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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.

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.)

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: Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes., Methods: In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine learning approaches to predict glycemic excursions: a classification model and a regression model., Results: The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone., Conclusions: Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction., 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.J.D. serves as an independent consultant for Dexcom separate from the present work. The remaining authors have no potential conflict of interests relevant to this article.

The use of continuous glucose monitors (CGMs) in individuals living without diabetes is increasing. The purpose of this study was to profile various CGM metrics around nutritional intake, sleep and exercise in a large cohort of physically active men and women living without any known metabolic disease diagnosis to better understand the normative glycemic response to these common stimuli. A total of 12,504 physically active adults (age 40 ± 11 years, BMI 23.8 ± 3.6 kg/m 2 ; 23% self-identified as women) wore a real-time CGM (Abbott Libre Sense Sport Glucose Biosensor, Abbott, USA) and used a smartphone application (Supersapiens Inc., Atlanta, GA, USA) to log meals, sleep and exercise activities. A total of >1 M exercise events and 274,344 meal events were analyzed. A majority of participants (85%) presented an overall (24 h) average glucose profile between 90 and 110 mg/dL, with the highest glucose levels associated with meals and exercise and the lowest glucose levels associated with sleep. Men had higher mean 24 h glucose levels than women (24 h-men: 100 ± 11 mg/dL, women: 96 ± 10 mg/dL). During exercise, the % time above >140 mg/dL was 10.3 ± 16.7%, while the % time <70 mg/dL was 11.9 ± 11.6%, with the remaining % within the so-called glycemic tight target range (70-140 mg/dL). Average glycemia was also lower for females during exercise and sleep events ( p < 0.001). Overall, we see small differences in glucose trends during activity and sleep in females as compared to males and higher levels of both TAR and TBR when these active individuals are undertaking or competing in endurance exercise training and/or competitive events.

Background: The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual's longer term control., Methods: We introduce explainable machine learning to make predictions of hypoglycemia (<70 mg/dL) and hyperglycemia (>270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user., Results: Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model., Conclusions: Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user's glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: New methods of continuous glucose monitoring (CGM) provide real-time alerts for hypoglycemia, hyperglycemia, and rapid fluctuations of glucose levels, thereby improving glycemic control, which is especially crucial during meals and physical activity. However, complex CGM systems pose challenges for individuals with diabetes and healthcare professionals, particularly when interpreting rapid glucose level changes, dealing with sensor delays (approximately a 10 min difference between interstitial and plasma glucose readings), and addressing potential malfunctions. The development of advanced predictive glucose level classification models becomes imperative for optimizing insulin dosing and managing daily activities., Methods: The aim of this study was to investigate the efficacy of three different predictive models for the glucose level classification: (1) an autoregressive integrated moving average model (ARIMA), (2) logistic regression, and (3) long short-term memory networks (LSTM). The performance of these models was evaluated in predicting hypoglycemia (<70 mg/dL), euglycemia (70-180 mg/dL), and hyperglycemia (>180 mg/dL) classes 15 min and 1 h ahead. More specifically, the confusion matrices were obtained and metrics such as precision, recall, and accuracy were computed for each model at each predictive horizon., Results: As expected, ARIMA underperformed the other models in predicting hyper- and hypoglycemia classes for both the 15 min and 1 h horizons. For the 15 min forecast horizon, the performance of logistic regression was the highest of all the models for all glycemia classes, with recall rates of 96% for hyper, 91% for norm, and 98% for hypoglycemia. For the 1 h forecast horizon, the LSTM model turned out to be the best for hyper- and hypoglycemia classes, achieving recall values of 85% and 87% respectively., Conclusions: Our findings suggest that different models may have varying strengths and weaknesses in predicting glucose level classes, and the choice of model should be carefully considered based on the specific requirements and context of the clinical application. The logistic regression model proved to be more accurate for the next 15 min, particularly in predicting hypoglycemia. However, the LSTM model outperformed logistic regression in predicting glucose level class for the next hour. Future research could explore hybrid models or ensemble approaches that combine the strengths of multiple models to further enhance the accuracy and reliability of glucose predictions.

