologies and routinely collected patient data. To address this need, we developed a proof-of-concept simulation model of the major physiological feedback mechanisms that drive maternal glucose and insulin dynamics and tested the model under OGTT and gold standard hyperinsulinemic-euglycemic clamp conditions. This study is an early step toward more personalized tools for monitoring maternal glucose dynamics over the course of pregnancy. METHODS System Dynamics Modeling Approach We used system dynamics to build a novel simulation model of the major physiological feedback mechanisms producing maternal glucose and insulin dynamics under OGTT and hyperinsulinemic-euglycemic clamp testing protocols in early (12–16 weeks) and late (34–36 weeks) pregnancy. We selected system dynamics as a modeling method because it can simulate endogenous relationships between system components as well as accumulations and delays within systems ( 15 ). The relationships between plasma glucose and insulin in vivo are characterized by endogenous processes with several critical accumulations and delays ( 16 ). Existing literature, clinical data, model calibration, and optimization informed model structure and parameters. Supplemental table 1 shows parameter values and sources. Optimization was conducted to estimate parameters that were not available in our clinical data or in the literature. We defined the optimization function as the minimum Total Mean Absolute Percent Error (MAPE) between model output and clinical glucose and insulin data identified via at least 10,000 simulation runs using the Powell algorithm. We calculated the MAPE for both glucose and insulin at each time point where there was data available and added them together to define Total MAPE. The optimization results (i.e., minimizing the Total MAPE), provided estimates for 9 parameters in the OGTT models and 6 parameters in the clamp models. Once the models were optimized, we conducted univariate sensitivity analyses by changing the parameter values by ± 20% over 40,000 simulation runs and reported the effect on insulin and glucose MAPE in tornado plots. This approach identifies the relative impact of each variable on model fit. Vensim DSS version 10 was used to build, calibrate, and optimize the system dynamics model. ChatGPT was used to generate Python code to create figures comparing model output to clinical data and sensitivity graphs( 17 ). Oral Glucose Tolerance Test Data We used unpublished primary data from the Maternal Metabolic Markers of Infant Adiposity (MAMMA) study to calibrate and optimize the model and conduct model testing under OGTT conditions (IRB# 12903). Healthy pregnant patients ages 18–45 with a pregravid BMI between 18–45 kg/m 2 and no previous history of high blood pressure or diabetes in pregnancy, and with no current drug or alcohol use, were eligible for the MAMMA study. Participants were recruited between 2018–2023 from Tufts Medical Center, Boston, MA. Upon enrollment, participants’ height and weight were measured, blood pressure was taken, medical history collected by trained research staff, and blood markers of thyroid, liver and kidney function were assessed to ensure participants were healthy enough to participate in the study. Participants completed two clinical visits in early pregnancy (12–16 weeks) and late pregnancy (34–36 weeks). Those visits included a 75g three hour-oral glucose tolerance test after an overnight fast. Blood was drawn at baseline and then every 30 minutes for two hours and then after 60 minutes for a total of six measures over 180 minutes. Plasma glucose and insulin were measured via an automated glucose analyzer (YSI 2500, Yellow Springs, Ohio) and ELISA (Human Insulin ELISA, Crystal Chem, Cat#90095) according to the manufacturer’s protocol. Hyperinsulinemic-Euglycemic Clamp Data In addition to oral glucose tolerance tests conducted in the MAMMA study, we used results of a hyperinsulinemic-euglycemic clamp study described in the literature to inform our model of maternal glucose and insulin dynamics during early and late pregnancy ( 18 ). The physiological system that the model summarizes is the same under either testing condition (OGTT vs Clamp); the models differed in the amount and timing of glucose and insulin intake in accordance with each testing condition. Using two tests allowed us to triangulate several unknown parameters and build confidence that the model worked as expected. The participant population and experimental protocol for the hyperinsulinemic-euglycemic clamp conditions is described in-depth elsewhere ( 18 ). Briefly, the protocol was conducted with 15 obese pregnant women with and without GDM at 12–14 and 34–36 weeks gestation ( 18 ). The clamp procedure began with an overnight fast and involves intravenous infusion of insulin and glucose in order to achieve a steady-state of plasma insulin (approximately 50 uU/mL) and glucose concentrations (approximately 90 mg/dL) ( 18 ). The clamp allows researchers to calculate endogenous glucose synthesis and glucose uptake ( 19 ). The procedure is considered the gold standard for assessing insulin sensitivity and glucose tolerance, but it is impractical for routine prenatal care. After building a model of insulin and glucose dynamics in early pregnancy under OGTT and clamp conditions, we modified the model parameters to replicate development of insulin resistance over the course of gestation (Supplemental Table 1). We modeled the development of insulin resistance over the course of gestation by modifying parameters in the late pregnancy model to reflect known metabolic adaptations. Those metabolic adaptions include: i) increased hepatic glucose production, ii) increased basal insulin production, iii) increased first and second phase insulin secretion in response to a rise in blood glucose levels, and iv) decreased effect of insulin on insulin-dependent glucose uptake ( 18 ). Collectively, the adaptations create insulin resistance and thus increase the circulating stock of glucose in the maternal blood stream. The increased maternal glucose stock creates a concentration gradient that drives glucose uptake by the placenta ( 20 ). In addition to the metabolic adaptations listed above, we also increased the rate of glucose uptake by the placenta in the late pregnancy model since the placenta and fetus have greater glucose requirements in late compared to early pregnancy. RESULTS MAMMA Study Participants Data for twenty-eight MAMMA participants were included in this modeling study. The average age of study participants was 31.9 years old. Average pregravid body mass index (BMI, kg/m 2 ) was 24.5, with 10 participants considered overweight (BMI ≥ 25) and 2 considered obese (BMI ≥ 30). Two participants self-reported Hispanic or Latino ethnicity. Eighteen participants self-reported being Caucasian, six Black or African American, two Asian, and two unknown or not reported. Two participants developed GDM during the study and 14 participants were nulliparous. Five MAMMA study participants were not included in this modeling study because they may have experienced fetal glucose steal since they had large-for-gestational-age babies but were not diagnosed with GDM. Simplified Model Structure The simulation model presented in this study portrays major maternal glucose and insulin dynamics. Figure 1 shows a simplified stock and flow diagram to illustrate key elements of the model. The simulation model contains more variables and equations (see Suppl materials). Variables within squares are stocks that can accumulate or dissipate according to rates of inflows and outflows, indicated by pipes with valves. Insulin is represented with two stocks, one showing levels of insulin in plasma and the