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Familial hyperinsulinaemic hypoglycaemia-1 arises from mutations within the genes of pancreatic beta cells, resulting in unregulated insulin secretion from pancreatic beta cells. A 4.06 kg female neonate, born to a second-degree consanguineously married couple, presented with repeated asymptomatic hypoglycaemia. There was a significant history of a previous sibling's death from nesidioblastosis. Despite treatment with intravenous glucose, diazoxide, hydrochlorothiazide and octreotide, she continued to experience hypoglycaemic episodes. Despite efforts to manage sepsis, including antibiotics, antifungals and intravenous immunoglobulin/granulocyte-macrophage colony-stimulated factor, her condition worsened. She succumbed on day 34. This case underscores the complexities of managing congenital hyperinsulinaemic hypoglycaemia, especially in the context of concurrent infections and the need for multidisciplinary care. Early genetic diagnosis proved invaluable in facilitating timely and effective treatment. Furthermore, the genetic results enabled us to counsel the parents regarding the recurrence risk in subsequent pregnancies and the necessity for antenatal diagnosis.

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INTRODUCTION: Despite the improvements in diabetes management by continuous glucose monitoring (CGM) it is difficult to capture the complexity of CGM data in one metric. We aimed to develop a clinically relevant multidimensional scoring model with the capacity to identify the most alarming CGM episodes and/or patients from a large cohort. RESEARCH DESIGN AND METHODS: Retrospective CGM data from 2017 to 2020 available in electronic medical records were collected from n=613 individuals with type 1 diabetes (total 82 114 days). A scoring model was developed based on three metrics; glycemic variability percentage, low blood glucose index and high blood glucose index. Values for each dimension were normalized to a numeric score between 0-100. To identify the most representative score for an extended time period, multiple ways to combine the mean score of each dimension were evaluated. Correlations of the scoring model with CGM metrics were computed. The scoring model was compared with interpretations of a clinical expert board (CEB). RESULTS: The dimension of hypoglycemia must be weighted to be representative, whereas the other two can be represented by their overall mean. The scoring model correlated well with established CGM metrics. Applying a score of ≥80 as the cut-off for identifying time periods with a 'true' target fulfillment (ie, reaching all targets for CGM metrics) resulted in an accuracy of 93.4% and a specificity of 97.1%. The accuracy of the scoring model when compared with the CEB was high for identifying the most alarming CGM curves within each dimension of glucose control (overall 86.5%). CONCLUSIONS: Our scoring model captures the complexity of CGM data and can identify both the most alarming dimension of glycemia and the individuals in most urgent need of assistance. This could become a valuable tool for population management at diabetes clinics to enable healthcare providers to stratify care to the patients in greatest need of clinical attention.

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Neonatal encephalopathy (NE) is a serious condition with various neurological dysfunctions in newborns. Disruptions in glucose metabolism, including both hypoglycemia and hyperglycemia, are common in NE and can significantly impact outcomes. Hypoglycemia, defined as blood glucose below 45 mg/dL, is associated with increased mortality, neurodevelopmental disabilities, and brain lesions on MRI. Conversely, hyperglycemia, above 120 to 150 mg/dL, has also been linked to heightened mortality, hearing impairment, and multiorgan dysfunction. Both aberrant glucose states appear to worsen prognosis compared to normoglycemic infants. Therapeutic hypothermia is the standard of care for NE that provides neuroprotection by reducing metabolic demands and inflammation. Adjunct therapies like glucagon and continuous glucose monitoring show promise in managing dysglycemia and improving outcomes. Glucagon can enhance cerebral blood flow and glucose supply, while continuous glucose monitoring enables real-time monitoring and personalized interventions. Maintaining balanced blood sugar levels is critical in managing NE. Early detection and intervention of dysglycemia are crucial to improve outcomes in neonates with encephalopathy. Further research is needed to optimize glycemic management strategies and explore the potential benefits of interventions like glucagon therapy.

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This article reviews the use of continuous glucose monitoring (CGM) devices in the management of type 2 diabetes (T2D). Study results show that continuous CGM use improves glycemic control, lowers glycated hemoglobin (HbA1c) levels, and reduces hypoglycemic episodes compared with traditional monitoring methods. -Observational studies also suggest a reduction in diabetes-related emergencies and hospitalizations. In addition, sporadic use of CGM appears to be beneficial for certain groups of people with T2D. However, more research is needed to fully understand the long-term effects and limitations of this technology. This article discusses -innovative perspectives on T2D management.

