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Background: A congenital portosystemic shunt (CPSS) is defined as abnormal vascular communications between the portal vein and the systemic vein. Encephalopathy, hepatopulmonary syndrome, and portopulmonary hypertension are manifestations in patients with CPSS. Hyperinsulinemic hypoglycemia is also one of the manifestations of CPSS. Hyperinsulinemic hypoglycemia secondary to CPSS is caused by a lack of hepatic first-pass elimination of insulin, which is secreted from pancreatic beta cells., Case Presentation: A 7-month-old boy had hypergalactosemia detected by newborn mass screening. Enhanced abdominal computed tomography showed the absence of the portal vein trunk and extrahepatic portosystemic communication between the superior mesenteric vein and the inferior vena cava. He had suffered from uncontrollable hyperinsulinemic hypoglycemia under protein and lactose restriction. We performed living donor liver transplantation (LDLT) using a left lateral segment graft from his father. The postoperative course was uneventful and the hypoglycemic attacks disappeared., Conclusion: We believe that uncontrolled hyperinsulinemic hypoglycemia secondary to CPSS is an indication of LDLT., (© 2024 Wiley Periodicals LLC.)

Diagnosing post-gastrectomy hypoglycemia is challenging, often relying on medical history with documented low plasma glucose levels. Here, we present three cases of patients who presented a high probability of post-gastrectomy hypoglycemia diagnosed and managed successfully using "theragnostic" continuous glucose monitoring and alpha-glucosidase inhibitors. In the first week, patients maintained their current lifestyle without medical intervention; in the second week, voglibose 0.2 mg before meals was prescribed. Continuous glucose monitoring data from the first week confirmed the diagnosis with multiple hypoglycemic events after postprandial peaks, whereas data from the second week showed reduced hypoglycemic events and lower glycemic variability, demonstrating voglibose's therapeutic effect. This report highlights the effective management of post-gastrectomy hypoglycemia using voglibose and theragnostic continuous glucose monitoring, showing its potential benefits and safety for similar cases., (© 2024 The Author(s). Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.)

A woman in her 60s presented with a small pancreatic head tumor. Imaging studies revealed a 13-mm well-defined pancreatic head tumor. A neuroendocrine neoplasm was suspected, and the patient opted for observation at that time. After 8 months, the patient began experiencing sweating while fasting, and blood tests during regular follow-up visits showed hypoglycemia. Hypoglycemia was induced during fasting test. The tumor exhibited clear features of an insulinoma during follow-up observation. While small neuroendocrine neoplasms are often managed through observation, caution should be exercised regarding their transformation into functional neuroendocrine tumors.

Aims: The aim was to investigate rebound hypoglycemic and hyperglycemic events, and describe their relation to other glycemic metrics., Methods: Data from intermittently scanned continuous glucose monitoring were downloaded for 90 days for 159 persons with type 1 diabetes. A hypoglycemic event was defined as glucose <3.9 mmol/l for at least two 15-minute periods. Rebound hypoglycemia (Rhypo) was a hypoglycemic event preceded by glucose >10.0 mmol/l within 120 minutes and rebound hyperglycemia (Rhyper) was hypoglycemia followed by glucose >10.0 mmol/l within 120 minutes., Results: A total of 10 977 hypoglycemic events were identified of which 3232 (29%) were Rhypo and 3653 (33%) were Rhyper, corresponding to a median frequency of 10.1, 2.5, and 3.0 events per person/14 days. For 1267 (12%) of the cases, Rhypo and Rhyper coexisted. The mean peak glucose was 13.0 ± 1.6 mmol/l before Rhypo; 12.8 ± 1.1 mmol/l in Rhyper. The frequency of Rhyper was significantly ( P < .001) correlated with Rhypo (Spearman's rho 0.84), glucose coefficient of variation (0.78), and time below range (0.69) but not with time above range (0.12, P = .13)., Conclusions: The strong correlation between Rhyper and Rhypo suggests an individual behavioral characteristic toward intensive correction of glucose excursions., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: KWH has received honorarium as an advisory board member for Abbott Laboratories A/S, Denmark. BMB has no conflicts of interest.

Objective: To evaluate the association between continuous glucose monitoring in pregnant people with type 2 diabetes and perinatal outcomes., Methods: This was a retrospective cohort study of pregnant people with type 2 diabetes who received prenatal care and delivered singleton, nonanomalous neonates at a single academic tertiary care center from November 1, 2019, to February 28, 2023. The primary outcome was a composite of neonatal morbidity, including hypoglycemia, hyperbilirubinemia, shoulder dystocia, large for gestational age at birth, preterm birth, neonatal intensive care unit (NICU) admission, or perinatal death. Demographics and outcomes were compared by type of monitoring (continuous glucose monitoring vs intermittent self-monitoring of blood glucose), and multivariable logistic regression estimated the association between continuous glucose monitoring use and perinatal outcomes., Results: Of 360 pregnant people who met the inclusion criteria, 82 (22.7%) used continuous glucose monitoring. The mean gestational age at continuous glucose monitoring initiation was 21.3±6.4 weeks. The use of continuous glucose monitoring was associated with lower odds of the primary composite neonatal morbidity (65.9% continuous glucose monitoring vs 77.0% self-monitoring of blood glucose, adjusted odds ratio [aOR] 0.48, 95% CI, 0.24-0.94). Continuous glucose monitoring use was also associated with lower odds of preterm birth (13.4% vs 25.2%, aOR 0.48, 95% CI, 0.25-0.93) and NICU admission (33.8% vs 47.6%, aOR 0.36, 95% CI, 0.16-0.81)., Conclusion: In pregnant people with type 2 diabetes, continuous glucose monitoring use was associated with less neonatal morbidity, fewer preterm births, and fewer NICU admissions., Competing Interests: Financial Disclosure: The authors did not report any potential conflicts of interest., (Copyright © 2024 by the American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved.)