other showing levels of insulin in interstitial fluid. Previous experimental and modeling studies highlighted the need to represent insulin in two compartments since they help explain an observed delay between rises in plasma insulin and insulin’s action to increase glucose uptake by peripheral tissues ( 21 , 22 ). Experimental studies show a 3:2 ratio of insulin in plasma to insulin in interstitial fluid, suggesting about one third of plasma insulin does not enter the interstitial fluid ( 23 ). The stock of glucose increases via two main inflows: glucose intake and endogenous glucose production by the liver. Glucose can leave plasma through non-insulin mediated uptake by the central nervous system and other tissue and through insulin-mediated uptake by skeletal muscle, white adipose, and liver tissue, all of which occur over the course of an OGTT. The first major balancing loop (B1) shows that a rise in glucose levels triggers an increase in plasma insulin levels, which reduces hepatic glucose production, thus reducing the flow of glucose into the bloodstream. The second balancing feedback loop (B2) shows that a rise in blood glucose levels causes an increase in the rate of insulin secretion, which leads to an increase in the rate of insulin-dependent glucose clearance and a subsequent lowering of the stock of blood glucose. Comparing Model Output With Clinical Data Under OGTT And Hyperinsulinemic-Euglycemic Clamp Conditions The model output closely resembled median plasma insulin and mean glucose levels measured in MAMMA participants during a 180-minute OGTT in early pregnancy (Fig. 2 ). Fit statistics indicate a good fit between model output and data (e.g., OGTT mean glucose R 2 in early pregnancy: 0.97, OGTT median insulin R 2 in early pregnancy: 0.98, Table 1 ). The R-squared for the clamp models is not available because the observed data values remain constant throughout the model. Median insulin was used because the ratio of mean to median values was greater than 0.1, indicating that outliers might have a large effect on the mean. When glucose and insulin intake were switched to reflect hyperinsulinemic-euglycemic clamp conditions (i.e., infusion of both glucose and insulin) ( 18 ), model output achieved a steady-state as intended (Fig. 3 ). Table 1 Statistical fit data comparing clinical measures to output from a novel simulation model of maternal glucose and insulin dynamics under oral glucose tolerance test and hyperinsulinemic-euglycemic clamp conditions in early (12–16 weeks) and late ( 34 – 36 ) weeks gestation. R 2 RMSE Oral glucose tolerance test (OGTT) models Early pregnancy Insulin 0.98 2.87 Glucose 0.96 4.28 Late pregnancy Insulin 0.99 3.38 Glucose 0.97 5.55 Hyperinsulinemic-euglycemic clamp models Early pregnancy Insulin NA 0.14 Glucose NA 0.0002 Late pregnancy Insulin NA 0.1517 Glucose NA 0.0017 RMSE = Root mean square error Simulating The Development of Insulin Resistance Due to Metabolic Adaptations During Pregnancy As described above, we modified model parameters to reflect five adaptations that occur as gestation progresses. The adaptations are: 1) increased hepatic glucose production, 2) increase basal insulin production, 3) increased first and second phase insulin secretion in response to a rise in blood glucose levels, 4) decreased effect of insulin on insulin-dependent glucose uptake (i.e., decreased insulin sensitivity), and 5) increased glucose uptake by the fetal-placental unit (Suppl Table 1). The late pregnancy model fit the data well (e.g., OGTT glucose R 2 in late pregnancy: 0.97, OGTT insulin R 2 in late pregnancy: 0.99, Table 1 , Fig. 4 ). A visual comparison of Figs. 2 and 4 clearly shows the development of insulin resistance over the course of gestation, indicated by the increase in insulin and glucose levels in response to an oral glucose load and the elevated levels of both glucose and insulin over the course of the OGTT in late compared to early pregnancy. The same physiological parameter changes were applied to the clamp model conditions. Similar to early pregnancy results, the late pregnancy clamp model output replicated the expected steady-state levels (Supplemental Fig. 2). Sensitivity Analysis Overall, our sensitivity analysis indicated that changing parameters by 20% did not improve or diminish model fit to clinical data by more than about 2% (Supplemental Fig. 2). Since MAPE was a composite of model fit to glucose and insulin data, there are opportunities to improve model fit by < 2% that were not implemented because they might improve fit with glucose data, for example, at the expense of fit with insulin data, and vice versa. A second reason we did not further improve model fit to the data was to avoid overfitting the model and thus reducing model generalizability. There was no single parameter or set of parameters that affected model fit most across the glucose testing conditions in early or late pregnancy. For example, in the early pregnancy OGTT model, changes in parameters that govern the amount and timing of glucose ingested and variables that govern maternal glucose uptake are more sensitive to changes than other variables, whereas variables that govern insulin response to a rise in blood glucose levels are more sensitive to changes in late pregnancy. At both time points, variables involved in insulin moving through the two model compartments are the least sensitive to parameter changes. The clamp models were less sensitive to parameter changes than the OGTT models, with most parameter changes corresponding to ≤ 1% change in total MAPE. DISCUSSION With obesity prevalence high (29% of reproductive age women in the US ( 24 )) and rising, incidence of GDM is also increasing, putting infants and their mothers at risk for adverse health outcomes during and after birth ( 7 ). Elevated glucose and insulin during pregnancy that characterize GDM can be managed, lowering the risk of large for gestational age infants and infants with high levels of adiposity ( 25 ). It is paramount to identify women with GDM and offer treatment. The novel simulation model described in this study is an initial step towards personalized tools for monitoring maternal glucose dynamics over the course of pregnancy that uses biomarkers that are routinely collected during prenatal care. The model output closely resembles plasma insulin and glucose in early and late pregnancy among twenty-eight research participants that underwent OGTTs, and it also resembles steady-state levels under hyperinsulinemic-euglycemic clamp conditions. To our knowledge, this is the first system dynamics model exploring changes in glucose and insulin dynamics known to develop over the course of pregnancy. Scientists have studied glucose and insulin dynamics for decades in a quest to understand and treat diabetes, which affects about 422 million people worldwide and is estimated to contribute to one in nine deaths among adults ages 20–79 years old ( 26 , 27 ). Many mathematical models have been developed to move the field closer to a “closed loop” system for managing insulin administration in order to reduce the burden of care on individuals with diabetes ( 28 ). Diabetes during pregnancy is a particularly important topic to study for two reasons: 1) if untreated it can increase risks for negative health outcomes for the pregnant patient and offspring, and 2) insulin resistance, a state of decreased insulin sensitivity that is on a continuum whose upper end is called diabetes, naturally develops over the course of pregnancy ( 29 ). Studying the development of physiological insulin resistance that occurs during pregnancy and rapidly resolves after delivery gives scientists a unique window into metabolic processes that would not be possible