Hyperglycaemic Preterm Babies Have Sex Differences in Insulin Secretion

Neonatal dysglycemia: a review of dysglycemia in relation to brain health and neurodevelopmental outcomes

Neonatal dysglycemia has been a longstanding interest of research in neonatology. Adverse outcomes from hypoglycemia were recognized early but are still being characterized. Premature infants additionally introduced and led the reflection on the importance of neonatal hyperglycemia. Cohorts of infants following neonatal encephalopathy provided further information about the impacts of hypoglycemia and, more recently, highlighted hyperglycemia as a central concern for this population. Innovative studies exposed the challenges of management of neonatal glycemic levels with a "u-shape" relationship between dysglycemia and adverse neurological outcomes. Lately, glycemic lability has been recognized as a key factor in adverse neurodevelopmental outcomes. Research and new technologies, such as MRI and continuous glucose monitoring, offered novel insight into neonatal dysglycemia. Combining clinical, physiological, and epidemiological data allowed the foundation of safe operational definitions, including initiation of treatment, to delineate neonatal hypoglycemia as ≤47 mg/dL, and >150-180 mg/dL for neonatal hyperglycemia. However, questions remain about the appropriate management of neonatal dysglycemia to optimize neurodevelopmental outcomes. Research collaborations and clinical trials with long-term follow-up and advanced use of evolving technologies will be necessary to continue to progress the fascinating world of neonatal dysglycemia and neurodevelopment outcomes. IMPACT STATEMENT: Safe operational definitions guide the initiation of treatment of neonatal hypoglycemia and hyperglycemia. Innovative studies exposed the challenges of neonatal glycemia management with a "u-shaped" relationship between dysglycemia and adverse neurological outcomes. The importance of glycemic lability is also being recognized. However, questions remain about the optimal management of neonatal dysglycemia to optimize neurodevelopmental outcomes. Research collaborations and clinical trials with long-term follow-up and advanced use of evolving technologies will be necessary to progress the fascinating world of neonatal dysglycemia and neurodevelopment outcomes.

Hyperglycemia and prematurity: a narrative review

Hyperglycemia is commonly encountered in extremely preterm newborns and physiologically can be attributed to immaturity in several biochemical pathways related to glucose metabolism. Although hyperglycemia is associated with a variety of adverse outcomes frequently described in this population, evidence for causality is lacking. Variations in definitions and treatment approaches have further complicated the understanding and implications of hyperglycemia on the immediate and long-term effects in preterm newborns. In this review, we describe the relationship between hyperglycemia and organ development, outcomes, treatment options, and potential gaps in knowledge that need further research. IMPACT: Hyperglycemia is common and less well described than hypoglycemia in extremely preterm newborns. Hyperglycemia can be attributed to immaturity in several cellular pathways involved in glucose metabolism in this age group. Hyperglycemia has been shown to be associated with a variety of adverse outcomes frequently described in this population; however, evidence for causality is lacking. Variations in definitions and treatment approaches have complicated the understanding and the implications of hyperglycemia on the immediate and long-term effects outcomes. This review describes the relationship between hyperglycemia and organ development, outcomes, treatment options, and potential gaps in knowledge that need further research.

Model-based subcutaneous insulin for glycemic control of pre-term infants in the neonatal intensive care unit

Hyperglycaemia is a common problem in neonatal intensive care units (NICUs). Achieving good control can result in better outcomes for patients. However, good control is difficult, where poor control and resulting hypoglycaemia reduces outcomes and confounds results. Clinically validated models can provide good control, and subcutaneous insulin delivery can provide more options for insulin therapy for clinicians. However, this combination has only been significantly utilised in adult outpatient diabetes, but could hold benefit for treating NICU infants. This research combines a well-validated NICU metabolic model with subcutaneous insulin kinetics models to assess the feasibility of a model-based approach. Clinical data from 12 very/extremely pre-mature infants was collected for an average study duration of 10.1 days. Blood glucose, interstitial and plasma insulin, as well as subcutaneous and local insulin were modelled, and patient-specific insulin sensitivity profiles were identified for each patient. Modelling error was low, where the cohort median [IQR] mean percentage error was 0.8 [0.3 3.4] %. For external validation, insulin sensitivity was compared to previous NICU cohorts using the same metabolic model, where overall levels of insulin sensitivity were similar. Overall, the combined system model accurately captured observed glucose and insulin dynamics, showing the potential for a model-based approach to glycaemic control using subcutaneous insulin in this cohort. The results justify further model validation and clinical trial research to explore a model-based protocol.