Background: Scuba diving was previously excluded because of hypoglycemic risks for patients with type 1 diabetes mellitus(T1DM). Specific eligibility criteria and a safety protocol have been defined, whereas continuous glucose monitoring (CGM) systems have enhanced diabetes management. This study aims to assess the feasibility and accuracy of CGM Dexcom G7 and Free Style Libre 3 in a setting of repetitive scuba diving in T1DM, exploring the possibility of nonadjunctive use. Material and Methods: The study was conducted during an event of Diabete Sommerso ® association in 2023. Participants followed a safety protocol, with capillary glucose as reference standard (Beurer GL50Evo). Sensors' accuracy was evaluated through median and mean absolute relative difference (MeARD, MARD) and surveillance error grid (SEG). Data distribution and correlation were estimated by Spearman test and Bland-Altman plots. The ability of sensors to identify hypoglycemia was assessed by contingency tables. Results: Data from 202 dives of 13 patients were collected. The overall MARD was 31% (Dexcom G7) and 14.2% (Free Style Libre 3) and MeARD was 19.7% and 11.6%, respectively. Free Style Libre 3 exhibited better accuracy in normoglycemic and hyperglycemic ranges. SEG analysis showed 82.1% (Dexcom G7) and 97.4% (Free Style Libre 3) data on no-risk zone. Free Style Libre 3 better performed on hypoglycemia identification (diagnostic odds ratio of 254.10 vs. 58.95). Neither of the sensors reached the MARD for nonadjunctive use. Conclusions: The study reveals Free Style Libre 3 superior accuracy compared with Dexcom G7 in a setting of repetitive scuba diving in T1DM, except for hypoglycemic range. Both sensors fail to achieve accuracy for nonadjunctive use. Capillary tests remain crucial for safe dive planning, and sensor data should be interpreted cautiously. We suggest exploring additional factors potentially influencing sensor performance.

Objective: The Glucose in Well Babies (GLOW) Study showed that there are two phases of low glucose concentrations in healthy newborn infants: an initial phase in which plasma concentrations of ketones are low; and a second phase in which low glucose concentrations are accompanied by elevated concentrations of ketones. The implications of these two phases for the brain differ depending on whether ketones are available as alternative substrate for brain metabolism. The purpose of this study was to estimate the duration of these two phases of neonatal low glucose concentrations in 66 healthy breastfed newborns from the GLOW Study during the first 5 days of life., Methods: The sum of glucose and beta-hydroxybutyrate (BOHB) was used as a proxy for the total concentrations of insulin-dependent fuels for the brain; a threshold value below 4 mmol/L was taken to indicate the presence of relative hyperinsulinism and a BOHB concentration above 0.5 mmol/L to indicate ketonaemia., Results: The first phase of low glucose concentrations lasted a median of 40 hours and in 15% of infants, this persisted beyond 60 hours. Fifty (76%) of the 66 infants subsequently had ketonaemia, which resolved at a median age of 76 hours (range 41->120 hours)., Conclusions: These data suggest that monitoring BOHB concentrations may be useful for interpreting glucose concentrations in newborns and screening for persistent hyperinsulinism., Competing Interests: Competing interests: DDDL has received consulting fees from Zealand Pharma, Crinetics Pharmaceuticals, Hanmi Pharmaceuticals, Eiger Pharma, Twist Biosciences and Rhythm Pharmaceuticals; has received research funding from Zealand Pharma, Rezolute, Crinetics Pharmaceuticals, Hanmi Pharmaceuticals, Twist Biosciences, Eiger Pharma and Ultragenyx for studies not discussed in this manuscript. CAS serves on the Data and Safety Monitoring Board for Zealand Pharma clinical trials in hyperinsulinism. DDDL and CAS serve on the Scientific Advisory Board for Congenital Hyperinsulinism International., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: There has been no unified definition for neonatal hypoglycemia. Generally, neonatal hypoglycemia is diagnosed when the whole blood glucose concentration is below 2.2 mmol/L. The reasons are numerous. This case report presents an instance of a newborn with extremely low blood glucose levels immediately after birth from a diabetic mother, referred to as the "Infants of Diabetic Mothers", providing a practical case reference for early monitoring and timely intervention for Infants of Diabetic Mothers (IDMs)., Methods: The glucose concentrations in whole blood and serum were measured using a fingerstick glucometer and a fully automatic biochemical analyzer, respectively. The levels of insulin and C-peptide were detected using a fully automatic chemiluminescence method. The glycated hemoglobin was measured using a glycated hemoglobin analyzer., Results: The mother's blood glucose value before delivery was 8.28 mmol/L. After delivery, the fingerstick blood glucose and serum glucose concentrations of the newborn were 2.6 mmol/L and 0.11 mmol/L, respectively. The levels of insulin and C-peptide were 805.55 pmol/L and 2,312.64 pmol/L, respectively. The value of glycated hemoglobin was 7.5%. Intravenous nutrition with a 100 mL of 10% glucose at an infusion rate of 12.7 mL/d was maintained. Then, 10% glucose was given to the neonate at an infusion rate of 7.7 mL/minute for 30 minutes. Eventually, the blood glucose levels were raised to 5.2 mmol/L., Conclusions: Diabetes has a profound impact on both the mother and the newborn. Close monitoring and timely intervention are necessary to reduce neonatal complications and promote longterm healthy growth and development.

Aim: To develop and test different machine learning algorithms for predicting nocturnal hypoglycaemia in patients with type 2 diabetes mellitus., Design: A retrospective study., Methods: We collected data from dynamic blood glucose monitoring of patients with T2DM admitted to the Department of Endocrinology and Metabolism at a hospital in Shanghai, China, from November 2020 to January 2022. Patients undergone the continuous glucose monitoring (CGM) for ≥ 24 h were included in this study. Logistic regression, random forest and light gradient boosting machine algorithms were employed, and the models were validated and compared using AUC, accuracy, specificity, recall rate, precision, F1 score and the Kolmogorov-Smirnov test., Results: A total of 4015 continuous glucose-monitoring data points from 440 patients were included, and 28 variables were selected to build the risk prediction model. The 440 patients had an average age of 62.7 years. Approximately 48.2% of the patients were female and 51.8% were male. Nocturnal hypoglycaemia appeared in 573 (14.30%) of 4015 continuous glucose monitoring data. The light gradient boosting machine model demonstrated the highest predictive performances: AUC (0.869), specificity (0.802), accuracy (0.801), precision (0.409), recall rate (0.797), F1 score (0.255) and Kolmogorov (0.603). The selected predictive factors included time below the target glucose range, duration of diabetes, insulin use before bed and dynamic blood glucose monitoring parameters from the previous day., Patient or Public Contribution: No Patient or Public Contribution., (© 2024 The Author(s). Nursing Open published by John Wiley & Sons Ltd.)