to experimentally manipulate in humans. The use of biomarkers and other data that are routinely collected during prenatal care in conjunction with mathematical modeling is a promising strategy for studying glucose and insulin dynamics in humans in pursuit of developing highly effective and acceptable treatments. Our system dynamics model contained several key features that help explain its ability to replicate clinical data. There are important delays and functional forms that influence the dynamic relationship between glucose and insulin ( 30 ). There is a delay in the dampening effect on plasma insulin levels and hepatic glucose synthesis suppression ( 22 ). There is also a delay as insulin moves from plasma to interstitial fluid where it binds with insulin receptors on the surface of peripheral cells and initiates the translocation of GLUT4 to the cell surface ( 23 ). Experimental evidence shows that insulin release rate follows a two-phase pattern ( 31 ); that non-linear rate is critical for accurately simulating insulin accumulation and action. Two reviews of mathematical models of glucose and insulin dynamics noted similar features in hyperinsulinemic-euglycemic clamp models ( 28 , 32 ). Challenges of modeling oral glucose intake compared to intravenous clinical tests include factors such as the rate of gastric emptying, extent of glucose absorption, and the effect of hormones such as incretins ( 28 ). We addressed these challenges by modeling exogenous glucose intake as a rate that is dependent on the stock of ingested glucose. This created an exponential decline in the rate of glucose uptake over the course of an OGTT, which represented a simplified yet sufficient function for our model. Our study has strengths and limitations. We used a small sample to calibrate the model, potentially limiting generalizability. Glucose and insulin production, signaling, metabolism, and other biochemical processes are characterized in exceptional detail that was not included in the model presented in this study ( 33 ). An important strength is that we used a dynamic modeling approach to study a dynamic biological process. Another strength is our ability to reproduce two glucose tolerance protocols, an OGTT and a hyperinsulinemic-euglycemic clamp, by modifying relevant model parameters (e.g., rate of glucose intake, rate of insulin secretion) within the same model structure. To our knowledge, no other published model can reproduce clinical data under multiple testing protocols ( 28 ). There are a number of opportunities to expand the model presented here to answer important research questions. For example, future models will include fetal glucose and insulin dynamics to test whether those additions might allow us to interrogate the fetal glucose steal hypothesis, a theoretical explanation for why some pregnant women are not diagnosed with GDM yet deliver babies with macrosomia ( 14 ). Study results could also inform considerations for different approaches to identify impaired glucose tolerance during pregnancy that might include: 1) greater attention to plasma insulin in addition to the current emphasis on maternal glucose levels, 2) utilizing continuous glucose monitoring to gather information about maternal glucose levels outside of the conditions tested with a routine OGTT, and 3) additional screening at multiple time points during pregnancy for at-risk individuals. Expanding this model to include details about the placenta as the interface between fetal nutrient glucose demands and maternal supply is another potential direction, as is adding lipid handling dynamics since insulin has major effects on lipid biosynthesis and metabolism. The placenta secretes hormones thought to promote insulin resistance as pregnancy progresses ( 34 ), so there is an opportunity to explore how placental growth and hormone production influence the development of insulin resistance with advancing gestation. Emerging evidence suggests that obesity and its effects on maternal metabolism may also increase risk of preterm delivery due to placental growth dysregulation ( 35 ). We could use a model to elucidate mechanisms underlying this observation and test potential intervention effects aiming to promote healthy fetal growth and prevent extremes on either end of the growth spectrum (i.e., small- or large-for-gestational-age infants). Validating the model with a larger dataset would be beneficial, especially if it afforded the opportunity to study differences in model parameter values by race and ethnicity since evidence suggests potential differences in glucose metabolism between racial groups ( 22 ). The model could be further developed for individual-level tailoring and clinical decision-making. A study of a federally qualified health system with significantly better maternal and neonatal outcomes than surrounding clinics (e.g., lower rate of preterm delivery, high rates of breastfeeding) found that participants described personalized care as a critical factor that shaped their positive prenatal care experiences ( 36 ). Finally, the model could be adapted to become a teaching tool for health professionals that care for patients experiencing diabetes or impaired glucose tolerance ( 37 ). CONCLUSION We developed a novel simulation model to show how a combination of metabolic adaptations during pregnancy can explain the observed development of insulin resistance the occurs between early to late pregnancy. We included key delays in insulin action, an innovative approach to model glucose intake during an OGTT, and used several testing conditions to inform and validate the model. The model output aligned with plasma insulin and glucose in early and late pregnancy among participants (N = 28) under measured OGTT and simulated hyperinsulinemic-euglycemic clamp conditions. This is an initial step toward developing a personalized tool for monitoring maternal glucose dynamics to improve prenatal care, especially for pregnancies complicated by obesity and/or GDM. Declarations Ethics approval and consent to participate The Health Sciences Institutional Review Board at Tufts University approved this study (IRB#12903), and all participants were enrolled following written informed consent. Consent for publication Not applicable Availability of data and materials The datasets generated and/or analyzed during the current study are available by request to the corresponding author. Competing interests The authors declare that they have no competing interests. Funding L. Calancie is supported by K12HD092535 from NIH/NICHD and Tufts Building Interdisciplinary Research Careers in Women's Health (BIRCWH) K12 Career Development. The MAMMA study is supported by R01HD091054 from NIH/NICHD (PI: O’Tierney-Ginn) and the National Center for Advancing Translational Sciences, National Institutes of Health award number, UM1TR004398. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Authors' contributions LC contributed to study conception, design, analysis, data interpretation, and drafted the manuscript; MSJ contributed to study design, analysis, data interpretation, and manuscript revisions; AA contributed to analysis, data interpretation, and manuscript revisions; TM contributed to study design and data acquisition; CDE contributed to manuscript revision; and POTG contributed to study conception, design, data acquisition, data interpretation, and manuscript revision. Acknowledgements We would like to acknowledge all the MAMMA study participants and the data collection team. References Adamo KB, Ferraro ZM, Brett KE. Can We Modify the Intrauterine Environment to Halt the Intergenerational Cycle of Obesity? Int J Environ Res Public Health. 