Preterm birth and metabolic implications on later life: A narrative review focused on body composition

Preterm newborn infants are characterized by low body weight and lower fat mass at birth compared with full-term newborn neonates. Conversely, at term corrected age, body fat mass is more represented in preterm newborn infants, causing a predisposition to developing metabolic syndrome and cardiovascular diseases in later life with a different risk profile in men as compared with women. Postnatal growth is a complex change in anthropometric parameters and body composition. Both quantity and quality of growth are regulated by several factors such as fetal programming, early nutrition, and gut microbiota. Weight gain alone is not an optimal indicator of nutritional status as it does not accurately describe weight quality. The analysis of body composition represents a potentially useful tool to predict later metabolic and cardiovascular risk as it detects the quality of growth by differentiating between fat and lean mass. Longitudinal follow-up of preterm newborn infants could take advantage of body composition analysis in order to identify high-risk patients who apply early preventive strategies. This narrative review aimed to examine the state-of-the-art body composition among born preterm children, with a focus on those in the pre-school age group.

Variability in Estimated Modelled Insulin Secretion

BACKGROUND

The ability to measure insulin secretion from pancreatic beta cells and monitor glucose-insulin physiology is vital to current health needs. C-peptide has been used successfully as a surrogate for plasma insulin concentration. Quantifying the expected variability of modelled insulin secretion will improve confidence in model estimates.

METHODS

Forty-three healthy adult males of Māori or Pacific peoples ancestry living in New Zealand participated in an frequently sampled, intravenous glucose tolerance test (FS-IVGTT) with an average age of 29 years and a BMI of 33 kg/m². A 2-compartment model framework and standardized kinetic parameters were used to estimate endogenous pancreatic insulin secretion from plasma C-peptide measurements. Monte Carlo analysis (N = 10 000) was then used to independently vary parameters within ±2 standard deviations of the mean of each variable and the 5th and 95th percentiles determined the bounds of the expected range of insulin secretion. Cumulative distribution functions (CDFs) were calculated for each subject for area under the curve (AUC) total, AUC Phase 1, and AUC Phase 2. Normalizing each AUC by the participant's median value over all N = 10 000 iterations quantifies the expected model-based variability in AUC.

RESULTS

Larger variation is found in subjects with a BMI > 30 kg/m², where the interquartile range is 34.3% compared to subjects with a BMI ≤ 30 kg/m² where the interquartile range is 24.7%.

CONCLUSIONS

Use of C-peptide measurements using a 2-compartment model and standardized kinetic parameters, one can expect ~±15% variation in modelled insulin secretion estimates. The variation should be considered when applying this insulin secretion estimation method to clinical diagnostic thresholds and interpretation of model-based analyses such as insulin sensitivity.

Long-Term Outcomes after Early Neonatal Hyperglycemia in VLBW Infants: A Systematic Review

INTRODUCTION

Long-term effects of early hyperglycemia in VLBW infants are poorly characterized. The objective of this study was to systematically review the effect of early hyperglycemia on growth, metabolic health, and neurodevelopment after neonatal intensive care unit discharge in VLBW infants.

METHODS

The systematic review was conducted in accordance with the PRISMA guidelines. A study protocol was registered in PROSPERO (CRD42019123335). Data sources included Ovid MEDLINE, Embase, Cochrane Library, CINAHL, and Scopus. Selected studies included infants with a blood glucose concentration >150 mg/dL (8.3 mmol/L) during the first 28 days of life, a gestational age (GA) <32 weeks, and/or a birth weight <1,500 g and longitudinal data on growth, metabolic health, or neurodevelopment outcomes. The GRADE system was used to assess quality of evidence.

RESULTS

Eight studies (n = 987 infants) reported long-term outcomes from 4-month corrected GA to 7 years old. Most studies compared long-term outcomes of preterm infants with and without hyperglycemia. Two studies addressed outcomes related to interventions following early hyperglycemia. Some studies found differences in growth, metabolic health, and neurodevelopment outcomes between VLBW preterm infants with hyperglycemia and without hyperglycemia, while other studies found no differences between groups. The overall graded quality of evidence was low.