Aims: Impaired awareness of hypoglycaemia (IAH) has been associated with increased diabetes distress and use of sensor technology can reduce diabetes distress. The aim of this study was to examine diabetes-specific distress (emotions, cognitions, behaviours) in relation to IAH status and use of glucose sensors in people with type 1 diabetes., Methods: Individuals with type 1 diabetes from an academic diabetes outpatient clinic completed the Clarke questionnaire (to assess hypoglycaemic awareness), Problem Areas in Diabetes (PAID-5), Hypoglycaemia Fear Survey-II (HFS-II), Attitudes to Awareness of Hypoglycaemia Survey (A2A), Nijmegen Clinical Screening Instrument Survey (NCSI) and Hyperglycaemia Avoidance Scale (HAS)., Results: Of the 422 participants (51.9% male, diabetes duration 30 [16-40] years, HbA 1c 60 ± 11 mmol/mol [7.6 ± 1.0%], 351 [88.2%] used a glucose sensor; 82 [19.4%]) had IAH. Compared to individuals with normal awareness, those with IAH more often had PAID-5 scores ≥8 (35.4% vs. 21.5%, p = 0.008) and higher scores on all HFS-II subscores (total [40.2 ± 21.5 vs. 27.9 ± 17.2, p < 0.001]), HFS-II behaviour (18.5 ± 10.0 vs. 15.1 ± 8.0, p = 0.005), HFS-II worry (21.8 ± 13.5 vs. 12.7 ± 10.9, p < 0.001), HAS worries (17.5 ± 7.3 vs. 14.3 ± 7.0, p < 0.001) and NCSI hypoglycaemia items. HAS behaviour, A2A and NCSI hyperglycaemia scores did not differ between individuals with or without IAH. Restricting the analyses to individuals using a glucose sensor did not materially change the results., Conclusions: Diabetes-specific distress remains a major problem among individuals with type 1 diabetes, particularly those with IAH, despite the widespread use of (intermittently scanned) sensor technology. Further studies are needed to examine strategies to lower diabetes-specific distress in individuals with IAH., (© 2023 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data., Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation., Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals., Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Background: Perioperative hyperglycemia is associated with adverse outcomes in surgical patients, and major societies recommend intraoperative monitoring and treatment targeting glucose <180-200 mg/dL. However, compliance with these recommendations is poor, in part due to fear of unrecognized hypoglycemia. Continuous Glucose Monitors (CGMs) measure interstitial glucose with a subcutaneous electrode and can display the results on a receiver or smartphone. Historically CGMs have not been utilized for surgical patients. We investigated the use of CGM in the perioperative setting compared to current standard practices., Method: This study evaluated the use of Abbott Freestyle Libre 2.0 and/or Dexcom G6 CGMs in a prospective cohort of 94 participants with diabetes mellitus undergoing surgery of ≥3 h duration. CGMs were placed preoperatively and compared to point of care (POC) BG checks obtained by capillary samples analyzed with a NOVA glucometer. Frequency of intraoperative blood glucose measurement was at the discretion of the anesthesia care team, with a recommendation of once per hour targeting BG of 140-180 mg/dL. Of those consented, 18 were excluded due to lost sensor data, surgery cancellation, or rescheduling to a satellite campus resulting in 76 enrolled subjects. There were zero occurrences of failure with sensor application. Paired POC BG and contemporaneous CGM readings were compared with Pearson product-moment correlation coefficients, and Bland-Altman plots., Results: Data for use of CGM in perioperative period was analyzed for 50 participants with Freestyle Libre 2.0, 20 participants with Dexcom G6, and 6 participants with both devices worn simultaneously. Lost sensor data occurred in 3 participants (15%) wearing Dexcom G6, 10 participants wearing Freestyle Libre 2.0 (20%) and 2 of the participants wearing both devices simultaneously. The overall agreement of the two CGM's utilized had a Pearson correlation coefficient of 0.731 in combined groups with 0.573 in Dexcom arm evaluating 84 matched pairs and 0.771 in Libre arm with 239 matched pairs. Modified Bland-Altman plot of the difference of CGM and POC BG indicated for the overall dataset a bias of -18.27 (SD 32.10)., Conclusions: Both Dexcom G6 and Freestyle Libre 2.0 CGMs were able to be utilized and functioned well if no sensor error occurred at time of initial warmup. CGM provided more glycemic data and further characterized glycemic trends more than individual BG readings. Required time of CGM warm up was a barrier for intraoperative use as well as unexplained sensor failure. CGMs had a fixed warm of time, 1 h for Libre 2.0 and 2 h for Dexcom G6 CGM, before glycemic data obtainable. Sensor application issues did not occur. It is anticipated that this technology could be used to improve glycemic control in the perioperative setting. Additional studies are needed to evaluate use intraoperatively and assess further if any interference from electrocautery or grounding devices may contribute to initial sensor failure. It may be beneficial in future studies to place CGM during preoperative clinic evaluation the week prior to surgery. Use of CGMs in these settings is feasible and warrants further evaluation of this technology on perioperative glycemic management., Competing Interests: Declaration of Competing Interest J. Aloi discloses that he receives research support from Abbott Diabetes., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The development of continuous glucose monitoring (CGM) systems has enabled people with type 1 diabetes mellitus (T1DM) to track their glucose trajectory in real-time and inspired research in personalised glucose prediction. In this paper, our aim is to predict postprandial abnormal-glycemia events. Different from prior research which focuses on hypoglycemia only, we make the first attempt to establish our problem as the joint prediction of hyperglycemia and hypoglycemia. On this basis, we propose a machine learning model that learns from the pattern of 1 hour past glucose and makes predictions for the two tasks simultaneously using a unified backbone. Key benefits of our methodology include 1) requiring only the CGM sequence as the input, thus making it more widely applicable than other counterparts using extra inputs such as the nutrition details, and 2) minimising the computational cost as the two tasks are unified into a single model. Our experiments on the openly available OhioT1DM dataset achieve state-of-the-art performance (Matthew's correlation coefficient of 0.61 for hyperglycemia and 0.48 for hypoglycemia). To encourage further study, we release our codes at https://github.com/r-cui/PostprandialHyperHypoPrediction under the MIT license.

Importance: A diagnosis of diabetes is considered when a patient has hyperglycemia with a random plasma glucose ≥200 mg/dL. However, in the inpatient setting, hyperglycemia is frequently non-specific, especially among patients who are acutely unwell. As a result, patients with transient hyperglycemia may be incorrectly labeled as having diabetes, leading to unnecessary treatment, and potential harm., Design, Setting, and Participants: We conducted a multicenter cohort study of patients hospitalized at six hospitals in Ontario, Canada, and identified those with a glucose value ≥200 mg/dL (including standing measurements and randomly drawn). We validated a definition for diabetes using manual chart review that included physician notes, pharmacy notes, home medications, and hemoglobin A1C. Among patients with a glucose value ≥200 mg/dL (11.1 mmol/L), we identified patients without diabetes who received a diabetes medication, and the number who experienced hypoglycemia during the same admission., Main Outcomes and Measures: To determine the diagnostic value of using random blood glucose to diagnose diabetes in the inpatient setting, and its impact on patient outcomes., Results: We identified 328,786 hospitalizations from hospital between 2010 and 2020. A blood glucose value of ≥200 mg/dL (11.1 mmol/L) had a positive predictive value of 68% and a negative predictive value of 90% for a diagnosis of diabetes. Of the 76,967 patients with an elevated glucose value reported, 16,787 (21.8%) did not have diabetes, and of these, 5375 (32%) received a diabetes medication. Hypoglycemia was frequently reported among the 5375 patients that received a diabetes medication, with 1406 (26.2%) experiencing hypoglycemia and 405 (7.5%) experiencing severe hypoglycemia., Conclusions and Relevance: Hyperglycemia in hospital is common but does not necessarily indicate a patient has diabetes. Furthermore, it can lead to treatment with diabetes medications with potential harm. Our findings highlight that clinicians should be cautious when responding to elevated random plasma glucose tests in the inpatient setting., (© 2023. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Aims: To examine real-world capillary blood glucose (CBG) data according to HbA 1c to define proportions of CBG readings at different HbA 1c levels, and evaluate patterns in CBG measurements to suggest areas to focus on with regard to self-management., Methods: A retrospective analysis stratified 682 adults with type 1 diabetes split into quartiles based on their HbA 1c . The proportions of results in different CBG ranges and associations with HbA 1c were evaluated. Patterns in readings following episodes of hyperglycaemia and hypoglycaemia were examined, using glucose to next glucose reading table (G2G)., Results: CBG readings in the target range (3.9-10 mmol/L) increase by ~10% across each CBG quartile (31% in the highest versus 63% in the lowest quartile, p < 0.05). The novel G2G table helps the treatment-based interpretation of data. Hypoglycaemia is often preceded by hyperglycaemia, and vice-versa, and is twice as likely in the highest HbA 1c quartile. Re-testing within 30 min of hypoglycaemia is associated with less hypoglycaemia, 1.6% versus 7.2%, p < 0.001, and also reduces subsequent hyperglycaemia and further hypoglycaemia in the proceeding 24 h. The coefficient of variation, but not standard deviation, is highly associated with hypoglycaemia, r = 0.71, and a CV ≤ 36% equates to 3.3% of CBG readings in the hypoglycaemic range., Conclusions: HbA 1c <58 mmol/mol (7.5%) is achievable even when only ~60% of CBG readings are between 3.9-10 mmol/L. Examining readings subsequent to out-of-range readings suggests useful behaviours which people with type 1 diabetes could be supported to adhere to, both in a clinic and structured education programmes, thereby decreasing the risk of hypoglycaemia whilst also reducing hyperglycaemia and improving HbA 1c ., (© 2022 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