2012;9(4):1263–307. Kristiansen O, Zucknick M, Reine TM, Kolset SO, Jansson T, Powell TL, et al. Mediators Linking Maternal Weight to Birthweight and Neonatal Fat Mass in Healthy Pregnancies. 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Jalali","email":"","orcid":"","institution":"Harvard Medical School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"S.","lastName":"Jalali","suffix":""},{"id":288319074,"identity":"3e0c235e-4d33-48c3-b66e-8c973a75ab1c","order_by":2,"name":"Ali Akhavan","email":"","orcid":"","institution":"Harvard Medical School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Akhavan","suffix":""},{"id":288319075,"identity":"0e3d9e76-2e14-46c8-afac-c28d86e10708","order_by":3,"name":"Taysir Mahmoud","email":"","orcid":"","institution":"Tufts Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Taysir","middleName":"","lastName":"Mahmoud","suffix":""},{"id":288319076,"identity":"e819e94e-83bf-44b1-90c8-c489729c725d","order_by":4,"name":"Christina D. Economos","email":"","orcid":"","institution":"Tufts University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Christina","middleName":"D.","lastName":"Economos","suffix":""},{"id":288319077,"identity":"db91b1be-bccd-42ee-b527-74fbf26ac8b5","order_by":5,"name":"Perrie F. O'Tierney-Ginn","email":"","orcid":"","institution":"Tufts Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Perrie","middleName":"F.","lastName":"O'Tierney-Ginn","suffix":""}],"badges":[],"createdAt":"2024-03-21 19:31:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4145532/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4145532/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54454997,"identity":"a1ebdc1a-1ea0-416f-b311-03515730572d","added_by":"auto","created_at":"2024-04-10 18:50:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":306511,"visible":true,"origin":"","legend":"\u003cp\u003eSimplified stock and flow diagram of the major feedback mechanisms that control maternal insulin and glucose.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4145532/v1/f77b1dc0460ad3a71688ff17.jpeg"},{"id":54455290,"identity":"da5fc25d-3e9a-4899-8ba9-bc1c81983584","added_by":"auto","created_at":"2024-04-10 18:58:34","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":206196,"visible":true,"origin":"","legend":"\u003cp\u003eModel output (continuous lines) compared to measured plasma insulin and glucose data points and 95% confidence intervals collected from MAMMA study participants during early pregnancy (12-16 weeks gestation) (N=28).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4145532/v1/f24a39c51f8e87a0827e2766.jpeg"},{"id":54454999,"identity":"bea497c0-f22e-4b82-a9a6-6d740d331052","added_by":"auto","created_at":"2024-04-10 18:50:35","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165551,"visible":true,"origin":"","legend":"\u003cp\u003eOutput from a system dynamics model designed to replicate maternal insulin and glucose dynamics in early pregnancy (12-16 weeks gestation) under hyperinsulinemic-euglycemic clamp conditions compared to target steady-state levels abstracted from a study in the literature (18).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4145532/v1/49a8da35a8e2059ddf01540e.jpeg"},{"id":54454998,"identity":"47852ea7-a400-4940-a482-da3e6134fdf2","added_by":"auto","created_at":"2024-04-10 18:50:35","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":216486,"visible":true,"origin":"","legend":"\u003cp\u003eOGTT model output that included five adaptations known to occur between early and late pregnancy: 1) increased hepatic glucose production, 2) increase basal insulin production, 3) increased first and second phase insulin secretion in response to a rise in blood glucose levels, 4) decreased effect of insulin on insulin-dependent glucose uptake (i.e., decreased insulin sensitivity), and 5) increased glucose uptake by the fetal-placental unit (34-36 weeks gestation) (N=28).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4145532/v1/2a8bccb7ad2138975cce28db.jpeg"},{"id":56807772,"identity":"d98275ba-2324-4b19-a419-781c03d3e8fa","added_by":"auto","created_at":"2024-05-20 18:23:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1369526,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4145532/v1/5cb75481-0951-4881-80b2-c5cbdd251658.pdf"},{"id":54455001,"identity":"9b3a6b4e-1350-44a4-a386-de30faf18575","added_by":"auto","created_at":"2024-04-10 18:50:35","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":902576,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaltablesandfiguresv3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4145532/v1/97916ac2817af95cf7b202b9.docx"}],"financialInterests":"","formattedTitle":"Modeling Insulin and Glucose Dynamics and Metabolic Adaptions During Pregnancy under Two Testing Conditions: Oral Glucose Tolerance Test and Hyperinsulinemic-Euglycemic Clamp","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eMaternal metabolism during pregnancy can have profound short- and long-term health effects on women and their offspring (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Persistently high maternal glucose and insulin levels that are characteristic of gestational diabetes mellitus (GDM) can drive fetal growth, specifically adipose tissue, which may lead to large-for-gestational age infants as described by the Pedersen hypothesis (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Large-for-gestational age neonates (weight\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;90th percentile) have a higher risk of birth injury and congenital abnormalities, and neonates with high levels of adiposity are more likely to develop obesity and associated health conditions including diabetes and hypertension compared to peers (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). GDM and sub-clinical glucose intolerance during pregnancy also significantly increases pregnant individuals\u0026rsquo; risk of complications and cardiometabolic disease after pregnancy (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Incidence of GDM and sub-clinical glucose intolerance during pregnancy is ~\u0026thinsp;7\u0026ndash;15% and projected to rise significantly in the United States (US) and globally as obesity prevalence and severity among women of reproductive age increases (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStandard prenatal care in the US includes a two-step test for GDM so cases can be managed to avoid negative outcomes associated with excess fetal growth and adiposity. Women receiving prenatal care are screened as a first step between 24\u0026ndash;28 weeks of gestation using a one-hour glucose challenge test (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). If patients screen positive on their initial test, they will undergo a 3-hour oral glucose tolerance test (OGTT) to diagnose GDM. The American Congress of Obstetricians and Gynecologists defines GDM as plasma glucose values of \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;140mg/dL during a 3-hour OGTT (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlucose challenge tests, like most medical screening tools, can produce false negative results. A systematic review of GDM screening tests reported sensitivity of 70\u0026ndash;88% for the glucose challenge test (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Moreover, a lesser-known hypothesis \u0026ndash; the \u0026ldquo;fetal glucose steal hypothesis\u0026rdquo; \u0026ndash; posits that women with impaired glucose tolerance might not be correctly identified with a glucose challenge test or even an OGTT because their developing fetus is rapidly draining the maternal glucose stock over the course of the test (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Interactions between maternal glucose supply and fetal demands create complex dynamics that fluctuate hourly and adapt as gestation progresses.