Successful Anesthetic Management of an Adult Patient With Glycogen Storage Disease Type 1 During Liver Transplant: A Case Report.

Glycogen storage disease type 1 is a congenital abnormality of metabolism caused by the deficiency of the glucose-6-phosphatase enzyme, essential in glucose homeostasis. Patients with this disease are at high risk of developing hypoglycemia, hyperlipidemia, lactic acidemia, growth retardation, neutropenia, inflammatory bowel disease, and many other severe complications, such as hepatic adenomas converting into hepatocellular carcinomas. To prevent these complications, a liver transplant is the ultimate method of treatment. We present the successful anesthesia management for a 21-year-old man who had gross hepatomegaly, severe hypoglycemia, and hyperlactatemia and who received a liver transplant from his mother, which is a substantial challenge for anesthesiologists. Anesthesiologists should know the underlying pathophysiological condition and perform a comprehensive preoperative evaluation to determine the correct anesthesia plan in patients with glycogen storage disease type 1 who will undergo an orthotopic liver transplant due to multiple system disorders. Successful perioperative management of patients with glycogen storage disease type 1 relies on effective communication and collaboration between specialists through a multidisciplinary team approach.

Malignant solitary fibrous tumor of the kidney with IGF2 secretion and without hypoglycemia.

BACKGROUND: Solitary fibrous tumor (SFT) is a rare fibroblastic mesenchymal tumor that mostly involves the pleura and infrequently involves extra-pleural sites. De novo SFT of the kidney is uncommon, and malignant SFT is extremely rare. CASE PRESENTATION: We report a case of a 51-year-old man with a large malignant SFT in the left kidney. Pathological examination confirmed the diagnosis of SFT based on typical morphology, nuclear STAT6 expression, and NAB2-STAT6 gene fusion. The malignant subtype was determined by a large tumor size (≥ 15 cm) and high mitotic counts (8/10 high-power fields). KRAS mutation was identified by DNA sequencing. Insulin-like growth factor 2 (IGF2) was diffusely and strongly expressed in tumor cells, however, hypoglycemia was not observed. Hyperglycemia and high adrenocorticotropic hormone (ACTH) concentration were observed one month after surgery. Hormone measurements revealed normal blood cortisol and aldosterone levels, and increased urinary free cortisol level. A pituitary microadenoma was identified using brain magnetic resonance imaging, which may be responsible for the promotion of hyperglycemia. CONCLUSIONS: We report a case of renal malignant SFT with a KRAS mutation, which was previously unreported in SFT and may be associated with its malignant behavior. Additionally, we emphasize that malignant SFT commonly causes severe hypoglycemia due to the production of IGF2. However, this effect may be masked by the presence of other lesions that promote hyperglycemia. Therefore, when encountering a malignant SFT with diffuse and strong IGF2 expression and without hypoglycemia, other lesions promoting hyperglycemia need to be ruled out.

[Personalized glycemic management for patients with diabetic ketoacidosis based on machine learning].