Introduction: Diabetes mellitus, commonly known as just diabetes, is a group of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. In order to maintain this blood glucose value in normal parameters, a careful monitoring of it and insulin administration are necessary., Areas Covered: Thus, to facilitate this procedure, new blood glucose monitoring systems have been studied. The smart lens, the nano tattoo, noninvasive sensors based on reverse ionthophoresis and glucose oxidase - based continuous blood glucose monitoring systems, are the methods described in this study. Of course, not only is blood glucose monitoring important, but also the lifestyle of a drug or the way a drug is administered, especially in the case of insulin. How insulin is administered is also a topic that we address in this article. In an attempt to promote compliance with the administration, we have discussed about new forms of administering insulin such as: oral, intranasal, administration on the oral mucosa and last but not least, transdermal administration., Expert Opinion: Further, the attention of specialists should be directed to devices based on sensors, with a role in the interruption of insulin administration, in case of detection of hypoglycemia or the additional dose of insulin, if hyperglycemia is detected.

Background: Late preterm steroid administration can induce transient maternal and thus fetal hyperglycemia, which can increase production of fetal insulin and C-peptide. Infants delivered in this setting are subsequently at increased risk for hypoglycemia. Although maternal glycemic control before delivery is a key component of care for parturients with diabetes, this intervention has not been studied in the setting of late preterm steroid administration., Objective: This study aimed to determine the effect of maternal screening for and treatment of hyperglycemia after late preterm steroid administration on fetal C-peptide levels and other metabolic markers., Study Design: This was a multicenter, randomized trial (NCT03076775) of nondiabetic parturients with a singleton gestation receiving betamethasone at 34 0/7 weeks to 36 5/7 weeks for anticipated preterm birth. Participants randomized to maternal glycemic control received fasting and 1-hour postprandial or serial intrapartum capillary blood glucose screening with insulin treatment as indicated. Those randomized to expectant management did not receive any glucose screening or treatment. The primary outcome was fetal C-peptide level measured from umbilical cord blood at delivery. Secondary outcomes included other fetal metabolic markers and neonatal hypoglycemia (glucose level <40 mg/dL). Baseline characteristics and outcomes were compared between the groups. We estimated that we would need a sample size of 144 to provide >90% power to show a 1 ng/mL decrease in C-peptide concentration (±1.5 ng/mL) at ⍺=0.05 using a 2-sample t test and 1 interim analysis. After the interim analysis, the trial was stopped for futility., Results: Of 491 screened parturients, 163 (33%) were deemed eligible and 86 (53%) were randomized to 1 of the treatment groups (June 2017 to February 2021). One person was lost to follow-up because of delivery at another hospital. Baseline characteristics were similar between groups. The median interval from betamethasone administration to delivery was 24 hours (interquartile range, 13-96 hours) and did not differ between groups (P=.82). Most (82%) randomized to maternal glycemic control had hyperglycemia: 80% had at least 1 fasting glucose level >95 mg/dL, 75% had at least one 1-hour postprandial glucose level >140 mg/dL, and 80% had at least 1 intrapartum glucose level >110 mg/dL. In addition, 15% had at least 1 glucose level >180 mg/dL. None had maternal hypoglycemia after insulin treatment. Compared with expectant management, maternal glycemic control did not affect the median fetal C-peptide level (1.02; interquartile range, 0.52-1.85 vs 1.09; interquartile range, 0.61-1.65; P=.97) or other metabolic markers. Maternal glycemic control also did not affect neonatal hypoglycemia (49% vs 51%; P=.83) or other secondary neonatal or maternal outcomes. There was no evidence of effect modification by gestational age or body mass index at randomization, indication for betamethasone, duration from betamethasone to delivery, maternal race or ethnicity, or neonatal sex. In addition, the results were unchanged in a sensitivity analysis using a per-protocol approach., Conclusion: Maternal hyperglycemia was observed in most nondiabetic parturients after receiving late preterm betamethasone. However, there was no improvement in fetal metabolic status, neonatal hypoglycemia, or other neonatal or maternal outcomes with maternal glycemic control. Therefore, maternal glucose surveillance and treatment does not seem to be beneficial in nondiabetic parturients receiving late preterm steroids., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Excess carbohydrate intake during hypoglycemia can lead to rebound hyperglycemia (RH). We investigated associations between RH and use of real-time continuous glucose monitoring (rtCGM) and an rtCGM system's predictive alert., Methods: RH events were series of sensor glucose values (SGVs) >180 mg/dL starting within two hours of an antecedent SGV <70 mg/dL. Events were characterized by their frequency, duration (consecutive SGVs >180 mg/dL × five minutes), and severity (area under the glucose concentration-time curve). To assess the impact of rtCGM, data gathered during the four-week baseline phase (without rtCGM) and four-week follow-up phase (with rtCGM) from 75 participants in the HypoDE clinical trial (NCT02671968) of hypoglycemia-unaware individuals were compared. To assess the impact of predictive alerts, we identified a convenience sample of 24 518 users of an rtCGM system without predictive alerts who transitioned to a system whose predictive alert signals an SGV ≤55 mg/dL within 20 minutes (Dexcom G5 and G6, respectively). RH events from periods of blinded versus unblinded rtCGM wear and from periods of G5 and G6 wear were compared with paired t tests., Results: Compared to RH events in the HypoDE baseline phase, the mean frequency, duration, and severity of events fell by 14%, 12%, and 23%, respectively, in the follow-up phase (all P < .05). Compared to RH events during G5 use, the mean frequency, duration, and severity of events fell by 7%, 8%, and 13%, respectively, during G6 use (all P < .001)., Conclusions: Rebound hypreglycemia can be objectively quantified and mitigated with rtCGM and rtCGM-based predictive alerts.