\u003c/p\u003e \u003cp\u003eGiven the rising incidence of obesity and GDM and the potential for missing pregnancies at risk for excess fetal fat accrual, novel approaches for monitoring maternal glucose dynamics and fetal growth over the course of pregnancy are needed. Approaches should be scalable, patient-centered, and make use of minimally invasive technologies and routinely collected patient data. To address this need, we developed a proof-of-concept simulation model of the major physiological feedback mechanisms that drive maternal glucose and insulin dynamics and tested the model under OGTT and gold standard hyperinsulinemic-euglycemic clamp conditions. This study is an early step toward more personalized tools for monitoring maternal glucose dynamics over the course of pregnancy.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSystem Dynamics Modeling Approach\u003c/h2\u003e \u003cp\u003eWe used system dynamics to build a novel simulation model of the major physiological feedback mechanisms producing maternal glucose and insulin dynamics under OGTT and hyperinsulinemic-euglycemic clamp testing protocols in early (12\u0026ndash;16 weeks) and late (34\u0026ndash;36 weeks) pregnancy. We selected system dynamics as a modeling method because it can simulate endogenous relationships between system components as well as accumulations and delays within systems (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The relationships between plasma glucose and insulin \u003cem\u003ein vivo\u003c/em\u003e are characterized by endogenous processes with several critical accumulations and delays (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Existing literature, clinical data, model calibration, and optimization informed model structure and parameters. Supplemental table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows parameter values and sources. Optimization was conducted to estimate parameters that were not available in our clinical data or in the literature. We defined the optimization function as the minimum Total Mean Absolute Percent Error (MAPE) between model output and clinical glucose and insulin data identified via at least 10,000 simulation runs using the Powell algorithm. We calculated the MAPE for both glucose and insulin at each time point where there was data available and added them together to define Total MAPE. The optimization results (i.e., minimizing the Total MAPE), provided estimates for 9 parameters in the OGTT models and 6 parameters in the clamp models. Once the models were optimized, we conducted univariate sensitivity analyses by changing the parameter values by \u0026plusmn;\u0026thinsp;20% over 40,000 simulation runs and reported the effect on insulin and glucose MAPE in tornado plots. This approach identifies the relative impact of each variable on model fit. Vensim DSS version 10 was used to build, calibrate, and optimize the system dynamics model. ChatGPT was used to generate Python code to create figures comparing model output to clinical data and sensitivity graphs(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOral Glucose Tolerance Test Data\u003c/h2\u003e \u003cp\u003eWe used unpublished primary data from the Maternal Metabolic Markers of Infant Adiposity (MAMMA) study to calibrate and optimize the model and conduct model testing under OGTT conditions (IRB# 12903). Healthy pregnant patients ages 18\u0026ndash;45 with a pregravid BMI between 18\u0026ndash;45 kg/m\u003csup\u003e2\u003c/sup\u003e and no previous history of high blood pressure or diabetes in pregnancy, and with no current drug or alcohol use, were eligible for the MAMMA study. Participants were recruited between 2018\u0026ndash;2023 from Tufts Medical Center, Boston, MA. Upon enrollment, participants\u0026rsquo; height and weight were measured, blood pressure was taken, medical history collected by trained research staff, and blood markers of thyroid, liver and kidney function were assessed to ensure participants were healthy enough to participate in the study. Participants completed two clinical visits in early pregnancy (12\u0026ndash;16 weeks) and late pregnancy (34\u0026ndash;36 weeks). Those visits included a 75g three hour-oral glucose tolerance test after an overnight fast. Blood was drawn at baseline and then every 30 minutes for two hours and then after 60 minutes for a total of six measures over 180 minutes. Plasma glucose and insulin were measured via an automated glucose analyzer (YSI 2500, Yellow Springs, Ohio) and ELISA (Human Insulin ELISA, Crystal Chem, Cat#90095) according to the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eHyperinsulinemic-Euglycemic Clamp Data\u003c/h2\u003e \u003cp\u003eIn addition to oral glucose tolerance tests conducted in the MAMMA study, we used results of a hyperinsulinemic-euglycemic clamp study described in the literature to inform our model of maternal glucose and insulin dynamics during early and late pregnancy (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The physiological system that the model summarizes is the same under either testing condition (OGTT vs Clamp); the models differed in the amount and timing of glucose and insulin intake in accordance with each testing condition. Using two tests allowed us to triangulate several unknown parameters and build confidence that the model worked as expected. The participant population and experimental protocol for the hyperinsulinemic-euglycemic clamp conditions is described in-depth elsewhere (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Briefly, the protocol was conducted with 15 obese pregnant women with and without GDM at 12\u0026ndash;14 and 34\u0026ndash;36 weeks gestation (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The clamp procedure began with an overnight fast and involves intravenous infusion of insulin and glucose in order to achieve a steady-state of plasma insulin (approximately 50 uU/mL) and glucose concentrations (approximately 90 mg/dL) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The clamp allows researchers to calculate endogenous glucose synthesis and glucose uptake (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The procedure is considered the gold standard for assessing insulin sensitivity and glucose tolerance, but it is impractical for routine prenatal care.\u003c/p\u003e \u003cp\u003eAfter building a model of insulin and glucose dynamics in early pregnancy under OGTT and clamp conditions, we modified the model parameters to replicate development of insulin resistance over the course of gestation (Supplemental Table\u0026nbsp;1). We modeled the development of insulin resistance over the course of gestation by modifying parameters in the late pregnancy model to reflect known metabolic adaptations. Those metabolic adaptions include: i) increased hepatic glucose production, ii) increased basal insulin production, iii) increased first and second phase insulin secretion in response to a rise in blood glucose levels, and iv) decreased effect of insulin on insulin-dependent glucose uptake (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Collectively, the adaptations create insulin resistance and thus increase the circulating stock of glucose in the maternal blood stream. The increased maternal glucose stock creates a concentration gradient that drives glucose uptake by the placenta (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In addition to the metabolic adaptations listed above, we also increased the rate of glucose uptake by the placenta in the late pregnancy model since the placenta and fetus have greater glucose requirements in late compared to early pregnancy.