OBJECTIVE: To explore the optimal blood glucose-lowering strategies for patients with diabetic ketoacidosis (DKA) to enhance personalized treatment effects using machine learning techniques based on the United States Critical Care Medical Information Mart for Intensive Care- IV (MIMIC- IV). METHODS: Utilizing the MIMIC- IV database, the case data of 2 096 patients with DKA admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center from 2008 to 2019 were analyzed. Machine learning models were developed, and receiver operator characteristic curve (ROC curve) and precision-recall curve (PR curve) were plotted to evaluate the model's effectiveness in predicting four common adverse outcomes: hypoglycemia, hypokalemia, reductions in Glasgow coma scale (GCS), and extended hospital stays. The risk of adverse outcomes was analyzed in relation to the rate of blood glucose decrease. Univariate and multivariate Logistic regression analyses were conducted to examine the relationship between relevant factors and the risk of hypokalemia. Personalized risk interpretation methods and predictive technologies were applied to individualize the analysis of optimal glucose control ranges for patients. RESULTS: The machine learning models demonstrated excellent performance in predicting adverse outcomes in patients with DKA, with areas under the ROC curve (AUROC) and 95% confidence interval (95%CI) for predicting hypoglycemia, hypokalemia, GCS score reduction, and extended hospital stays being 0.826 (0.803-0.849), 0.850 (0.828-0.870), 0.925 (0.903-0.946), and 0.901 (0.883-0.920), respectively. Analysis of the relationship between the rate of blood glucose reduction and the risk of four adverse outcomes showed that a maximum glucose reduction rate > 6.26 mmol×L-1×h-1 significantly increased the risk of hypoglycemia (P < 0.001); a rate > 2.72 mmol×L-1×h-1 significantly elevated the risk of hypokalemia (P < 0.001); a rate > 5.53 mmol×L-1×h-1 significantly reduced the risk of GCS score reduction (P < 0.001); and a rate > 8.03 mmol×L-1×h-1 significantly shortened the length of hospital stay (P < 0.001). Multivariate Logistic regression analysis indicated significant correlations between maximum bicarbonate levels, blood urea nitrogen levels, and total insulin doses with the risk of hypokalemia (all P < 0.01). In terms of establishing personalized optimal treatment thresholds, assuming optimal glucose reduction thresholds for hypoglycemia, hypokalemia, GCS score reduction, and extended hospital stay were x1, x2, x3, x4, respectively, the recommended glucose reduction rates to minimize the risks of hypokalemia and hypoglycemia should be ≤min{x1, x2}, while those to reduce GCS score decline and extended hospital stay should be ≥ max{x3, x4}. When these ranges overlap, i.e., max{x3, x4} ≤ min{x1, x2}, this interval was the recommended optimal glucose reduction range. If there was no overlap between these ranges, i.e., max{x3, x4} > min{x1, x2}, the treatment strategy should be dynamically adjusted considering individual differences in the risk of various adverse outcomes. CONCLUSIONS: The machine learning models shows good performance in predicting adverse outcomes in patients with DKA, assisting in personalized blood glucose management and holding important clinical application prospects.

Etiology of the Neonatal Hypoglycemias.

To provide a more appropriate foundation for dealing with the problem of hypoglycemia in newborn infants, this article focuses on the mechanisms which underlie the various forms of neonatal hypoglycemia and discusses their implications for newborn care. Evidence indicates that all of the major forms of neonatal hypoglycemia are the result of hyperinsulinism due to dysregulation of pancreatic islet insulin secretion. Based on these observations, the authors propose that routine measurement of B-hydroxybutyrate should be considered an essential part of glucose monitoring in newborn infants.

Recurrent Hypoglycemia After Total Gastrectomy: A Case Report and Literature Analysis.

BACKGROUND Hypoglycemia is a common complication following total gastrectomy, primarily caused by dumping syndrome and severe malnutrition, with late dumping syndrome being particularly significant. However, for recurrent fasting hypoglycemia, the possibility of insulinoma should be considered. Hypoglycemia caused by insulinoma can lead to severe consequences, including seizures and even death. Thus, it is crucial to differentially diagnose hypoglycemia occurring after total gastrectomy. CASE REPORT In this report, we present the case of a 36-year-old Chinese woman who underwent total gastrectomy for gastric cancer and subsequently received chemotherapy. Four months after surgery, she began experiencing recurrent seizures, and multiple tests confirmed hypoglycemia. A series of laboratory and imaging examinations ultimately led to a diagnosis of insulinoma. After surgical resection of the tumor, the patient's hypoglycemic symptoms resolved, and pathology results confirmed an insulinoma. CONCLUSIONS This case report highlights the rapid weight loss and severe hypoglycemia observed in a patient only 4 months after total gastrectomy for gastric cancer. Although dumping syndrome was initially suspected based on the clinical course, the final diagnosis turned out to be insulinoma. The case underscores the importance of comprehensive evaluation and appropriate diagnostic investigations for patients experiencing hypoglycemia after total gastrectomy. Furthermore, the case suggests that the increased levels of enteroglucagon following changes in the gastrointestinal tract resulting from total gastrectomy may promote the development of insulinomas. This case report also contributes to the existing literature regarding atypical presentations of insulinomas and their association with gastric resection.