This paper focuses on establishing an effective predictive model to quickly and accurately alert hypoglycemia and hyperglycemia for helping control blood glucose levels of people with diabetes. In general, a good predictive model is established on the features of data. Inspired by this, we first analyze the characteristics of continuous glucose monitoring (CGM) data by the equality of variances test and outlier detection, which show time-varying fluctuations and jump points in CGM data. Therefore, we incorporate the change detection method and the Winsorization method into the predictive model based on the autoregressive moving average (ARMA) model and the recursive least squares (RLS) method to fit the above characteristics. To the best of our knowledge, the proposed method is the first attempt to give a solution for matching the time-varying fluctuations and jump points of CGM data simultaneously. A case study using CGM data is given to validate the effectiveness of the proposed method under 30-min-ahead prediction. The results show that the proposed method can improve the true alarm ratio of hypoglycemia and hyperglycemia from 0.7983 to 0.8783, and lengthen the average advance detection time of hypoglycemia and hyperglycemia from 19.77 to 22.64 min., (© 2021. International Federation for Medical and Biological Engineering.)

Abstract: Glycemic control strategies for sepsis have changed significantly over the last decade, but their impact on dysglycemia and its associated outcomes has been poorly understood. In addition, there is controversy regarding the detrimental effects of hyperglycemia in sepsis. To evaluate the incidence and risks of dysglycemia under current strategy, we conducted a preplanned subanalysis of the sepsis cohort in a prospective, multicenter FORECAST study. A total of 1,140 patients with severe sepsis, including 259 patients with pre-existing diabetes, were included. Median blood glucose levels were approximately 140 mg/dL at 0 and 72 h indicating that blood glucose was moderately controlled. The rate of initial and late hyperglycemia was 27.3% and 21.7%, respectively. The rate of early hypoglycemic episodes during the initial 24 h was 13.2%. Glycemic control was accompanied by a higher percentage of initial and late hyperglycemia but not with early hypoglycemic episodes, suggesting that glycemic control was targeted at excess hyperglycemia. In nondiabetic patients, late hyperglycemia (hazard ratio, 95% confidence interval; P value: 1.816, 1.116-2.955, 0.016) and early hypoglycemic episodes (1.936, 1.180-3.175, 0.009) were positively associated with in-hospital mortality. Further subgroup analysis suggested that late hyperglycemia and early hypoglycemic episodes independently, and probably synergistically, affect the outcomes. In diabetic patients, however, these correlations were not observed. In conclusion, a significantly high incidence of dysglycemia was observed in our sepsis cohort under moderate glycemic control. Late hyperglycemia in addition to early hypoglycemia was associated with poor outcomes at least in nondiabetic patients. More sophisticated approaches are necessary to reduce the incidence of these serious complications., Competing Interests: The other authors report no conflicts of interest., (Copyright © 2021 by the Shock Society.)

Next-generation sequencing (NGS) technologies have been proposed as a first-line test for the diagnosis of inborn errors of metabolism (IEM), a group of genetically heterogeneous disorders with overlapping or nonspecific phenotypes. Over a 3-year period, we prospectively analyzed 311 pediatric patients with a suspected IEM using four targeted gene panels. The rate of positive diagnosis was 61.86% for intermediary metabolism defects, 32.84% for complex molecular defects, 19% for hypoglycemic/hyperglycemic events, and 17% for mitochondrial diseases, and a conclusive molecular diagnosis was established in 2-4 weeks. Forty-one patients for whom negative results were obtained with the mitochondrial diseases panel underwent subsequent analyses using the NeuroSeq panel, which groups all genes from the individual panels together with genes associated with neurological disorders (1870 genes in total). This achieved a diagnostic rate of 32%. We next evaluated the utility of a tool, Phenomizer, for differential diagnosis, and established a correlation between phenotype and molecular findings in 39.3% of patients. Finally, we evaluated the mutational architecture of the genes analyzed by determining z-scores, loss-of-function observed/expected upper bound fraction (LOEUF), and haploinsufficiency (HI) scores. In summary, targeted gene panels for specific groups of IEMs enabled rapid and effective diagnosis, which is critical for the therapeutic management of IEM patients.