Cet article explore l'impact des dispositifs de mesure continue du glucose (MCG) dans la gestion du diabète de type 2 (DT2). Les ­résultats des études démontrent que l'utilisation régulière de la MCG améliore le contrôle de la glycémie, réduit les taux d'hémoglobine glyquée (HbA1c) et diminue les épisodes hypoglycémiques par rapport à la surveillance traditionnelle. Les données issues d'études observationnelles mettent également en évidence une réduction des événements aigus liée au diabète et des hospitalisations. De plus, l'emploi occasionnel de la MCG semble davantage bénéfique pour certains groupes de patients DT2. Toutefois, des recherches supplémentaires sont nécessaires pour clarifier les effets à long terme et les limites de cette technologie. Cet article discute les perspectives innovantes pour la gestion du DT2.

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BACKGROUND: Continuous glucose monitoring (CGM) can decrease hypoglycemic events and health care costs; however, barriers and facilitators that influence CGM use are unknown. PURPOSE: The purpose of this study was to evaluate hypoglycemic events and cost outcomes after CGM implementation and describe associated barriers and facilitators. METHODS: A mixed-methods study design was used to evaluate CGM implementation on 2 pulmonary units within an academic health center. Hypoglycemic events were evaluated before and after CGM implementation, and nurses were interviewed about facilitators and barriers that influence CGM use. RESULTS: Hypoglycemic events decreased from a rate of 0.0906 per 1000 patient days to 0.0503 postimplementation, P < .0001. A $105 766 cost avoidance was recognized. Barriers and facilitators to CGM use are described. CONCLUSIONS: Findings support CGM implementation, while uniquely contributing financial impact and device use barriers and facilitators. Hospitals may consider CGM use to improve timely identification and treatment of hypoglycemia.

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BACKGROUND: Extended glucose predictions are novel in diabetes management. Currently, there is no solution widely available. People with diabetes mellitus (DM) are offered features like trend arrows and limited predictions linked to predefined situations. Thus, the impact of extended glucose predictions on the burden of diabetes and person-reported outcomes (PROs) is unclear. METHODS: In this online survey, 206 people with type 1 and type 2 diabetes (T1D and T2D), 70.9% and 29.1%, respectively, who participated in the dia·link online panel and were current continuous glucose monitoring (CGM) users, were presented with different scenarios of hypothetical extended glucose predictions. They were asked to imagine how low glucose predictions of 30 minutes and overnight as well as glucose predictions up to 2 hours would influence their diabetes management. Subsequently, they completed the Hypoglycemia Fear Survey II (HFS-II) and the T1 Diabetes Distress Scale (T1-DDS) by rating each item on a 5-point scale (-2: strong deterioration to +2: strong improvement) according to the potential change due to using glucose predictions. RESULTS: For all glucose prediction periods, 30 minutes, up to 2 hours, and at nighttime, the surveyed participants expected moderate improvements in both fear of hypoglycemia (HFS-II: 0.57 ± 0.49) and overall diabetes distress (T1-DDS = 0.44 ± 0.49). The T1-DDS did not differ for type of therapy or diabetes. CONCLUSIONS: People with T1D and T2D would see glucose predictions as a potential improvement regarding reduced fear of hypoglycemia and diabetes distress. Therefore, glucose predictions represent a value for them in lowering the burden of diabetes and its management.

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Continuous glucose monitoring (CGM) has become an increasingly important tool for self-management in people with diabetes mellitus (DM). In this paper, we discuss recommendations on how to implement predictive features provided by the Accu-Chek SmartGuide Predict app in clinical practice. The Predict app's features are aimed at ultimately reducing diabetes stress and fear of hypoglycemia in people with DM. Furthermore, we explore the use cases and potential benefits of continuous glucose prediction, predictions of low glucose, and nocturnal hypoglycemia.

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Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.

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Insulin autoimmune syndrome (IAS) is a rare condition triggered by exposure to various drugs containing sulfhydryl compounds, proton pump inhibitors, supplements, plasma proteins, viral infections like measles, influenza, hepatitis C, and autoimmune conditions like Graves' disease and systemic lupus erythematosus.1,3 We report two patients with recurrent episodes of hypoglycemia who were then diagnosed with IAS due to the consumption of α-lipoic acid (ALA) present in a multivitamin supplement. Eating small, frequent meals containing complex carbohydrates and steroids helps normalize blood glucose levels and reduce the insulin autoantibodies (IAA) to undetectable levels.3.