Objective: Use of continuous glucose monitoring (CGM) has led to greater detection of hypoglycemia; the clinical significance of this is not fully understood. The Hypoglycaemia-Measurement, Thresholds and Impacts (Hypo-METRICS) study was designed to investigate the rates and duration of sensor-detected hypoglycemia (SDH) and their relationship with person-reported hypoglycemia (PRH) in people living with type 1 diabetes (T1D) and insulin-treated type 2 diabetes (T2D) with prior experience of hypoglycemia., Research Design and Methods: We recruited 276 participants with T1D and 321 with T2D who wore a blinded CGM and recorded PRH in the Hypo-METRICS app over 10 weeks. Rates of SDH <70 mg/dL, SDH <54 mg/dL, and PRH were expressed as median episodes per week. Episodes of SDH were matched to episodes of PRH that occurred within 1 h., Results: Median [interquartile range] rates of hypoglycemia were significantly higher in T1D versus T2D; for SDH <70 mg/dL (6.5 [3.8-10.4] vs. 2.1 [0.8-4.0]), SDH <54 mg/dL (1.2 [0.4-2.5] vs. 0.2 [0.0-0.5]), and PRH (3.9 [2.4-5.9] vs. 1.1 [0.5-2.0]). Overall, 65% of SDH <70 mg/dL was not associated with PRH, and 43% of PRH had no associated SDH. The median proportion of SDH associated with PRH in T1D was higher for SDH <70 mg/dL (40% vs. 22%) and SDH <54 mg/dL (47% vs. 25%) than in T2D., Conclusions: The novel findings are that at least half of CGM hypoglycemia is asymptomatic, even below 54 mg/dL, and many reported symptomatic hypoglycemia episodes happen above 70 mg/dL. In the clinical and research setting, these episodes cannot be used interchangeably, and both need to be recorded and addressed., (© 2024 by the American Diabetes Association.)

Aims: To conduct a systematic review and meta-analysis, within the Coordinating Research and Evidence for Medical Devices (CORE-MD) project, evaluating CE-marked high-risk devices for glucose management., Materials and Methods: We identified interventional and observational studies evaluating the efficacy and safety of eight automated insulin delivery (AID) systems, two implantable insulin pumps, and three implantable continuous glucose monitoring (CGM) devices. We meta-analysed randomized controlled trials (RCTs) comparing AID systems with other treatments., Results: A total of 182 studies published between 2009 and 2024 were included, comprising 166 studies on AID systems, six on insulin pumps, and 10 on CGM devices; 26% reported industry funding; 18% were pre-market; 37% had a comparator group. Of the studies identified, 29% were RCTs, 24% were non-randomized trials, and 47% were observational studies. The median (interquartile range) sample size was 48 (28-102), age 34.8 (14-44.2) years, and study duration 17.5 (12-26) weeks. AID systems lowered glycated haemoglobin by 0.5 percentage points (absolute mean difference [MD] = -0.5; 21 RCTs; I 2  = 86%) and increased time in target range for sensor glucose level by 13.4 percentage points (MD = 13.4; 14 RCTs; I 2  = 90%). At least one safety outcome was assessed in 71% of studies., Conclusions: High-risk devices for glucose monitoring or insulin dosing, in particular AID systems, improve glucose control safely, but evidence on diabetes-related end-organ damage is lacking due to short study durations. Methodological heterogeneity highlights the need for developing standards for future pre- and post-market investigations of diabetes-specific high-risk medical devices., (© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Objective:  Hypoglycemia, the most common metabolic derangement in the newborn period remains a contentious issue, not only due to various numerical definitions, but also due to limited therapeutical options which either lack evidence to support their efficacy or are increasingly recognized to lead to adverse reactions in this population. This study aimed to investigate neonatologists' current attitudes in diagnosing and managing transient and persistent hypoglycemia in newborns admitted to the Neonatal Intensive Care Unit (NICU)., Methods:  A web-based electronic survey which included 34 questions and a clinical vignette was sent to U.S. neonatologists., Results:  There were 246 survey responses with most respondents using local protocols to manage this condition. The median glucose value used as the numerical definition of hypoglycemia in first 48 hours of life (HOL) for symptomatic and asymptomatic term infants and preterm infants was 45 mg/dL (2.5 mmol/L; 25-60 mg/dL; 1.4-3.3 mmol/L), while after 48 HOL the median value was 50 mg/dL (2.8 mmol/L; 30-70 mg/dL; 1.7-3.9 mmol/L). There were various approaches used to manage transient and persistent hypoglycemia that included dextrose gel, increasing caloric content of the feeds using milk fortifiers, using continuous feedings, formula or complex carbohydrates, and use of various medications such as diazoxide, glucocorticoids, and glucagon., Conclusion:  There is still large variability in current practices related to hypoglycemia. Further research is needed not only to provide evidence to support the values used as a numerical definition for hypoglycemia, but also on the efficacy of current strategies used to manage this condition., Key Points: · Numerical definition of glucose remains variable.. · Strategies managing transient and persistent hypoglycemia are diverse.. · There is a need for further research to investigate efficacy of various treatment options.., Competing Interests: None declared., (Thieme. All rights reserved.)

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., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

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., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

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., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

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., Competing Interests: Competing interests: LC, P-OC and DE have co-founded the company OneTwo Analytics AB of which JD is an employee. OneTwo Analytics AB is developing a commercial product (PRIO Scoring) based on the results presented here., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Aromatic L-amino acid decarboxylase (AADC) deficiency is a disease in which neurological findings are dominant due to deficiencies in neurotransmitter synthesis. Hypoglycemia caused by autonomic dysfunction is one of the symptoms that may be encountered. Here we report a case of mild AADC deficiency presenting with hypoglycemia without any neurological signs. A 4-year-old girl presented with recurrent hypoglycemia. Her growth and development were normal. Plasma insulin and cortisol values were normal in the sample at the time of hypoglycemia. C8:1-Carnitine elevation was detected in the acylcarnitine profile. A clinical exome panel was performed with the suggestion of a fatty acid oxidation defect. However, a homozygous variant in the DDC gene was detected. Furthermore, cerebrospinal fluid neurotransmitter analysis revealed low 5-hydroxyindolacetic acid and homovanillic acid and high 3-O-methyl-dopa and methyltetrahydrofolate (5 MTHF) consistent with AADC deficiency. Plasma AADC enzyme activity was low. The episodes of hypoglycemia were treated with uncooked cornstarch. This case suggests that AADC deficiency should be considered in some patients with hypoglycemia., Competing Interests: Conflict of interest: None declared., (©Copyright 2024 by Turkish Society for Pediatric Endocrinology and Diabetes / The Journal of Clinical Research in Pediatric Endocrinology published by Galenos Publishing House.)