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMAMMA Study Participants\u003c/h2\u003e \u003cp\u003eData for twenty-eight MAMMA participants were included in this modeling study. The average age of study participants was 31.9 years old. Average pregravid body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e) was 24.5, with 10 participants considered overweight (BMI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;25) and 2 considered obese (BMI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30). Two participants self-reported Hispanic or Latino ethnicity. Eighteen participants self-reported being Caucasian, six Black or African American, two Asian, and two unknown or not reported. Two participants developed GDM during the study and 14 participants were nulliparous. Five MAMMA study participants were not included in this modeling study because they may have experienced fetal glucose steal since they had large-for-gestational-age babies but were not diagnosed with GDM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSimplified Model Structure\u003c/h2\u003e \u003cp\u003eThe simulation model presented in this study portrays major maternal glucose and insulin dynamics. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a simplified stock and flow diagram to illustrate key elements of the model. The simulation model contains more variables and equations (see Suppl materials). Variables within squares are stocks that can accumulate or dissipate according to rates of inflows and outflows, indicated by pipes with valves. Insulin is represented with two stocks, one showing levels of insulin in plasma and the other showing levels of insulin in interstitial fluid. Previous experimental and modeling studies highlighted the need to represent insulin in two compartments since they help explain an observed delay between rises in plasma insulin and insulin\u0026rsquo;s action to increase glucose uptake by peripheral tissues (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Experimental studies show a 3:2 ratio of insulin in plasma to insulin in interstitial fluid, suggesting about one third of plasma insulin does not enter the interstitial fluid (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The stock of glucose increases via two main inflows: glucose intake and endogenous glucose production by the liver. Glucose can leave plasma through non-insulin mediated uptake by the central nervous system and other tissue and through insulin-mediated uptake by skeletal muscle, white adipose, and liver tissue, all of which occur over the course of an OGTT. The first major balancing loop (B1) shows that a rise in glucose levels triggers an increase in plasma insulin levels, which reduces hepatic glucose production, thus reducing the flow of glucose into the bloodstream. The second balancing feedback loop (B2) shows that a rise in blood glucose levels causes an increase in the rate of insulin secretion, which leads to an increase in the rate of insulin-dependent glucose clearance and a subsequent lowering of the stock of blood glucose.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026thinsp;+\u0026thinsp;Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e legend here\u0026gt;\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eComparing Model Output With Clinical Data Under OGTT And Hyperinsulinemic-Euglycemic Clamp Conditions\u003c/h2\u003e \u003cp\u003eThe model output closely resembled median plasma insulin and mean glucose levels measured in MAMMA participants during a 180-minute OGTT in early pregnancy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Fit statistics indicate a good fit between model output and data (e.g., OGTT mean glucose R\u003csup\u003e2\u003c/sup\u003e in early pregnancy: 0.97, OGTT median insulin R\u003csup\u003e2\u003c/sup\u003e in early pregnancy: 0.98, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The R-squared for the clamp models is not available because the observed data values remain constant throughout the model. Median insulin was used because the ratio of mean to median values was greater than 0.1, indicating that outliers might have a large effect on the mean. When glucose and insulin intake were switched to reflect hyperinsulinemic-euglycemic clamp conditions (i.e., infusion of both glucose and insulin) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), model output achieved a steady-state as intended (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026thinsp;+\u0026thinsp;Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e legend here\u0026gt;\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026thinsp;+\u0026thinsp;Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e legend here\u0026gt;\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical fit data comparing clinical measures to output from a novel simulation model of maternal glucose and insulin dynamics under oral glucose tolerance test and hyperinsulinemic-euglycemic clamp conditions in early (12\u0026ndash;16 weeks) and late (\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) weeks gestation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOral glucose tolerance test (OGTT) models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEarly pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLate pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHyperinsulinemic-euglycemic clamp models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEarly pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLate pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eRMSE\u0026thinsp;=\u0026thinsp;Root mean square error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSimulating The Development of Insulin Resistance Due to Metabolic Adaptations During Pregnancy\u003c/h2\u003e \u003cp\u003eAs described above, we modified model parameters to reflect five adaptations that occur as gestation progresses. The adaptations are: 1) increased hepatic glucose production, 2) increase basal insulin production, 3) increased first and second phase insulin secretion in response to a rise in blood glucose levels, 4) decreased effect of insulin on insulin-dependent glucose uptake (i.e., decreased insulin sensitivity), and 5) increased glucose uptake by the fetal-placental unit (Suppl Table\u0026nbsp;1). The late pregnancy model fit the data well (e.g., OGTT glucose R\u003csup\u003e2\u003c/sup\u003e in late pregnancy: 0.97, OGTT insulin R\u003csup\u003e2\u003c/sup\u003e in late pregnancy: 0.99, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA visual comparison of Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e clearly shows the development of insulin resistance over the course of gestation, indicated by the increase in insulin and glucose levels in response to an oral glucose load and the elevated levels of both glucose and insulin over the course of the OGTT in late compared to early pregnancy. The same physiological parameter changes were applied to the clamp model conditions. Similar to early pregnancy results, the late pregnancy clamp model output replicated the expected steady-state levels (Supplemental Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;+\u0026thinsp;Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e legend here\u0026gt;\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003eOverall, our sensitivity analysis indicated that changing parameters by 20% did not improve or diminish model fit to clinical data by more than about 2% (Supplemental Fig.