Risk of Neonatal Hypoglycemia in Infants of Mothers With Gestational Glucose Intolerance.

OBJECTIVE: To examine the relationship between gestational glucose intolerance (GGI) and neonatal hypoglycemia. RESEARCH DESIGN AND METHODS: This was a secondary analysis of 8,262 mother-infant dyads, with delivery at two hospitals between 2014 and 2023. We categorized maternal glycemic status as normal glucose tolerance (NGT), GGI, or gestational diabetes mellitus (GDM). We defined NGT according to a normal glucose load test result, GGI according to an abnormal glucose load test result with zero (GGI-0) or one (GGI-1) abnormal value on the 100-g oral glucose tolerance test, and GDM according to an abnormal glucose load test result with two or more abnormal values on the glucose tolerance test. Neonatal hypoglycemia was defined according to blood glucose <45 mg/dL or ICD-9 or ICD-10 diagnosis of neonatal hypoglycemia. We used logistic regression analysis to determine associations between maternal glucose tolerance category and neonatal hypoglycemia and conducted a sensitivity analysis using Δ-adjusted multiple imputation, assuming for unscreened infants a rate of neonatal hypoglycemia as high as 33%. RESULTS: Of infants, 12% had neonatal hypoglycemia. In adjusted models, infants born to mothers with GGI-0 had 1.28 (95% 1.12, 1.65), GGI-1 1.58 (95% CI 1.11, 2.25), and GDM 4.90 (95% CI 3.81, 6.29) times higher odds of neonatal hypoglycemia in comparison with infants born to mothers with NGT. Associations in sensitivity analyses were consistent with the primary analysis. CONCLUSIONS: GGI is associated with increased risk of neonatal hypoglycemia. Future research should include examination of these associations in a cohort with more complete neonatal blood glucose ascertainment and determination of the clinical significance of these findings on long-term child health.

Are the variations in ECG morphology associated to different blood glucose levels? implications for non-invasive glucose monitoring for T1D paediatric patients.

AIMS: Recent clinical trials and real-world studies highlighted those variations in ECG waveforms and HRV recurrently occurred during hypoglycemic and hyperglycemic events in patients with diabetes. However, while several studies have been carried out for adult age, there is lack of evidence for paediatric patients. The main aim of the study is to identify the correlations of variations in ECG Morphology waveforms with blood glucose levels in a paediatric population. METHODS: T1D paediatric patients who use CGM were enrolled. They wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glucose metrics, ECG parameters and HRV features were collected, and Wilcoxon rank-sum test and Spearman's correlation analysis were used to explore if different levels of blood glucose were associated to ECG morphological changes. RESULTS: Results showed that hypoglycaemic events in paediatric patients with T1D are strongly associated with variations in ECG morphology and HRV. CONCLUSIONS: Results showed the opportunity of using the ECG as a non-invasive adding instrument to monitor the hypoglycaemic events through the integration of the ECG continuous information with CGM data. This innovative approach represents a promising step forward in diabetes management, offering a more comprehensive and effective means of detecting and responding to critical changes in glucose levels.

Neonatal Hypoglycemia.

Neonatal hypoglycemia (NH) is broadly defined as a low plasma glucose concentration that elicits hypoglycemia-induced impaired brain function. To date, no universally accepted threshold (reference range) for plasma glucose levels in newborns has been published, as data consistently indicate that neurologic responses to hypoglycemia differ at various plasma glucose concentrations. Infants at risk for NH include infants of diabetic mothers, small or large for gestational age, and premature infants. Common manifestations include jitteriness, poor feeding, irritability, and encephalopathy. Neurodevelopmental morbidities associated with NH include cognitive and motor delays, cerebral palsy, vision and hearing impairment, and poor school performance. This article offers a timely discussion of the state of the science of NH and recommendations for neonatal providers focused on early identification and disease prevention.