Abstract: Despite advances in treatments for diabetes mellitus (DM), severe acute glycemic crises still occur. In this study, the characteristics of patients who were transported to an emergency department due to acute glycemic crises were investigated.We enrolled patients who were transported to our hospital by ambulance due to hypoglycemia or hyperglycemia during the period from January 2015 to December 2019. Initial glucose levels below 70 mg/dL and above 250 mg/dL were defined as hypoglycemia and hyperglycemia, respectively.In the 5-year period, 16,910 patients were transported to our hospital by ambulance. Of those patients, 87 patients (0.51%) were diagnosed with hypoglycemia, 26 patients (0.15%) were diagnosed with hyperglycemia and 1 patient was diagnosed with lactic acidosis. Compared to patients with hypoglycemia, blood urea nitrogen, serum potassium and hemoglobin levels were higher in patients with hyperglycemia. Systolic blood pressure was lower and pulse rate was higher in patients with hyperglycemia, possibly reflecting dehydration in hyperglycemia. Patients with hyperglycemia were younger (63 vs 70 years old, median), more likely to be hospitalized (92.3% vs 23.0%) with poorer prognosis (23.1% vs 4.6%) than those with hypoglycemia. In 64 DM patients with hypoglycemia, 34 patients were treated with insulin and 24 patients were treated with sulfonylurea or glinide, and their medication was often inappropriate. Excessive alcohol intake and malnutrition were the main causes of hypoglycemia in 23 non-DM patients. The main reasons for hyperglycemia were interrupted treatment, forgetting insulin injection and infection.To avoid acute glycemic crises, optimization of anti-DM therapy and education of patients are needed., Competing Interests: The authors have no funding and conflicts of interests to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Hyperglycaemia and hypoglycaemia are common in preterm infants and have been associated with increased risk of mortality and morbidity. Interventions to reduce risk associated with these exposures are particularly challenging due to the infrequent measurement of blood glucose concentrations, with the potential of causing more harm instead of improving outcomes for these infants. Continuous glucose monitoring (CGM) is widely used in adults and children with diabetes to improve glucose control, but has not been approved for use in neonates. The REACT trial aimed to evaluate the efficacy and safety of CGM in preterm infants requiring intensive care., Methods: This international, open-label, randomised controlled trial was done in 13 neonatal intensive care units in the UK, Spain, and the Netherlands. Infants were included if they were within 24 h of birth, had a birthweight of 1200 g or less, had a gestational age up to 33 weeks plus 6 days, and had parental written informed consent. Infants were randomly assigned (1:1) to real-time CGM or standard care (with masked CGM for comparison) using a central web randomisation system, stratified by recruiting centre and gestational age (<26 or ≥26 weeks). The primary efficacy outcome was the proportion of time sensor glucose concentration was 2·6-10 mmol/L for the first week of life. Safety outcomes related to hypoglycaemia (glucose concentrations <2·6 mmol/L) in the first 7 days of life. All outcomes were assessed on the basis of intention to treat in the full analysis set with available data. The study is registered with the International Standard Randomised Control Trials Registry, ISRCTN12793535., Findings: Between July 4, 2016, and Jan 27, 2019, 182 infants were enrolled, 180 of whom were randomly assigned (85 to real-time CGM, 95 to standard care). 70 infants in the real-time CGM intervention group and 85 in the standard care group had CGM data and were included in the primary analysis. Compared with infants in the standard care group, infants managed using CGM had more time in the 2·6-10 mmol/L glucose concentration target range (mean proportion of time 84% [SD 22] vs 94% [11]; adjusted mean difference 8·9% [95% CI 3·4-14·4]), equivalent to 13 h (95% CI 5-21). More infants in the standard care group were exposed to at least one episode of sensor glucose concentration of less than 2·6 mmol/L for more than 1 h than those in the intervention group (13 [15%] of 85 vs four [6%] of 70). There were no serious adverse events related to the use of the device or episodes of infection., Interpretation: Real-time CGM can reduce exposure to prolonged or severe hyperglycaemia and hypoglycaemia. Further studies using CGM are required to determine optimal glucose targets, strategies to obtain them, and the potential effect on long-term health outcomes., Funding: National Institute for Health Research Efficacy and Mechanisms Evaluation Programme., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objectives: Given the high incidence of hyperglycemia and hypoglycemia in hospital and the lack of prediction tools for this problem, we developed a clinical tool to assist early identification of individuals at risk for persistent adverse glycemia (AG) in hospital., Methods: We analyzed a cohort of 594 consecutive adult inpatients with type 2 diabetes. We identified clinical factors available early in the admission course that were associated with persistent AG (defined as ≥2 days with capillary glucose <4 or >15 mmol/L during admission). A prediction model for persistent AG was constructed using logistic regression and internal validation was performed using a split-sample approach., Results: Persistent AG occurred in 153 (26%) of inpatients, and was associated with admission dysglycemia (odds ratio [OR], 3.65), glycated hemoglobin ≥8.1% (OR, 5.08), glucose-lowering treatment regimen containing sulfonylurea (OR, 3.50) or insulin (OR, 4.22), glucocorticoid medication treatment (OR, 2.27), Charlson Comorbidity Index score and the number of observed days. An early-identification prediction tool, based on clinical factors reliably available at admission (admission dysglycemia, glycated hemoglobin, glucose-lowering regimen and glucocorticoid treatment), could accurately predict persistent AG (receiver-operating characteristic area under curve = 0.806), and, at the optimal cutoff, the sensitivity, specificity and positive predictive value were 84%, 66% and 53%, respectively., Conclusions: A clinical prediction tool based on clinical risk factors available at admission to hospital identified patients at increased risk for persistent AG and could assist early targeted management by inpatient diabetes teams., (Copyright © 2020 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.)

Background and Objectives: Continuous glucose monitoring (CGM) could be a valuable instrument for measurement of glucose concentration in preterm neonate. We undertook a systematic review and meta-analysis to compare the diagnostic accuracy of CGM devices to intermittent blood glucose evaluation methods for the detection of hypoglycaemic or hypoglycaemic events in preterm infants., Data Sources: A structured electronic database search was performed for studies that assessed the accuracy of CGM against any intermittent blood glucose testing methods in detecting episodes of altered glycaemia in preterm infants. No restrictions were used. Three review authors screened records and included studies., Data Extraction: Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. From individual patient data (IPD), sensitivity and specificity were determined using predefined thresholds. The mean absolute relative difference (MARD) of the studied CGM devices was assessed and if those satisfied the accuracy requirements (EN ISO 15197). IPD datasets were meta-analysed using a logistic mixed-effects model. A bivariate model was used to estimate the summary receiver operating characteristic curve (ROC) curve and extract the area under the curve (AUC). The overall level of certainty of the evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation., Results: Among 4481 records, 11 were included. IPD datasets were obtained for five studies. Only two of the studies showed an MARD lower than 10%, with none of the five CGM devices studied satisfying the European Union (EU) ISO 15197 requirements. Pooled sensitivity and specificity of CGM devices for hypoglycaemia were 0.39 and 0.99, whereas for hyperglycaemia were 0.87 and 0.99, respectively. The AUC was 0.70 and 0.86, respectively. The certainty of the evidence was considered as low to moderate. Limitations primarily related to the lack of representative population, reference standard and CGM device., Conclusions: CGM devices demonstrated low sensitivity for detecting hypoglycaemia in preterm infants, however, provided high accuracy for detection of hyperglycaemia., Prospero Registration Number: CRD42020152248., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Preterm infants are susceptible to hyperglycemia and hypoglycemia, conditions which may lead to adverse neurodevelopment. The use of continuous glucose monitoring devices (CGM) might help keeping glucose levels in the normal range, and reduce the need for blood sampling. However, the use of CGM might be associated with harms in the preterm infant., Objectives: Objective one: to assess the benefits and harms of CGM alone versus standard method of glycemic measure in preterm infants. Objective two: to assess the benefits and harms of CGM with automated algorithm versus standard method of glycemic measure in preterm infants. Objective three: to assess the benefits and harms of CGM with automated algorithm versus CGM without automated algorithm in preterm infants., Search Methods: We adopted the standard search strategy of Cochrane Neonatal to search the Cochrane Central Register of Controlled Trials (CENTRAL; 2020, Issue 9), in the Cochrane Library; MEDLINE via PubMed (1966 to 25 September 2020); Embase (1980 to 25 September 2020); and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) (1982 to 25 September 2020). We also searched clinical trials databases, conference proceedings, and reference lists of retrieved articles for randomized controlled trials and quasi-randomized trials., Selection Criteria: Randomized controlled trials (RCTs) and quasi-RCTs in preterm infants comparing: 1) the use of CGM versus intermittent modalities to measure glycemia (comparison 1); or CGM associated with prespecified interventions to correct hypoglycemia or hyperglycemia versus CGM without such prespecified interventions (comparison 2)., Data Collection and Analysis: We assessed the methodological quality of included trials using Cochrane Effective Practice and Organisation of Care Group (EPOC) criteria (assessing randomization, blinding, loss to follow-up, and handling of outcome data). We evaluated treatment effects using a fixed-effect model with risk ratio (RR) for categorical data and mean, standard deviation (SD), and mean difference (MD) for continuous data. We used the GRADE approach to assess the certainty of the evidence., Main Results: Four trials enrolling 138 infants met our inclusion criteria. Investigators in three trials (118 infants) compared the use of CGM to intermittent modalities (comparison one); however one of these trials was analyzed separately because CGM was used as a standalone device, without being coupled to a control algorithm like in the other trials. A fourth trial (20 infants) assessed CGM with an automated algorithm versus CGM with a manual algorithm. None of the four included trials reported the neurodevelopmental outcome, i.e. the primary outcome of this review. Within comparison one, the certainty of the evidence on the use of CGM on mortality during hospitalization is very uncertain (typical RR 3.00, 95% CI 0.13 to 70.30; typical RD 0.04, 95% CI -0.06 to 0.14; 50 participants; 1  study; very low certainty). The number of hypoglycemic episodes was reported in two studies with conflicting data. The number of hyperglycemic episodes was reported in one study (typical MD -1.40, 95% CI -2.84 to 0.04; 50 participants; 1 study). The certainty of the evidence was very low for all outcomes because of limitations in study design, and imprecision of estimates.  Three studies are ongoing., Authors' Conclusions: There is insufficient evidence to determine if CGM improves preterm infant mortality or morbidities. Long-term outcomes were not reported. Clinical trials are required to determine the most effective CGM and glycemic management regimens in preterm infants before larger studies can be performed to assess the efficacy of CGM  for reducing mortality, morbidity and long-term neurodevelopmental impairments. The absence of CGM labelled for neonatal use is still a major limit in its use as well as the absence of dedicated neonatal devices., (Copyright © 2020 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.)