Background: The current study sets out to develop and validate a robust machine-learning model utilizing electronic health records (EHR) to forecast the risk of hypoglycemia among ICU patients in Jordan., Research Design and Methods: The present study drew upon a substantial cohort of 13,567 patients admitted 26,248 times to the intensive care unit (ICU) over 10 years from July 2012 to July 2022. The primary outcome of interest was the occurrence of any hypoglycemic episode during the patient's ICU stay. Developing and testing predictor models was conducted using Python machine-learning libraries., Results: A total of 1,896 were eligible to participate in the study, 206 experienced at least one hypoglycemic episode. Eight machine-learning models were trained to predict hypoglycemia. All models showed predicting power with a range of 74.53-99.69 for AUROC. Except for Naive Bayes, the six remaining models performed distinctly better than the basic logistic regression usually used for prediction in epidemiological studies. CatBoost model was consistently the best performer with the highest AUROC (0.99), accuracy and precision, sensitivity and specificity, and recall., Conclusions: We used machine learning to anticipate the likelihood of hypoglycemia, which can significantly decrease hypoglycemia incidents and enhance patient outcomes.

Background: Cohort data on continuous glucose monitoring (CGM) metrics are scarce for liver glycogen storage diseases (GSDs) and idiopathic ketotic hypoglycemia (IKH). The aim of this study was to retrospectively describe CGM metrics for people with liver GSDs and IKH., Patients and Methods: CGM metrics (descriptive, glycemic variation and glycemic control parameters) were calculated for 47 liver GSD and 14 IKH patients, categorized in cohorts by disease subtype, age and treatment status, and compared to published age-matched CGM metrics from healthy individuals. Glycemic control was assessed as time-in-range (TIR; ≥3.9 - ≤7.8 and ≥3.9 - ≤10.0 mmol/L), time-below-range (TBR; <3.0 mmol/L and ≥3.0 - ≤3.9 mmol/L), and time-above-range (TAR; >7.8 and >10.0 mmol/L)., Results: Despite all patients receiving dietary treatment, GSD cohorts displayed significantly different CGM metrics compared to healthy individuals. Decreased TIR together with increased TAR were noted in GSD I, GSD III, and GSD XI (Fanconi-Bickel syndrome) cohorts (all p < 0.05). In addition, all GSD I cohorts showed increased TBR (all p < 0.05). In GSD IV an increased TBR (p < 0.05) and decreased TAR were noted (p < 0.05). In GSD IX only increased TAR was observed (p < 0.05). IKH patient cohorts, both with and without treatment, presented CGM metrics similar to healthy individuals., Conclusion: Despite dietary treatment, most liver GSD cohorts do not achieve CGM metrics comparable to healthy individuals. International recommendations on the use of CGM and clinical targets for CGM metrics in liver GSD patients are warranted, both for patient care and clinical trials., Competing Interests: Declaration of competing interest All authors declare that they have no conflicts of interest related to the content of this manuscript. R.J.O.: There are confidentiality agreements with third parties. There is a consultation agreement (with Ultragenyx Pharmaceutical Inc). For all private-public relationships, all contracts are via UMCG Contract Research Desk and all payments are to UMCG. A.V., C.M.A.L.: No conflicts of interest. M.J.F.W., F.D.B., A.B.S.: There are confidentiality agreements with third parties. For all private-public relationships, all contracts are via UMCG Contract Research Desk and all payments are to UMCG. A.R.: There are confidentiality agreements with third parties. In the past 36 months there have been consultation agreements or honoraria for lectures or presentations with Nestlé, Danone and Ultragenyx Pharmaceutical Inc. T.G.J.D.: There are confidentiality agreements with third parties. In the past 36 months, there have been consultation agreements (with Danone, Ultragenyx Pharmaceutical Inc, ModernaTX Inc., and Beam Therapeutics), contracts for financial research support for investigator-initiated research (NCT04311307) and sponsor-initiated research (NCT03517085, NCT03970278, NCT05139316, and NCT05196165), honoraria for lectures or presentations (by MEDTalks, Prelum, and Danone), and participations in a Data Safety Monitoring Board (NCT05095727) and Advisory Boards (Ultragenyx Pharmaceutical Inc, ModernaTX Inc., and Beam Therapeutics). For all private-public relationships, all contracts are via UMCG Contract Research Desk and all payments are to UMCG., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Context: Patients discharged as "healthy" with the symptoms of spontaneous hypoglycemia, commonly known as Whipple's triad, need more attention., Objective: Characterization and long-term follow-up of symptom development in patients with spontaneous hypoglycemia discharged as "healthy". The objective was to ascertain whether any conditions related to the symptoms were diagnosed during the follow-up period., Methods: Retrospective analysis of patient data and evaluation of a specific questionnaire on the development of symptoms of spontaneous hypoglycemia. In addition, patient questionnaires were evaluated and primary care physicians were asked about possible diseases not recorded at baseline that occurred during the follow-up period., Setting: Center for Endocrinology, Diabetology, and Osteology at the University Hospital Marburg, Inpatient Department, Germany., Patients: All patients who presented to our center for the 72-hour fast between 2005 and 2018 and were discharged without an internal medicine diagnosis were included., Interventions: Survey by questionnaire, via telephone interview., Main Outcome Measures: Patient-reported information on current symptoms compared to original symptoms, diagnosis of insulinoma or diabetes mellitus during follow-up, matched with primary care physician data, and metabolic and biometric data such as body mass index (BMI), homeostasis model assessment for insulin resistance (HOMA IR), insulin sensitivity Matsuda Index (ISI-M), and area under the curve., Results: A total of 41 datasets were evaluated at baseline and 38 patients were followed for an average of approximately 10 years. In total, 61% of respondents still reported the same symptoms as at baseline. No insulinoma was missed in these patients. Only two of the 38 patients developed diabetes mellitus., Conclusion: The high percentage of patients who are discharged as "healthy" and still have symptoms after many years is disturbing. It is possible that the symptoms are not due to low blood glucose. We urge caution with use of the term "healthy". We advocate a multidisciplinary therapeutic approach after an organic cause of hypoglycemia has been ruled out. Psychosomatic treatment seems to be useful. In addition, more research should be conducted on this topic., (© 2024. The Author(s).)