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Almost 90% of patients with type 2 diabetes mellitus (DM2) are obese. Obesity increases the risk of developing DM2 several times. The calculation of anthropometric indices is used to diagnose the severity of obesity, as well as to assess the risk associated with obesity. The aim of the study is to study the relationship between Body Mass Index (BMI), waist circumference to hip circumference ratio (waist-to-hip ratio, WC/HR), Body Roundness Index (BRI) and Visceral Adiposity Index (VAI) with the risk of hypoglycemia in elderly and senile patients with DM2. The study included 122 elderly and senile patients (mean age 71±6,18 years) with DM2. The study participants were divided into 2 groups: patients with cases of hypoglycemia (n=65) and patients without a history of hypoglycemia (n=57). We have found that lower BMI, WC/HR, BRI, and VAI values are significantly associated with an increased risk of hypoglycemia in patients with DM2 of older age groups.

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OBJECTIVES: Detecting and treating severe hypoglycemia promptly after birth is crucial due to its association with adverse long-term neurodevelopmental outcomes. However, limited data are available on the optimal timing of glucose screening in asymptomatic high-risk neonates prone to hypoglycemia. Risk factors associated with asymptomatic high-risk neonates include late prematurity ≥35 and <37 weeks gestation (LPT), small-for-gestational-age (SGA), large-for-gestational-age (LGA), and infant-of-a-diabetic mother (IDM). This study aims to determine the incidence and the impact of individual risk factors on early hypoglycemia (defined as blood glucose ≤25 mg/dL in the initial hour after birth) in asymptomatic high-risk neonates. METHODS: All asymptomatic high-risk neonates ≥35 weeks gestation underwent early blood glucose screening within the first hour after birth (n=1,690). A 2-year retrospective analysis was conducted to assess the incidence of early neonatal hypoglycemia in this cohort and its association with hypoglycemia risk factors. RESULTS: Out of the 9,919 births, 1,690 neonates (17 %) had risk factors for neonatal hypoglycemia, prompting screening within the first hour after birth. Incidence rates for blood glucose ≤25 mg/dL and ≤15 mg/dL were 3.1 and 0.89 %, respectively. Of concern, approximately 0.5 % of all asymptomatic at-risk neonates had a blood glucose value of ≤10 mg/dL. LPT and LGA were the risk factors significantly associated with early neonatal hypoglycemia. CONCLUSIONS: Asymptomatic high-risk neonates, particularly LPT and LGA neonates, may develop early severe neonatal hypoglycemia identified by blood glucose screening in the first hour of life. Additional investigation is necessary to establish protocols for screening and managing asymptomatic high-risk neonates.

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Diabetes is a common chronic metabolic disease with a wide range of clinical symptoms and consequences and one of the main causes of death. For the management of diabetes, painless and continuous interstitial fluid (ISF) glucose monitoring is ideal. Here, we demonstrate continuous diabetes monitoring using an integrated microneedle (MN) biosensor with an emergency alert system. MNs are a novel technique in the field of biomedical engineering because of their ability to analyze bioinformation with minimal invasion. In this work we developed a poly(methyl methacrylate) (PMMA) based MN glucose sensor. The device was produced by the 3D printing technique, microfabrication, electrodeposition, and enzyme immobilization step. The in vitro test for the glucose MN sensor showed a linear range from 1.5 to 14 mM with a sensitivity of 1.51 µA mM-1, limit of detection (LOD) of 0.35 mM and good selectivity. Highly repeatable sensing is observed with good reproducibility. The interference-free detection of glucose in the presence of physiologically relevant concentrations of ascorbic acid, uric acid, and mannose is demonstrated, along with the operational stability of the array. After resolving the biofouling consequences linked to on-body sensing, this MN platform would be appealing for minimally invasive electrochemical glucose monitoring. An alert is sent to confidants via email or SMS when the values are abnormal. The application is also able to display the recorded values in the form of a graph to help determine the state of health of the user over a period of time. It can be concluded that continuous monitoring and an emergency alert system are important for keeping an eye on diabetic patients and can send alert in case of an abnormal situation of the patient.

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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.

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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.

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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.

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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.

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