Competing Interests: Disclosure The authors report no conflicts of interest in this work.

All original articles and meta-analysis use the single cut-off value to distinguish high-risk hyperglycemic from other acute myocardial infarction (AMI) patients. The mortality rate is 3.9 times higher in non-diabetic AMI patients with admission glycemia ≥6.1mmol compared to normoglycemic non-diabetic AMI patients. On the other hand, admission hypoglycemia in AMI is an important predictor of mortality. Because both admission hypo- and hyperglycemia correspond to higher in-hospital mortality, this graph is recognized as "J or U shaped curve". The review suggests two cut-off values for admission glycemia for risk assessment in AMI instead of single one because hypoglycemia as well as hyperglycemia represents a high-risk factor., (Copyright © 2020 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.)

Background and Aims: Although the use of Continuous Glucose Monitoring (CGM) is rapidly extending, little evidence is currently available on daily glycemic excursions after different bariatric procedures. We assessed glycemic patterns after sleeve gastrectomy (SG) and roux-en-Y gastric bypass (RYGB) using CGM., Methods and Results: Cross-sectional study in subjects who had undergone RYGB (n = 22) or SG (n = 29) since at least 1 year, without pre-surgery or in current diabetes (T2DM) remission. All subjects underwent 7 day-CGM (Dexcom G4 PLATINUM), which provides glucose variability (GV), number and time spent in hypoglycemia, hypoglycemia patterns (postprandial, nocturnal or mixed). All indexes of GV were higher after RYGB than after SG (p < 0.001). Twenty-eight (55%) subjects experienced hypoglycemia. The number of events was higher after RYGB than SG (p = 0.017) while it did not differ in subjects with or without pre-surgery T2DM (p = 0.129). Overall, 9 (32%) subjects presented hypoglycemia exclusively during the postprandial period, 8 (29%) an exclusively nocturnal pattern and 11 (39%) a mixed pattern. The nocturnal pattern was more frequent after SG than RYGB (53.8% vs 6.7%, p = 0.036) while no difference was observed in subjects with or without pre-surgery T2DM (p = 0.697). Hypoglycemia symptoms were more frequent in subjects with postprandial than in those with nocturnal pattern (77.8% vs 12.5%, p = 0.015)., Conclusions: RYGB is characterized by a greater GV and a higher number of hypoglycemia events mostly post-prandial and symptomatic, while SG is associated with nocturnal and often asymptomatic hypoglycemia. These findings suggest that post-bariatric hypoglycemia is a more complex, not exclusively, postprandial phenomenon., (Copyright © 2020 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.)

Glucose and insulin metabolism in patients with diabetes are profoundly altered by advanced chronic kidney disease (CKD). Risk of hypoglycemia is increased by failure of kidney gluconeogenesis, impaired insulin clearance by the kidney, defective insulin degradation due to uremia, increased erythrocyte glucose uptake during hemodialysis, impaired counterregulatory hormone responses (cortisol, growth hormone), nutritional deprivation, and variability of exposure to oral antihyperglycemic agents and exogenous insulin. Patients with end-stage kidney disease frequently experience wide glycemic excursions, with common occurrences of both hypoglycemia and hyperglycemia. Assessment of glycemia by glycated hemoglobin (HbA1c) is hampered by a variety of CKD-associated conditions that can bias the measure either to the low or high range. Alternative glycemic biomarkers, such as glycated albumin or fructosamine, are not fully validated. Therefore, HbA1c remains the preferred glycemic biomarker despite its limitations. Based on observational data for associations with mortality and risks of hypoglycemia with intensive glycemic control regimens in advanced CKD, an HbA1c range of 7% to 8% appears to be the most favorable. Emerging data on the use of continuous glucose monitoring in this population suggest promise for more precise monitoring and treatment adjustments to permit fine-tuning of glycemic management in patients with diabetes and advanced CKD., (© Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

An atypical course of type 2 diabetes should prompt a reconsideration of the diagnosis. First of all, consider the possibility of autoimmune diabetes (type 1 diabetes). Also bear in mind that an underlying condition, such as pancreatic carcinoma, could be the main cause of or an aggravating factor for the diabetes. Characteristics of diabetes mellitus that differ from a typical presentation of newly diagnosed type 2 diabetes are: (a) loss of body weight at the time of the diabetes mellitus diagnosis followed by continued weight loss despite diabetes treatment, (b) lack of metabolic syndrome characteristics (for example, hypertension, dyslipidaemia and obesity) and (c) sudden development of poor glycaemic control without other contributing factors.