\u0026nbsp;2). Since MAPE was a composite of model fit to glucose and insulin data, there are opportunities to improve model fit by \u0026lt;\u0026thinsp;2% that were not implemented because they might improve fit with glucose data, for example, at the expense of fit with insulin data, and vice versa. A second reason we did not further improve model fit to the data was to avoid overfitting the model and thus reducing model generalizability. There was no single parameter or set of parameters that affected model fit most across the glucose testing conditions in early or late pregnancy. For example, in the early pregnancy OGTT model, changes in parameters that govern the amount and timing of glucose ingested and variables that govern maternal glucose uptake are more sensitive to changes than other variables, whereas variables that govern insulin response to a rise in blood glucose levels are more sensitive to changes in late pregnancy. At both time points, variables involved in insulin moving through the two model compartments are the least sensitive to parameter changes. The clamp models were less sensitive to parameter changes than the OGTT models, with most parameter changes corresponding to \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;1% change in total MAPE.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWith obesity prevalence high (29% of reproductive age women in the US (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)) and rising, incidence of GDM is also increasing, putting infants and their mothers at risk for adverse health outcomes during and after birth (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Elevated glucose and insulin during pregnancy that characterize GDM can be managed, lowering the risk of large for gestational age infants and infants with high levels of adiposity (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). It is paramount to identify women with GDM and offer treatment. The novel simulation model described in this study is an initial step towards personalized tools for monitoring maternal glucose dynamics over the course of pregnancy that uses biomarkers that are routinely collected during prenatal care. The model output closely resembles plasma insulin and glucose in early and late pregnancy among twenty-eight research participants that underwent OGTTs, and it also resembles steady-state levels under hyperinsulinemic-euglycemic clamp conditions. To our knowledge, this is the first system dynamics model exploring changes in glucose and insulin dynamics known to develop over the course of pregnancy.\u003c/p\u003e \u003cp\u003eScientists have studied glucose and insulin dynamics for decades in a quest to understand and treat diabetes, which affects about 422\u0026nbsp;million people worldwide and is estimated to contribute to one in nine deaths among adults ages 20\u0026ndash;79 years old (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Many mathematical models have been developed to move the field closer to a \u0026ldquo;closed loop\u0026rdquo; system for managing insulin administration in order to reduce the burden of care on individuals with diabetes (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Diabetes during pregnancy is a particularly important topic to study for two reasons: 1) if untreated it can increase risks for negative health outcomes for the pregnant patient and offspring, and 2) insulin resistance, a state of decreased insulin sensitivity that is on a continuum whose upper end is called diabetes, naturally develops over the course of pregnancy (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Studying the development of physiological insulin resistance that occurs during pregnancy and rapidly resolves after delivery gives scientists a unique window into metabolic processes that would not be possible to experimentally manipulate in humans. The use of biomarkers and other data that are routinely collected during prenatal care in conjunction with mathematical modeling is a promising strategy for studying glucose and insulin dynamics in humans in pursuit of developing highly effective and acceptable treatments.\u003c/p\u003e \u003cp\u003eOur system dynamics model contained several key features that help explain its ability to replicate clinical data. There are important delays and functional forms that influence the dynamic relationship between glucose and insulin (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). There is a delay in the dampening effect on plasma insulin levels and hepatic glucose synthesis suppression (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). There is also a delay as insulin moves from plasma to interstitial fluid where it binds with insulin receptors on the surface of peripheral cells and initiates the translocation of GLUT4 to the cell surface (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Experimental evidence shows that insulin release rate follows a two-phase pattern (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e); that non-linear rate is critical for accurately simulating insulin accumulation and action. Two reviews of mathematical models of glucose and insulin dynamics noted similar features in hyperinsulinemic-euglycemic clamp models (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Challenges of modeling oral glucose intake compared to intravenous clinical tests include factors such as the rate of gastric emptying, extent of glucose absorption, and the effect of hormones such as incretins (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). We addressed these challenges by modeling exogenous glucose intake as a rate that is dependent on the stock of ingested glucose. This created an exponential decline in the rate of glucose uptake over the course of an OGTT, which represented a simplified yet sufficient function for our model.\u003c/p\u003e \u003cp\u003eOur study has strengths and limitations. We used a small sample to calibrate the model, potentially limiting generalizability. Glucose and insulin production, signaling, metabolism, and other biochemical processes are characterized in exceptional detail that was not included in the model presented in this study (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). An important strength is that we used a dynamic modeling approach to study a dynamic biological process. Another strength is our ability to reproduce two glucose tolerance protocols, an OGTT and a hyperinsulinemic-euglycemic clamp, by modifying relevant model parameters (e.g., rate of glucose intake, rate of insulin secretion) within the same model structure. To our knowledge, no other published model can reproduce clinical data under multiple testing protocols (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are a number of opportunities to expand the model presented here to answer important research questions. For example, future models will include fetal glucose and insulin dynamics to test whether those additions might allow us to interrogate the fetal glucose steal hypothesis, a theoretical explanation for why some pregnant women are not diagnosed with GDM yet deliver babies with macrosomia (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Study results could also inform considerations for different approaches to identify impaired glucose tolerance during pregnancy that might include: 1) greater attention to plasma insulin in addition to the current emphasis on maternal glucose levels, 2) utilizing continuous glucose monitoring to gather information about maternal glucose levels outside of the conditions tested with a routine OGTT, and 3) additional screening at multiple time points during pregnancy for at-risk individuals. Expanding this model to include details about the placenta as the interface between fetal nutrient glucose demands and maternal supply is another potential direction, as is adding lipid handling dynamics since insulin has major