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: GB, TG, MH, MS, and NW are employees and stockholders of Roche Diabetes Care GmbH. BG received lecture fees and/or consulting of Roche, Dexcom, Abbott, Vitalaire, Diabeloop, Ascensia, and Insulet. DE received lecture fees from Roche Diabetes Care, Sanofi-Aventis, Dexcom; Advisory Board member of Roche Diabetes Care, and Dexcom. MTPM received lecture fees and/or consulting fees from Abbott, Air Liquide, Sanofi, Novo Nordisk, Ascensia, Roche, Lilly, Medtronic, and Liesno Buno. JO received lecture fees from Abbott.

Objective: Since the introduction of continuous glucose monitoring (CGM) technology, developers have rigorously researched the feasibility of creating a noninvasive glucose monitoring device. In a recent pilot study, investigators reported a strong correlation between glucose values obtained from novel noninvasive monitoring device (GWave) values to venous and capillary glucose measurements. Research Design and Methods: We investigated whether the level of accuracy observed in the pilot study could be reproduced in a larger cohort, using a smaller third-generation manufacturable device (Gen III GWave) containing a standardized sensor chip that can be mass produced for commercial use. The evaluation assessed concordance with capillary blood glucose, reproducibility between two Gen III devices, and accuracy during insulin-induced hypoglycemia. Results: Assessment of samples from 75 subjects (type 2 diabetes, n  = 6; type 1 diabetes, n  = 28; nondiabetic pregnant subjects, n  = 10; and nondiabetic, n  = 31) showed that 97% of values were in Zone A with 3% in Zone B of the Clarke Error Grid, with a mean absolute relative difference of 6.7% from reference blood glucose. Comparison between two independent Gen III GWave devices demonstrated reproducibility between the sensors ( R 2  = 0.95), with 100% of values within Zone A. In the hypoglycemia assessment, measurements from the Gen III sensor tightly followed the capillary glucose measurements down to 42 mg/dL (2.3 mmol/L), whereas the CGM measurements from two different CGM only converged with the GWave and capillary glucose readings after 90 min of decreasing glucose levels. Conclusion: Our results show promise as potentially the first noninvasive technology. Future studies will focus on larger number of people in all glucose ranges. Real-time noninvasive blood glucose monitoring is possible using GWave technology.

Background: The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant., Method: The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits., Results: The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use., Conclusions: The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RSW participated in multicenter clinical trials through her institution, sponsored by Insulet, Medtronic, Eli Lilly, Novo Nordisk, and Boehringer Ingelheim, and has used donated DexCom CGMs in projects sponsored by NIH and the Leona M. and Harry B. Helmsley Charitable Trust. KMM has used donated DexCom CGMs in projects sponsored by NIH and the Leona M. and Harry B. Helmsley Charitable Trust. REP reports consulting fees from Bayer AG, Corcept Therapeutics Incorporated, DexCom, Hanmi Pharmaceutical Co., Merck, Novo Nordisk, Pfizer, Sanofi, Scohia Pharma Inc., and Sun Pharmaceutical Industries, and grants/research support from Hanmi Pharmaceutical Co., Janssen, Metavention, Novo Nordisk, Poxel SA, and Sanofi. All funds are paid directly to Dr Pratley’s employer, AdventHealth, a nonprofit organization that supports education and research.

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: D.E. reports lecture fees from Roche Diabetes Care, Sanofi-Aventis, and Dexcom Germany and is an Advisory Board member of Roche Diabetes Care and Dexcom Germany. L.L. reports lecture fees from Abbott, AstraZeneca, Boehringer-Ingelheim, Eli Lilly, Merck Sharp & Dohme, Medtronic, Menarini, MOVI SpA, Mundipharma, Novo Nordisk, Roche Diabetes Care, Sanofi-Aventis, and Terumo. He is an Advisory Board member of Abbott, AstraZeneca, Boehringer-Ingelheim, Eli Lilly Italia, Medtronic, MOVI SpA, Novo Nordisk, Roche Diabetes Care, Sanofi-Aventis, and Terumo. L-S.P. reports lecture fees from Ypsomed. N.H. reports Advisory Board member fees from Abbott Diabetes Care and Insulet as well as honoraria for lectures from Berlin Chemie AG, Becton Dickenson, Sanofi Germany, Roche Diabetes Care, and Dexcom Germany. N.B. and T.G. are employees and stockholders of Roche Diabetes Care GmbH.

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: BK is head of the research institute of the diabetes academy Bad Mergentheim (FIDAM). BK has received speakers’ honoraria or consulting fees form Abbott, Bayer, Berlin Chemie, Dexcom, Embecta, Emperra, Lilly, Novo Nordisk, Roche, Sanofi, and Ypsomed. GF is general manager and medical director of the Institute for Diabetes Technology (Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany IfDT), I, which carries out clinical studies, eg, with medical devices for diabetes therapy on its own initiative and on behalf of various companies. GF/IfDT have received research support, speakers’ honoraria, or consulting fees in the last three years from Abbott, Ascensia, Berlin Chemie, Boydsense, Dexcom, Lilly Deutschland, Novo Nordisk, Perfood, PharmaSens, Roche, Sinocare, Terumo, and Ypsomed. RZ has received speakers’ honoraria or consulting fees in the last three years from Abbott, Dexcom, Lilly, Medtronic, mySugr, Novo Nordisk, Roche Diabetes Care, Sanofi, Vertex, VitalAire/Tandem, and Ypsomed. LH is a shareholder of the Profil Institut für Stoffwechselforschung GmbH, Neuss, Germany and a consultant for several companies that are developing novel diagnostic and therapeutic options for diabetes treatment. OS is general manager and founder of Sciarc GmbH. He is consultant to several companies that are developing products for metabolic, cardiovascular, and kidney diseases.