This work contributes to the improvement of glucose quantification using near-infrared (NIR), mid-infrared (MIR), and combination of NIR and MIR absorbance spectroscopy by classifying the spectral data prior to the application of regression models. Both manual and automated classification are presented based on three homogeneous classes defined following the clinical definition of the glycaemic ranges (hypoglycaemia, euglycaemia, and hyperglycaemia). For the manual classification, partial least squares and principal component regressions are applied to each class separately and shown to lead to improved quantification results compared to when applying the same regression models for the whole dataset. For the automatic classification, linear discriminant analysis coupled with principal component analysis is deployed, and regressions are applied to each class separately. The results obtained are shown to outperform those of regressions for the entire dataset., Competing Interests: Declaration of competing interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Prolonged time elapsing between the blood drawing and separation of the cell mass may result in decreased sample glucose levels due to continuous glycolysis. This can lead to underdiagnoses of hyperglycemic states and overdiagnosis of hypoglycemia. We aimed to evaluate the clinical impact of shortened transit time and earlier centrifugation of laboratory specimens on reported glucose results and diagnosis of clinically significant hypoglycemia (<50 mg/dL) or elevated glucose levels (>100 mg/dL)., Methods: We assessed all fasting-serum glucose tests from the adult population (190 767 subjects) without known diabetes residing in Southern Israel. Before and after intervention periods were compared: 268 359 blood tests were performed during 2009-2010, and 317 336 during 2012-2013., Results: While glucose levels were 94.17 mg/dL ± 14.12 in 2012-2013 versus 83.53 mg/dL ± 14.50 in 2009-2010 (12.75% ± 0.88 increase, P < .001), the difference in glycated hemoglobin levels was statistically significant but clinically negligible: 5.84% ± 0.56 in 2012-2013 versus 5.88% ± 0.56 in 2009-2010 (0.53% ± 0.78 decrease, P < .01). There was an increased likelihood of a glucose result to be above 100 mg/dL following intervention: 9.80% versus 25.90%, P < .001. For clinics distanced over 40 km from the laboratory, age-adjusted odds ratio value was 1.26 (95% CI 1.13, 1.41). The proportion of samples with hypoglycemia values decreased from 0.33% to 0.03% ( P < .001)., Conclusions: We demonstrated an important change in glucose values over a two-year period following an improvement of the preanalytic processes. The intervention was related to an increase in the frequency of hyperglycemia results and a decrease in the number of hypoglycemia results. Future administrative projects should consider clinical consequences with involvement of all relevant stakeholders.

Background: Self-monitored blood glucose (SMBG) and real-time continuous glucose monitoring (rtCGM) are used by people living with type 1 diabetes (T1D) to assess glucose and inform decision-making. Percentage time in range (%TIR) between 3.9 and 10 mmol/L has been associated with incident microvascular complications using historical SMBG data. However, the association between %TIR calculated from rtCGM data has not been identified. This study investigates whether %TIR values generated from rtCGM and SMBG data significantly differ from each other in adults with T1D. Materials and Methods: rtCGM and SMBG data from the REPLACE-BG study were obtained and analyzed. The dataset contained rtCGM (Dexcom G4 Platinum) and SMBG (Contour Next) values for 226 participants during a run-in phase lasting up to 10 weeks, followed by the 26-week trial. Percentages times in hypoglycemic, euglycemia and hyperglycemic ranges were generated from rtCGM and SMBG data using last observation carry forward method (zero-order hold) and linear interpolation (first-order hold). Results: Participants had a median (interquartile range [IQR]) age of 43.0 (31.0-55.0) years, and hemoglobin A1C of 53 (49-57) mmol/mol [7.0 (6.6-7.4)%]. The median (IQR) %TIR was significantly higher with rtCGM than with SMBG; 63.0 (55.9-71.0)% versus 54.6 (45.6-63.0)%, respectively, P  < 0.001. Median %times in hypoglycemia and hyperglycemia were significantly different with SMBG than rtCGM ( P  < 0.001). SMBG-derived data using linear interpolation significantly differed from the carry forward method ( P  < 0.001 for all glycemic ranges). Differences reported were greater at night than during the day ( P  < 0.001 for all glycemic ranges). Conclusion: The %time in all glycemic ranges reported by SMBG and rtCGM differ significantly, suggesting relationships between times in ranges, and complication status may be different between monitoring modalities. In addition, varying methods of calculating %TIR from SMBG-derived data provide significantly differing results. %TIR targets may therefore vary by monitoring choice and methods of calculation and harmonization of TIR standards may be challenging.

Objective: There is a growing market for diabetes-alert dogs but little has been published regarding their ability to reliably detect hypoglycemia. We aimed to determine whether 2 dogs could detect hypoglycemic breath samples from people with type 1 diabetes (T1D) and then transfer detection to novel hypoglycemic breath samples., Methods: Breath samples were collected from individuals with T1D during times of normo-, hypo- and hyperglycemia. Two dogs, previously trained (3 alternative forced choice) with breath samples from 3 different individuals with T1D, were presented with 3 breath samples from the same individual: 1 hypoglycemic, 1 normoglycemic and 1 hyperglycemic, and trained to identify the hypoglycemic sample using a "yes/no" procedure. The dogs' ability to transfer detection was then tested by presenting them with a novel sample set from the same individual. Then we tested whether 1 dog could transfer detection of the odour of hypoglycemia by presenting new samples from a different individual., Results: One dog was able to transfer detection of the odour of hypoglycemia to samples from the same individual (specificity 89%, sensitivity 62%), but a second dog was not. Results were inconclusive regarding the ability of 1 dog to transfer detection of the odour of hypoglycemia across 2 individuals., Conclusions: The results suggest that some dogs can be trained to detect hypoglycemic breath of an individual with T1D, but detection may not transfer to novel samples from other individuals. Results should be interpreted with caution, as the dogs were trained with only a small number of breath samples before testing., (Copyright © 2019 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.)

Background Hyperglycemic crisis is one of the complications of diabetes mellitus, which is common in hospitalized diabetic patient with intercurrent illness, requiring immediate action to control blood glucose. As an effort to attain rapid, gradually and more definite blood glucose, insulin is given intravenously. This study aimed to explore the patterns of blood glucose in hyperglycemic crisis and intercurrent illness, precipitating conditions, insulin regimen and blood glucose (BG) level results. Methods It was a cross-sectional study conducted on type 2 diabetic patients. The inclusion criteria were as follows: hospitalized in the general/internal medicine ward with or without any complication or comorbidity receiving intravenous insulin therapy; have pre- and post-BG data after insulin intervention. Results In 3 months of the study period, 22 patients fulfilled the inclusion criteria with 28 cases of intravenous insulin therapy, and 1 patient could get more than one intervention. The major condition toward a hyperglycemic crisis condition was infection. The patient's BG before interventions was 243 mg/dL to more than 600 mg/dL. The dosage of insulin varied from 4 to 10 units per hour, intravenously with a frequency of 1-4 times. The dosage consideration was not only based on BG levels but also on the patient's condition. The reduction in BG level varied greatly between 0.2 and 28.1 mg/dL per unit of insulin. The BG level of three patients did not decrease. On the other hand, one patient experienced mild hypoglycemia. Conclusions Infection conditions were the most common factor for the hyperglycemia crisis. Moreover, intravenous insulin dosing was done individually, and there was a large variation in the results of the decrease in BG levels.