CONCLUSIONS

Well-designed randomized controlled and prospective studies are necessary to determine the effect of early hyperglycemia and its treatment on later metabolic and neurodevelopmental outcomes in VLBW infants. Authors propose a potential study design for standardizing the assessment of long-term metabolic and neurodevelopmental outcomes following early hyperglycemia in preterm infants.

Insulin sensitivity in critically ill patients: are women more insulin resistant?

Background

Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care.

Methods

Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal.

Results

Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent.

Conclusion

Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.

Insulin sensitivity in critically ill patients: are women more insulin resistant?

BACKGROUND

Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care.

METHODS

Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal.

RESULTS

Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent.

CONCLUSION

Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.

Hyperglycemia in Extremely Preterm Infants

Hyperglycemia after birth is common in extremely preterm infants (<28 weeks of gestation). Lower gestational age, lower birthweight, presence of severe illness, and higher parenteral glucose intake increase the risk for hyperglycemia, while provision of higher amounts of amino acids and lipids in parenteral nutrition and early initiation and faster achievement of full enteral feeding decrease the risk. Hyperglycemia is associated with increased mortality and morbidity in the neonatal period. Limited data show an association with long-term adverse effects on growth, neurodevelopment, and cardiovascular and metabolic health. Lowering the glucose infusion rate and administration of insulin are the 2 treatment options. Lowering the glucose infusion could lead to calorie deficits and long-term adverse effects on growth and neurodevelopment. Conversely, insulin use increases the risk for hypoglycemia and requires close blood glucose monitoring and frequent adjustments to glucose infusion and insulin dosage. Randomized trials of varying strategies of nutrient provision and/or insulin therapy and long-term follow-up are needed to improve clinical care and overall health of extremely preterm infants with hyperglycemia.

Do preterm girls need different nutrition to preterm boys? Sex-specific nutrition for the preterm infant

Boys born preterm are recognised to be at higher risk of adverse outcomes than girls born preterm. Despite advances in neonatal intensive care and overall improvements in neonatal morbidity and mortality, boys born preterm continue to show worse short- and long-term outcomes than girls. Preterm birth presents a nutritional crisis during a critical developmental period, with postnatal undernutrition and growth-faltering common complications of neonatal intensive care. Furthermore, this preterm period corresponds to that of rapid in utero brain growth and development, and the developmental window relating to foetal programming of adult non-communicable diseases, the prevalence of which are associated both with preterm birth and sex. There is increasing evidence to show that from foetal life, boys and girls have different responses to maternal nutrition, that maternal breastmilk composition differs based on foetal sex and that early neonatal nutritional interventions affect boys and girls differently. This narrative review examines the evidence that sex is an important moderator of the outcomes of preterm nutrition intervention, and describes what further knowledge is required before providing nutrition intervention for infants born preterm based on their sex. IMPACT: This review examines the increasing evidence that boys and girls respond differently to nutritional stressors before birth, that maternal breastmilk composition differs by foetal sex and that nutritional interventions have different responses based on infant sex. Boys and girls born preterm are given standard nutritional support which does not take infant sex into account, and few studies of neonatal nutrition consider infant sex as a potential mediator of outcomes. By optimising early nutrition for boys and girls born preterm, we may improve outcomes for both sexes. We propose future studies of neonatal nutritional interventions should consider infant sex.

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, E_rs, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future E_rs values using retrospective patient data to develop and validate a model indicating future intra-patient variability of E_rs. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5-95% and the 25-75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future E_rs values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.

Modelling intestinal glucose absorption in premature infants using continuous glucose monitoring data

BACKGROUND

Model-based glycaemic control protocols have shown promise in neonatal intensive care units (NICUs) for reducing both hyperglycaemia and insulin-therapy driven hypoglycaemia. However, current models for the appearance of glucose from enteral feeding are based on values from adult intensive care cohorts. This study aims to determine enteral glucose appearance model parameters more reflective of premature infant physiology.