Pyruvate dehydrogenase complex deficiency masked by septic shock-induced lactic acidosis: a case report.

Pyruvate dehydrogenase complex (PDHC) deficiency is a common genetic disorder leading to lactic acidosis, which can also result from several nongenetic conditions, such as septic shock. The present study reports a case of PDHC deficiency masked by septic shock-induced lactic acidosis. This case involved a 16-year-old adolescent with poor exercise tolerance compared with his peers, and no underlying diseases. The disease onset was characterized by cough, fever, and dyspnea, with hypotension and elevated lactate levels, which indicated septic shock. However, severe hypoglycemia and lactic acidosis persisted despite resolution of a pulmonary infection and correction of septic shock, requiring continuous intravenous infusion of 50% glucose. Although the patient did not experience acute kidney injury and had normal urine output, continuous renal replacement therapy was used to regulate the internal environment owing to the severity of the acidosis. The diagnosis of PDHC deficiency was considered on the basis of the persistent hypoglycemia and hyperlactatemia, before genetic mutation testing was completed. The clinical thinking process required a rich accumulation of pathophysiological knowledge. This article reports a case of PDHC deficiency masked by septic shock-induced lactic acidosis to raise awareness of the disease and avoid misdiagnosis and missed diagnosis.

The Maintain High Blood Glucose subscale of the child hypoglycemia fear survey: proposed preliminary cut points for screening youth with type 1 diabetes.

OBJECTIVE: To improve the clinical utility of the Maintain High Blood Glucose subscale of the Hypoglycemia Fear Surveys (HFS) by identifying clinically meaningful cut points associated with glycemic outcomes. METHODS: Youth (N = 994; 13.96 ± 2.3 years) with type 1 diabetes and their caregivers (N = 1,111; 72% female) completed the Child or Parent version of the HFS. Modal Score Distribution, Standard Deviation Criterion, and Elevated Item Criterion approaches were used to identify proposed preliminary cut points for the Maintain High Blood Glucose subscale. The association between proposed preliminary cut points was examined with youth glycemic outcomes. RESULTS: A cut point of ≥7 for the Maintain High Blood Glucose subscale on the Child HFS was associated with a greater percentage of blood glucose readings >180 mg/dl (p < .01), higher mean blood glucose (p < .001), and a higher hemoglobin A1c (p < .05). In subsequent multiple regression analyses, controlling for other factors associated with glycemia, the significant association between scores above ≥7 and higher mean blood glucose and higher hemoglobin A1c remained. A clinically useful cut point was not identified for caregivers. However, elevated youth scores on the Maintain High Blood Glucose subscale were positively associated with elevated caregiver scores (phi = .171, p < .001). CONCLUSIONS: The proposed preliminary cut point for the Maintain High Blood Glucose subscale will aid the type 1 diabetes care team in identifying youth whose behaviors may be contributing to their suboptimal glycemia.

How should we differentiate hypoglycaemia in non-diabetic patients?

Hypoglycaemic syndromes are rare in apparently healthy individuals and their diagnosis can be a difficult challenge for clinicians as there are no shared guidelines that suggest how to approach patients with a suspect hypoglycaemic disorder. Since hypoglycaemia symptoms are common and nonspecific, it's necessary to document the Whipple Triad (signs and/or symptoms compatible with hypoglycaemia; relief of symptoms following glucose administration; low plasma glucose levels) before starting any procedure. Once the triad is documented, a meticulous anamnesis and laboratory tests (blood glucose, insulin, proinsulin, C-peptide, ß-hydroxybutyrate and anti-insulin antibodies) should be performed. Results can guide the physician towards further specific tests, concerning the suspected disease. In this review, we consider all current causes of hypoglycaemia, including rare diseases such as nesidioblastosis and Hirata's syndrome, describe appropriate tests for diagnosis and suggest strategies to differentiate hypoglycaemia aetiology.