effects on lipid biosynthesis and metabolism. The placenta secretes hormones thought to promote insulin resistance as pregnancy progresses (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), so there is an opportunity to explore how placental growth and hormone production influence the development of insulin resistance with advancing gestation. Emerging evidence suggests that obesity and its effects on maternal metabolism may also increase risk of preterm delivery due to placental growth dysregulation (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). We could use a model to elucidate mechanisms underlying this observation and test potential intervention effects aiming to promote healthy fetal growth and prevent extremes on either end of the growth spectrum (i.e., small- or large-for-gestational-age infants). Validating the model with a larger dataset would be beneficial, especially if it afforded the opportunity to study differences in model parameter values by race and ethnicity since evidence suggests potential differences in glucose metabolism between racial groups (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The model could be further developed for individual-level tailoring and clinical decision-making. A study of a federally qualified health system with significantly better maternal and neonatal outcomes than surrounding clinics (e.g., lower rate of preterm delivery, high rates of breastfeeding) found that participants described personalized care as a critical factor that shaped their positive prenatal care experiences (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Finally, the model could be adapted to become a teaching tool for health professionals that care for patients experiencing diabetes or impaired glucose tolerance (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWe developed a novel simulation model to show how a combination of metabolic adaptations during pregnancy can explain the observed development of insulin resistance the occurs between early to late pregnancy. We included key delays in insulin action, an innovative approach to model glucose intake during an OGTT, and used several testing conditions to inform and validate the model. The model output aligned with plasma insulin and glucose in early and late pregnancy among participants (N\u0026thinsp;=\u0026thinsp;28) under measured OGTT and simulated hyperinsulinemic-euglycemic clamp conditions. This is an initial step toward developing a personalized tool for monitoring maternal glucose dynamics to improve prenatal care, especially for pregnancies complicated by obesity and/or GDM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Health Sciences Institutional Review Board at Tufts University approved this study (IRB#12903), and all participants were enrolled following written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available by request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eL. Calancie is supported by K12HD092535 from NIH/NICHD and Tufts Building Interdisciplinary Research Careers in Women\u0026apos;s Health (BIRCWH) K12 Career Development. The MAMMA study is supported by R01HD091054 from NIH/NICHD (PI: O\u0026rsquo;Tierney-Ginn) and the National Center for Advancing Translational Sciences, National Institutes of Health award number, UM1TR004398. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLC\u003c/strong\u003e contributed to study conception, design, analysis, data interpretation, and drafted the manuscript; \u003cstrong\u003eMSJ\u003c/strong\u003e contributed to study design, analysis, data interpretation, and manuscript revisions; \u003cstrong\u003eAA\u003c/strong\u003e contributed to analysis, data interpretation, and manuscript revisions; \u003cstrong\u003eTM\u003c/strong\u003e contributed to study design and data acquisition; \u003cstrong\u003eCDE\u003c/strong\u003e contributed to manuscript revision; and \u003cstrong\u003ePOTG\u003c/strong\u003e contributed to study conception, design, data acquisition, data interpretation, and manuscript revision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge all the MAMMA study participants and the data collection team.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamo KB, Ferraro ZM, Brett KE. 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Syst Dyn Rev. 2015;31(4):250\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gestation, pregnancy, gestational diabetes mellitus, system dynamics, glucose, maternal, metabolism, systems biology, in silico, observational cohort","lastPublishedDoi":"10.21203/rs.3.rs-4145532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4145532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMaternal metabolism has important short- and long-term implications for mothers and their infants. Elevated levels of circulating maternal glucose and insulin are associated with large for gestational age infants and increased neonatal adiposity, both of which can have negative health effects. Assessing maternal glucose and insulin dynamics during pregnancy is important for identifying women in need of intervention and has the potential for informing personalized prenatal care.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe developed a novel system dynamics simulation model that estimates plasma insulin and glucose levels in early (12\u0026ndash;16 weeks) and late (34\u0026ndash;36 weeks) pregnancy under two clinical testing conditions: a 3-hour 75g fasted oral glucose tolerance test, and 3-hr fasted hyperinsulinemic-euglycemic clamp conditions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eModel output closely resembled research data collected from 28 racially and ethnically diverse participants at both time points (e.g., OGTT glucose R\u003csup\u003e2\u003c/sup\u003e in early pregnancy: 0.97, OGTT insulin R\u003csup\u003e2\u003c/sup\u003e in early pregnancy: 0.98). The late pregnancy model includes five known metabolic adaptations that occur over the course of gestation, which contribute to the development of maternal insulin resistance. This physiologic insulin resistance in pregnancy facilitates nutrient availability to support fetal growth as gestation progresses.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study is an initial step toward developing a personalized tool for monitoring maternal glucose dynamics to improve prenatal care, especially for pregnancies complicated by obesity and/or GDM. The novel simulation model shows how a combination of metabolic adaptations during pregnancy can explain the observed development of insulin resistance the occurs between early to late pregnancy. We included key delays in insulin action, an innovative approach to model glucose intake during an OGTT, and used several testing conditions to inform and validate the model. The model output aligned with plasma insulin and glucose in early and late pregnancy among participants (N\u0026thinsp;=\u0026thinsp;28) under measured OGTT and simulated hyperinsulinemic-euglycemic clamp conditions.\u003c/p\u003e","manuscriptTitle":"Modeling Insulin and Glucose Dynamics and Metabolic Adaptions During Pregnancy under Two Testing Conditions: Oral Glucose Tolerance Test and Hyperinsulinemic-Euglycemic Clamp","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 18:50:30","doi":"10.21203/rs.3.rs-4145532/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b4bef9b-ac16-412c-aa8e-58429b7c17e8","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-03T08:41:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 18:50:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4145532","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4145532","identity":"rs-4145532","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}