METHODS

Peaks in CGM data associated with enteral milk feeds in preterm and term infants are used to fit a two compartment gut model. The first compartment describes glucose in the stomach, and the half life of gastric emptying is estimated as 20 min from literature. The second compartment describes glucose in the small intestine, and absorption of glucose into the blood is fit to CGM data. Two infant cohorts from two NICUs are used, and results are compared to appearances derived from data in highly controlled studies in literature.

RESULTS

The average half life across all infants for glucose absorption from the gut to the blood was 50 min. This result was slightly slower than, but of similar magnitude to, results derived from literature. No trends were found with gestational or postnatal age. Breast milk fed infants were found to have a higher absorption constant than formula fed infants, a result which may reflect known differences in gastric emptying for different feed types.

CONCLUSIONS

This paper presents a methodology for estimation of glucose appearance due to enteral feeding, and model parameters suitable for a NICU model-based glycaemic control context.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Insulin kinetics and the Neonatal Intensive Care Insulin-Nutrition-Glucose (NICING) model

BACKGROUND

Models of human glucose-insulin physiology have been developed for a range of uses, with similarly different levels of complexity and accuracy. STAR (Stochastic Targeted) is a model-based approach to glycaemic control. Elevated blood glucose concentrations (hyperglycaemia) are a common complication of stress and prematurity in very premature infants, and have been associated with worsened outcomes and higher mortality. This research identifies and validates the model parameters for model-based glycaemic control in neonatal intensive care.

METHODS

C-peptide, plasma insulin, and BG from a cohort of 41 extremely pre-term (median age 27.2 [26.2-28.7] weeks) and very low birth weight infants (median birth weight 839 [735-1000] g) are used alongside C-peptide kinetic models to identify model parameters associated with insulin kinetics in the NICING (Neonatal Intensive Care Insulin-Nutrition-Glucose) model. A literature analysis is used to determine models of kidney clearance and body fluid compartment volumes. The full, final NICING model is validated by fitting the model to a cohort of 160 glucose, insulin, and nutrition data records from extremely premature infants from two different NICUs (neonatal intensive care units).

RESULTS

Six model parameters related to insulin kinetics were identified. The resulting NICING model is more physiologically descriptive than prior model iterations, including clearance pathways of insulin via the liver and kidney, rather than a lumped parameter. In addition, insulin diffusion between plasma and interstitial spaces is evaluated, with differences in distribution volume taken into consideration for each of these spaces. The NICING model was shown to fit clinical data well, with a low model fit error similar to that of previous model iterations.

CONCLUSIONS

Insulin kinetic parameters have been identified, and the NICING model is presented for glycaemic control neonatal intensive care. The resulting NICING model is more complex and physiologically relevant, with no loss in bedside-identifiability or ability to capture and predict metabolic dynamics.

A C-Peptide-Based Model of Pancreatic Insulin Secretion in Extremely Preterm Neonates in Intensive Care

BACKGROUND

Model-based glycemic control relies on sufficiency of underlying models to describe underlying patient physiology. In particular, very preterm infant glucose-insulin metabolism can differ significantly from adults, and is relatively unstudied. In this study, C-peptide concentrations are used to develop insulin-secretion models for the purposes of glycemic control in neonatal intensive care.

METHODS

Plasma C-peptide, insulin, and blood glucose concentrations (BGC) were retrospectively analyzed from a cohort of 41 hyperglycemic very preterm (median age 27.2 [26.2-28.7] weeks) and very low birth-weight infants (median birth weight 839 [735-1000] g). A 2-compartment model of C-peptide kinetics was used to estimate insulin secretion. Insulin secretion was examined with respect to nutritional intake, exogenous and plasma insulin concentration, and BGC.

RESULTS

Insulin secretion was found to be highly variable between patients and over time, and could not be modeled with respect to age, weight, or protein or dextrose intake. In 13 of 54 samples exogenous insulin was being administered, and insulin secretion was lower. However, low data numbers make this result inconclusive. Insulin secretion was found to increase with BG, with a stronger association in female infants than males (R(2) = .51 vs R(2) = .13, and R(2) = .26 for the combined cohort).

CONCLUSIONS

A sex-based insulin secretion model was created and incorporated into a model-based glycemic control framework. Nutritional intake did not predict insulin secretion, indicating that insulin secretion is a complex function of a number of metabolic factors.