Introduction: Gestational diabetes (GD) is a risk factor for neonatal hypoglycaemia (NH), but other factors can increase this risk., Objectives: To create a score to predict NH in women with GD., Methods: Retrospective study of women with GD with a live singleton birth between 2012 and 2017 from the Portuguese GD registry. Pregnancies with and without NH were compared. A logistic regression was used to study NH predictors. Variables independently associated with NH were used to score derivation. The model's internal validation was performed by a bootstrapping. The association between the score and NH was assessed by logistic regression., Results: We studied 10216 pregnancies, 410 (4.0%) with NH. The model's AUC was 0.628 (95%CI: 0.599-0.657). Optimism-corrected c-index: 0.626. Points were assigned to variables associated with NH in proportion to the model's lowest regression coefficient: insulin-treatment 1, preeclampsia 3, preterm delivery 2, male sex 1, and small-for-gestational-age 2, or large-for-gestational-age 3. NH prevalence by score category 0-1, 2, 3, 4, and ≥5 was 2.3%, 3.0%, 4.5%, 6.0%, 7.4%, and 11.5%, respectively. Per point, the OR for NH was 1.35 (95% CI: 1.27-1.42). A score of 2, 3, 4, 5 or ≥6 (versus ≤1) had a OR for NH of 1.67 (1.29-2.15), 2.24 (1.65-3.04), 2.83 (2.02-3.98), 3.08 (1.83-5.16), and 6.84 (4.34-10.77), respectively., Conclusion: Per each score point, women with GD had 35% higher risk of NH. Those with ≥6 points had 6.8-fold higher risk of NH compared to a score ≤1. Our score may be useful for identifying women at a higher risk of NH., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Glycemic control poses a challenge in intensive care unit (ICU) patients and dysglycemia is associated with poor outcomes. Continuous glucose monitoring (CGM) has been successfully implemented in the type 1 diabetes out-patient setting and renewed interest has been directed into the transition of CGM into the ICU. This scoping review aimed to provide an overview of CGM accuracy in ICU patients to inform future research and CGM implementation., Methods: We systematically searched PubMed and EMBASE between 5th of December 2023 and 21st of May 2024 and reported findings in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline for scoping reviews (PRISMA-ScR). We assessed studies reporting the accuracy of CGM in the ICU and report study characteristics and accuracy outcomes., Results: We identified 2133 studies, of which 96 were included. Most studies were observational (91.7%), conducted in adult patients (74%), in mixed ICUs (47.9%), from 2014 and onward, and assessed subcutaneous CGM systems (80%) using arterial blood samples as reference test (40.6%). Half of the studies (56.3%) mention the use of a prespecified reference test protocol. The mean absolute relative difference (MARD) ranged from 6.6 to 30.5% for all subcutaneous CGM studies. For newer factory calibrated CGM, MARD ranged from 9.7 to 20.6%. MARD for intravenous CGM was 5-14.2% and 6.4-13% for intraarterial CGM., Conclusions: In this scoping review of CGM accuracy in the ICU, we found great diversity in accuracy reporting. Accuracy varied depending on CGM and comparator, and may be better for intravascular CGM and potentially lower during hypoglycemia., Competing Interests: Declarations. Conflicts of interest: UPB has served on advisory boards for Novo Nordisk, Sanofi-Aventis, and Vertex, and has received lecture fees from Novo Nordisk and Sanofi-Aventis. KN holds shares in Novo Nordisk; has been a paid consultant for Novo Nordisk and Medtronic; has received speaker honorarium and honorarium for Advisory Board to her institution from Medtronic, Novo Nordisk, Convatec, and her institution has received research funding from Zealand Pharma, Novo Nordisk, Medtronic, and Dexcom. JM has received research support from DexCom, Inc. outside the submitted work. MGD, AP, and MHM are affiliated with Dept. of Intensive Care, Rigshospitalet, which receives funds for research from the Novo Nordisk Foundation, Sygeforsikringen ‘danmark’ and the Independent Research Fund Denmark., (© 2024. Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: Use of continuous glucose monitoring-CGM in patients on kidney replacement therapy including kidney transplant recipients, may improve glycemic control and detection of hypoglycemia. However, studies in this population in particular in kidney transplant recipients are very limited., Methods: The study aimed to evaluate glycemic profiles using the Dexcom G6 CGM system for 30 d a personal smartphone in people with diabetes after kidney transplantation and to assess the impact of monthly use of the CGM system on glycemic control and quality of life in this group., Results: Over 8 months, 9 people after kidney transplantation were included in the study (7 women, median age 57 years), 8 people with new-onset diabetes after transplantation (NODAT), and 1 person with diabetes mellitus type 1. The time since kidney transplantation in each of the study participants was less than 2 years. In 7 people with NODAT, the time in range was above 70%. In all study participants, hyperglycemia was observed mainly in the afternoon (2-6 pm). In some patients, pressure-induced sensor attenuations (PISAs) were noted. There was no effect of the use of Dexcom G6 on the HbA1c value. There was no impact of the use of the Dexcom G6 system on the perception of overall quality of life, but monthly use had a positive impact on the perception of the quality of health., Conclusions: CGM systems seem to be a promising method for assessing glycemic control in people with diabetes after kidney transplantation. More extensive research is needed to assess safety and usefulness in everyday practice.

Aim: To evaluate the relationship between breath volatile organic compounds (VOCs) and glycaemic states in individuals with type 1 diabetes (T1D), focusing on identifying specific VOCs as biomarkers for hypoglycaemia to offer a non-invasive diabetes-monitoring method., Materials and Methods: Ten individuals with T1D underwent induced hypoglycaemia in a clinical setting. Breath samples, collected every 10-15 minutes, were analysed using gas chromatography-ion mobility spectrometry (GC-IMS). Correlation analysis and machine learning models, including Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine classifiers, were used to classify glycaemic states based on VOC profiles., Results: Statistical analysis revealed moderate correlations between specific VOCs (e.g. isoprene, acetone) and venous blood glucose levels. Machine learning models showed high accuracy in classifying glycaemic states, with the best performance achieved by a two-class PLS-DA model showing an accuracy of 93%, sensitivity of 92% and specificity of 94%. Key biomarkers identified included isoprene, acetone, 2-butanone, methanol, ethanol, 2-propanol and 2-pentanone., Conclusions: This study shows the potential of breath VOCs to accurately classify glycaemic states in individuals with T1D. While key biomarkers such as isoprene, acetone and 2-butanone were identified, the analysis emphasizes the importance of using overall VOC patterns rather than individual compounds, which can be markers for multiple conditions. Machine learning models leveraging these patterns achieved high accuracy, sensitivity and specificity. These findings suggest that breath analysis using GC-IMS could be a viable non-invasive method for monitoring glycaemic states and managing diabetes., (© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