Effect of switch from flash glucose monitoring to flash glucose monitoring with real-time alarms on hypoglycaemia in people with type 1 diabetes mellitus.

We aimed to evaluate the utility of the FreeStyle Libre 2 device for reducing time below range level 1 and level 2 compared with the Freestyle Libre device (without alarms) in people with type 1 diabetes mellitus. We conducted longitudinal observational follow-up study of a cohort of 100 people with type 1 diabetes mellitus who had switched from FreeStyle Libre to FreeStyle Libre 2 as part of routine clinical practice. Three months after switching to FreeStyle Libre 2, compared with results with FreeStyle Libre, there were a significant improvements in time below range level 1 (p = 0.02) and level 2 (p <0.001), time in range (p <0.001), time above range level 1 (p = 0.002), glucose management indicator (p= 0.04) and mean glucose (p= 0.04) during follow-up. Furthermore there was a significant direct association between age and change in TIR with a coefficient of 0.23, and a significant inverse association between age and change in TAR-1 with a coefficient of 0.11. Switching to a flash glucose monitoring system with alarms improves time below range, time in range and coefficient of variation in people with type 1 diabetes mellitus.

Precision and accuracy of a point of care glucometer for detection of hypoglycaemia in horses.

Point-of-care (POC) glucometry is commonly used in horses; however, measurement error with this method when analysing hypoglycaemic samples (<4 mmol/L) is unknown. The objective of this study was to determine the precision and accuracy of glucometry in hypoglycaemic horses in comparison to a laboratory method of glucose measurement (LAB). Repeatability coefficients were 0.47 mmol/L for POC and 0.09 mmol/L for LAB, and coefficients of variation were 10 % and 2.11 %, for the POC and LAB methods, respectively. Systemic bias with the POC method was present, with a mean bias of -0.26 mmol/L (95 % limits of agreement: -0.88 - 0.37) in comparison to LAB, and <70% of measurements were within 20 % of paired LAB results. Prior to use of glucometers, assessment of the diagnostic performance of the equipment is necessary, including determination of acceptable criteria and reference ranges for hypoglycaemic samples.

Accuracy of continuous glucose monitoring during exercise-related hypoglycemia in individuals with type 1 diabetes.

Background: Hypoglycemia is common in individuals with type 1 diabetes, especially during exercise. We investigated the accuracy of two different continuous glucose monitoring systems during exercise-related hypoglycemia in an experimental setting. Materials and methods: Fifteen individuals with type 1 diabetes participated in two separate euglycemic-hypoglycemic clamp days (Clamp-exercise and Clamp-rest) including five phases: 1) baseline euglycemia, 2) plasma glucose (PG) decline ± exercise, 3) 15-minute hypoglycemia ± exercise, 4) 45-minute hypoglycemia, and 5) recovery euglycemia. Interstitial PG levels were measured every five minutes, using Dexcom G6 (DG6) and FreeStyle Libre 1 (FSL1). Yellow Springs Instruments 2900 was used as PG reference method, enabling mean absolute relative difference (MARD) assessment for each phase and Clarke error grid analysis for each day. Results: Exercise had a negative effect on FSL1 accuracy in phase 2 and 3 compared to rest (ΔMARD = +5.3 percentage points [(95% CI): 1.6, 9.1] and +13.5 percentage points [6.4, 20.5], respectively). In contrast, exercise had a positive effect on DG6 accuracy during phase 2 and 4 compared to rest (ΔMARD = -6.2 percentage points [-11.2, -1.2] and -8.4 percentage points [-12.4, -4.3], respectively). Clarke error grid analysis showed a decrease in clinically acceptable treatment decisions during Clamp-exercise for FSL1 while a contrary increase was observed for DG6. Conclusion: Physical exercise had clinically relevant impact on the accuracy of the investigated continuous glucose monitoring systems and their ability to accurately detect hypoglycemia.