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., Competing Interests: Le Dr P. Oriot n’a déclaré aucun conflit d’intérêts en relation avec cet article. Le Dr L. Derdelinckx a participé à différentes réunions scientifiques rémunérées par la société Abbott. Le Pr J.-C. Philips a participé à différentes réunions scientifiques rémunérées par la société Abbott. Le Pr R. P. Radermecker a participé et présidé différentes réunions scientifiques rémunérées par la société Abbott.

After birth, healthy neonates undergo a period of altered glucose metabolism, known as "transitional hypoglycemia." During the first 0 to 4 hours of life, the mean plasma glucose concentration decreases to 57 mg/dL, then by 72 to 96 hours of life increases to 82 mg/dL, well within the normal adult range. Recent data suggest that transitional hypoglycemia is due to persistence of the fetal beta cell's lower threshold for insulin release, resulting in a transient hyperinsulinemic state. While hypoglycemia is an expected part of the transition to postnatal life, it makes the identification of infants with persistent hypoglycemia disorders challenging. Given the risk of neurologic injury from hypoglycemia, identifying these infants is critical. Hyperinsulinism is the most common cause of persistent hypoglycemia in neonates and infants and carries a high risk of neurocognitive dysfunction given the severity of the hypoglycemia and the inability to generate ketones, a critical alternative cerebral fuel. Screening neonates at risk for persistent hypoglycemia disorders and completing evaluations prior to hospital discharge is essential to prevent delayed diagnoses and neurologic damage., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

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.

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., Competing Interests: Disclosure C.A. Stanley serves on the Data Safety Monitoring Board for Zealand Pharma clinical trials in hyperinsulinism. D.D. De Leon has received consulting fees from Zealand Pharma A/S, Crinetics Pharmaceuticals, Hanmi Pharmaceuticals, Eiger Pharma, Twist Biosciences, and Rhythm Pharmaceuticals. Diva D. De Leon has received research funding from Zealand Pharma A/S, Rezolute, Crinetics Pharmaceuticals, Hanmi Pharmaceuticals, Twist Biosciences, Eiger Pharma, and Ultragenyx, United States for studies not discussed in this article. D.D. De Leon and C.A. Stanley serve on the Scientific Advisory Board for Congenital Hyperinsulinism International., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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 ., (© Journal of the Association of Physicians of India 2024.)

Aims: To identify risk factors for nocturnal/morning hypo- and hyperglycaemia in type 1 diabetes., Methods: Data on self-management practices were obtained from 3-day records. We studied the associations between self-management practices on the first recording day and the self-reported blood glucose (BG) concentrations on the subsequent night/morning., Results: Of the 1025 participants (39 % men, median age 45 years), 4.4 % reported nocturnal hypoglycaemia (<3.9 mmol/l), 9.8 % morning hypoglycaemia, 51.5 % morning euglycaemia, and 34.3 % morning hyperglycaemia (≥8.9 mmol/l). Within hypoglycaemic range, insulin pump use was associated with higher nocturnal BG concentration (B=0.486 [95 % Confidence Interval=0.121-0.852], p=0.009). HbA 1c was positively (0.046 [0.028-0.065], p<0.001), while antecedent fibre intake (-0.327 [-0.543 - -0.111], p=0.003) and physical activity (PA) (-0.042 [-0.075 - -0.010], p=0.010) were inversely associated with morning BG concentration. The odds of morning hypoglycaemia were increased by previous day hypoglycaemia (OR=2.058, p=0.002) and alcohol intake (1.031, p=0.001). Previous day PA (0.977, p=0.031) and fibre intake (0.848, p=0.017) were inversely, while HbA 1c (1.027, p<0.001) was positively associated with the risk of morning hyperglycaemia., Conclusions: Alcohol avoidance may prevent nocturnal hypoglycaemia, while PA and fibre intake may reduce hyperglycaemia risk. Avoidance of daytime hypoglycaemia and keeping HbA 1c in control may help maintain normoglycaemia also at night-time., Competing Interests: Declaration of Competing Interest Professor Per-Henrik Groop has received investigator-initiated grants from Eli Lilly and Roche, is an advisory board member for AbbVie, Astellas, Astra Zeneca, Bayer, Boehringer-Ingelheim, Eli Lilly, Janssen, Medscape, MSD, Mundipharma, Nestlé, Novartis, Novo Nordisk, and Sanofi. He has received lecture honoraria from Astellas, Astra Zeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Elo Water, Genzyme, Medscape, MSD, Mundipharma, Novartis, Novo Nordisk, PeerVoice, Sanofi and Sciarc. Erika Parente has received lecture honoraria from Eli Lilly, Abbott, Astra Zeneca, Sanofi, and Boehringer Ingelheim. The other authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (© 2024. The Author(s).)

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., (© 2024 the author(s), published by De Gruyter, Berlin/Boston.)

Noonan syndrome (NS) is a dominantly inherited genetic disorder with mutations in genes encoding components or regulators of the Rat sarcoma virus/mitogen-activated protein kinase pathway. Its diagnosis is based on characteristic features, including typical facial features, a short stature, congenital heart disease, mild developmental delay, and cryptorchidism. Patients with NS sometimes develop autoimmune diseases, such as Hashimoto's thyroiditis and, rarely, systemic lupus erythematosus (SLE). We herein present a 29-year-old Japanese female with NS complicated by SLE and repeated severe hypoglycaemia. The patient was diagnosed with SLE based on thrombocytopenia, nephritis, a positive antinuclear antibody titre (1:640), and a positive anti-dsDNA antibody. The patient was treated with a glucocorticoid, mycophenolate mofetil, and tacrolimus, which attenuated both SLE and hypoglycaemia. Since insulin receptor antibody levels were higher to the upper normal range and decreased after treatment, hypoglycaemia probably appeared to be attributed to type B insulin resistance syndrome. We herein present the first case of SLE in NS complicated by type B insulin resistance syndrome. Although NS is a rare disease, we need to consider the complication of autoimmune diseases, including SLE., (© Japan College of Rheumatology 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Many continuous glucose monitoring (CGM) systems provide functionality which alerts users of potentially unwanted glycemic conditions. These alerts can include glucose threshold alerts to call the user's attention to hypoglycemia or hyperglycemia, predictive alerts warning about impeding hypoglycemia or hyperglycemia, and rate-of-change alerts. A recent review identified 129 articles about CGM performance studies, of which approximately 25% contained alert evaluations. In some studies, real alerts were assessed; however, most of these studies retrospectively determined the timing of CGM alerts because not all CGM systems record alerts which necessitates manual documentation. In contrast to assessment of real alerts, retrospective determination allows assessment of a variety of alert settings for all three types of glycemic condition alerts. Based on the literature and the Clinical and Laboratory Standards Institute's POCT05 guideline, two common approaches to threshold alert evaluation were identified, one value-based and one episode-based approach. In this review, a critical discussion of the two approaches, including a post hoc analysis of clinical study data, indicates that the episode-based approach should be preferred over the value-based approach. For predictive alerts, fewer results were found in the literature, and retrospective determination of CGM alert timing is complicated by the prediction algorithms being proprietary information. Rate-of-change alert evaluations were not reported in the identified literature, and POCT05 does not contain recommendations for assessment. A possible approach is discussed including post hoc analysis of clinical study data. To conclude, CGM systems should record alerts, and the episode-based approach to alert evaluation should be preferred., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: G.F. is a general manager and medical director of the Institute for Diabetes Technology (Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany), which carries out clinical studies, for example, with medical devices for diabetes therapy on its own initiative and on behalf of various companies. G.F./IfDT has received research support, speakers’ honoraria, or consulting fees in the last 3 years from Abbott, Ascensia, Berlin Chemie, Boydsense, Dexcom, Lilly, Metronom, Medtronic, Menarini, MySugr, Novo Nordisk, PharmaSens, Roche, Sanofi, and Terumo. S.P., M.E., and D.W. are employees of IfDT.

Continuous glucose monitoring (CGM) has become the standard of care in diabetes management with the recent advances in technology and accessibility in the last decade. An International Consensus was established to define CGM metrics and its goals in diabetes care. The 2019 International Consensus suggested 14 days of CGM sampling for the assessment of CGM metrics stating the limitations that may occur for hypoglycemia and glycemic variability metrics. Since then, several studies assessed the correlation between CGM metrics and duration of the sampling period. This review summarized the studies that investigated the relationship between 14-day CGM sampling to 90-day CGM data in >70% CGM users for all CGM metrics and highlighted possible solutions for more accurate CGM sampling durations in type 1 diabetes (T1D). Accumulating evidence showed that 14-day CGM sampling correlates well with 90-day CGM data for mean glucose, time in 70-180 mg/dL, and hyperglycemia metrics; however, it correlates weakly for hypoglycemia and glycemic variability metrics. In the studies included in this review, in adults with T1D, minimum sampling duration was 14 days for mean glucose, time in 70-180 mg/dL, and time in hyperglycemia (>180 and >250 mg/dL); however, minimum sampling duration varied between 21 to 30 days for time <70 mg/dL, 30 to 35 days for time <54 mg/dL, and 28 to 35 days for coefficient of variation. Longer than 14 days of CGM, sampling was required to properly assess hypoglycemia and glycemic variability in T1D.

Differences in the effectiveness of real-time continuous glucose monitoring (rtCGM) and intermittently scanned continuous glucose monitoring (isCGM) in type 1 diabetes (T1D) are reported. The impact on percent time in range of switching from an isCGM with glucose threshold-based optional alerts only (FreeStyle Libre 2 [FSL2]) to an rtCGM (Dexcom G7) with an urgent low soon predictive alert was assessed, alongside other secondary outcomes including hemoglobin A1c (HbA1c) and other continuous glucose monitoring metrics. Adults with T1D using FSL2 were switched to Dexcom G7 for 12 weeks. HbA1c and continuous glucose data during FSL2 and Dexcom G7 use were compared. Data from 29 participants (aged 44.8 ± 16.5 years, 12 male and 17 female) were analyzed. After switching to rtCGM, participants spent less time in hypoglycemia below 3.9 mmol/L (70 mg/dL) (3.0% [1.0%, 5.0%] vs. 2.0% [1.0%, 3.0%], P  = 0.006) and had higher percentage achievement of time below 3.9 mmol/L (70 mg/dL) of <4% (55.2% vs. 82.8%, P  = 0.005). Coefficient of variation was lower (39.3 ± 6.6% vs. 37.2 ± 5.6%, P  = 0.008). In conclusion, adults with T1D who switched from isCGM to rtCGM may benefit from reduced exposure to hypoglycemia and glycemic variability.

Aim: The aim of this study was to develop and validate a prediction model based on continuous glucose monitoring (CGM) data to identify a week-to-week risk profile of excessive hypoglycemia. Methods: We analyzed, trained, and internally tested two prediction models using CGM data from 205 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach (XGBoost) combined with feature engineering deployed on the CGM signals was utilized to predict excessive hypoglycemia risk defined by two targets (time below range [TBR] >4% and the upper TBR 90th percentile limit) of TBR the following week. The models were validated in two independent cohorts with a total of 253 additional patients. Results: A total of 61,470 weeks of CGM data were included in the analysis. The XGBoost models had an area under the receiver operating characteristic curve (ROC-AUC) of 0.83-0.87 (95% confidence interval; 0.83-0.88) in the test dataset. The external validation showed ROC-AUCs of 0.81-0.90. The most discriminative features included the low blood glucose index, the glycemic risk assessment diabetes equation (GRADE), hypoglycemia, the TBR, waveform length, the coefficient of variation and mean glucose during the previous week. This highlights that the pattern of hypoglycemia combined with glucose variability during the past week contains information on the risk of future hypoglycemia. Conclusion: Prediction models based on real-world CGM data can be used to predict the risk of hypoglycemia in the forthcoming week. The models showed good performance in both the internal and external validation cohorts.

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., (© 2024 by the American Diabetes Association.)