A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study.

BACKGROUND: Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes. METHODS AND FINDINGS: We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation. CONCLUSIONS: Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.

Association of Fetal Catecholamines With Neonatal Hypoglycemia.

Importance: Perinatal stress and fetal growth restriction increase the risk of neonatal hypoglycemia. The underlying pathomechanism is poorly understood. In a sheep model, elevated catecholamine concentrations were found to suppress intrauterine insulin secretion, followed by hyperresponsive insulin secretion once the adrenergic stimulus subsided. Objective: To determine whether neonates with risk factors for hypoglycemia have higher catecholamine concentrations in umbilical cord blood (UCB) and/or amniotic fluid (AF) and whether catecholamines are correlated with postnatal glycemia. Design, Setting, and Participants: In a prospective cohort study of 328 neonates at a tertiary perinatal center from September 2020 through May 2022 in which AF and UCB were collected immediately during and after delivery, catecholamines and metanephrines were analyzed using liquid chromatography with tandem mass spectrometry. Participants received postnatal blood glucose (BG) screenings. Exposure: Risk factor for neonatal hypoglycemia. Main Outcomes and Measures: Comparison of catecholamine and metanephrine concentrations between at-risk neonates and control participants, and correlation of concentrations of catecholamines and metanephrines with the number and severity of postnatal hypoglycemic episodes. Results: In this study of 328 neonates (234 in the risk group: median [IQR] gestational age, 270 [261-277] days; and 94 in the control group: median [IQR] gestational age, 273 [270-278] days), growth-restricted neonates showed increased UCB median (IQR) concentrations of norepinephrine (21.10 [9.15-42.33] vs 10.88 [5.78-18.03] nmol/L; P < .001), metanephrine (0.37 [0.13-1.36] vs 0.12 [0.08-0.28] nmol/L; P < .001), and 3-methoxytyramine (0.149 [0.098-0.208] vs 0.091 [0.063-0.149] nmol/L; P = .001). Neonates with perinatal stress had increased UCB median (IQR) concentrations of norepinephrine (22.55 [8.99-131.66] vs 10.88 [5.78-18.03] nmol/L; P = .001), normetanephrine (1.75 [1.16-4.93] vs 1.25 [0.86-2.56] nmol/L; P = .004), and 3-methoxytyramine (0.120 [0.085-0.228] vs 0.091 [0.063-0.149] nmol/L; P = .008) (P < .0083 was considered statistically significant). Concentrations of UCB norepinephrine, metanephrine, and 3-methoxytyramine were negatively correlated with AF C-peptide concentration (rs = -0.212, P = .005; rs = -0.182, P = .016; and rs = -0.183, P = .016, respectively [P < .017 was considered statistically significant]). Concentrations of UCB norepinephrine, metanephrine, and 3-methoxytyramine were positively correlated with the number of hypoglycemic episodes (BG concentration of 30-45 mg/dL) (rs = 0.146, P = .01; rs = 0.151, P = .009; and rs = 0.180, P = .002, respectively). Concentrations of UCB metanephrine and 3-methoxytyramine were negatively correlated with the lowest measured BG concentration (rs = -0.149, P = .01; and rs = -0.153, P = .008, respectively). Conclusions and Relevance: Neonates at risk for hypoglycemia displayed increased catecholamine and metanephrine concentrations that were correlated with postnatal hypoglycemic episodes and lower BG levels; these results are consistent with findings in a sheep model that fetal catecholamines are associated with neonatal ß-cell physiology and that perinatal stress or growth restriction is associated with subsequent neonatal hyperinsulinemic hypoglycemia. Improving the pathomechanistic understanding of neonatal hypoglycemia may help to guide management of newborns at risk for hypoglycemia.