Background: Point-of-care (POC) glucometers are commonly used in intensive care units (ICUs). The Centers for Medicare & Medicaid Services have called into question the accuracy of POC glucometers in critically ill patients. This study sought to identify specific characteristics within our facility's ICU patients that were associated with inaccuracies in POC glucose measurements., Methods: We conducted a prospective cohort study that compared POC capillary blood glucose samples with venous samples collected in our ICU. All nonpregnant patients >18 years old admitted to the ICU with orders for daily laboratory testing that included blood glucose were eligible for inclusion., Results: A total of 46 patients were enrolled and 85 samples were collected. The mean difference between venous and POC samples was 5.23 mg/dL (95% CI, 3.16-7.3 mg/dL). Measurement inaccuracies would have altered treatment in 7/85 instances (8.2%). The only clinically significant inaccuracy found was the omission of 2 units of insulin in 1 hyperglycemic patient. Measurement inconsistencies generally underestimated low blood glucose values (2/2 instances) and overestimated high blood glucose values (4/5 instances)., Conclusions: In our study, the mean difference between venous and POC glucose samples was small. Similarly, measurement inaccuracies that would have altered treatment were rare and only one instance was deemed clinically significant. We conclude that POC capillary glucose testing within our cohort and in similar critically ill patients is likely safe and effective.

Physiologic adaptations in the postnatal period, along with gradual establishment of enteral feeding, help maintain plasma glucose concentrations in the neonatal period. The definition of normal plasma glucose in the neonatal period has been a subject of debate because of a lack of evidence linking a set plasma or blood glucose concentration to clinical symptoms or predictors of short- and long-term outcomes. However, there is consensus that maintaining plasma glucose in the normal range for age is important to prevent immediate and long-term neurodevelopmental consequences of hypoglycemia or hyperglycemia. The specific management strategy for abnormal glucose levels in neonates depends on the underlying etiology, and interventions could include nutritional changes, medications, hormone therapy, or even surgery. Here, we will review the physiological processes that help maintain plasma glucose in newborns and discuss the approach to a newborn with disordered glucose homeostasis, with an emphasis on the endocrine basis of abnormal glucose homeostasis., (Copyright © 2020 by the American Academy of Pediatrics.)

Aim: To describe the characteristics of diabetes diagnosis, insulin therapy regarding type, dose and frequency also the degree of glycemic control achieved in toddlers and preschoolers coming from families with limited resources., Methods: Over one year, 69 toddlers and preschoolers clinically diagnosed as type 1 diabetes were included. Data related to presentation at diagnosis, insulin therapy and glycemic control was reviewed from patients' care givers and medical records., Results: Diabetic ketoacidosis (DKA) was recorded in 71% of the children at initial diagnosis. The mean time since diabetes diagnosis was 2 ± 1 years. Most of children (65/69, 94%) were on basal-bolus regimen while four (6%) were on basal insulin only. NPH and long acting analogues were used as basal insulin in (74%) and (26%). Regular insulin and rapid acting analogues were used as bolus insulin (27%) and (73%).The mean frequency of daily self monitoring of blood glucose was (2.9 ± 1). The arithmetic mean of HbA1c done over past 6 months to one year before inclusion in the study was 8.2 ± 1.5% (66 ± 12 mmol/mol) with 65% having HbA1c ≥ 7.5% (≥58 mmol/mol)., Conclusion: Toddlers and preschoolers with diabetes coming from families with limited resources frequently present with DKA at diagnosis and have suboptimal self-monitoring of blood glucose and glycemic control. NPH is more commonly used in this age group, combined with postprandial rapid analogues and less commonly preprandial regular insulin and that yields more favorable HbA1c but with a greater risk of hypoglycemia. The most common cause of hospital readmission was ketoacidosis and uncontrolled hyperglycemia., (Copyright © 2019. Published by Elsevier B.V.)

Introduction : Type 1 diabetes (T1D) is a chronic condition characterized by a complete deficiency in insulin production. Optimal management requires constant knowledge of glucose levels for safe and effective insulin administration. Self-monitoring of blood glucose (SMBG) using capillary blood glucose meters is cumbersome and provides limited information to guide management. Continuous glucose monitoring (CGM) technology addresses many of these gaps, but itself has limitations which have prevented people with diabetes and their clinicians from fully embracing this technology. This review covers the benefits and limitations of CGM use, and looks toward future application of this technology in the management of T1D. Areas covered : Impact of CGM on physical and psychosocial outcomes in people with T1D. Barriers to CGM uptake. Integration with insulin pumps and other technologies. Opportunities for future application. Expert opinion : CGM technology will be utilized by the majority of people with T1D and increasing numbers of people with type 2 diabetes due to improved insurance coverage and easier-to-use systems. Its use as part of artificial pancreas systems will add further utility, as it will help to protect from both hypoglycemia and hyperglycemia. People with diabetes will spend more time in range and experience fewer acute and chronic complications.

Background: In patients with acute ischemic stroke, current guidelines recommend maintaining blood glucose levels in a range of 140-180 mg/dL and closely monitoring to prevent hypoglycemia (<60 mg/dL). We aimed to assess glucose variability by continuous glucose monitoring (CGM) and to demonstrate the risk of acute ischemic stroke patients with glucose levels outside of the glucose management recommendations., Methods: Patients with ischemic stroke admitted within 7 days after onset were prospectively enrolled, and their blood glucose levels were monitored every 15 minutes for 72-hour period using the FreeStyle Libre Pro. Multivariate logistic regression analyses were used to analyze potential predictors for hyperglycemic (>180 mg/dL) and hypoglycemic (<60 mg/dL) events., Results: A total of 39 acute ischemic stroke patients (mean age 75.9 ± 11.5 years) were enrolled, and CGM was started from 58.6 ± 41.9 hours after stroke onset. CGM showed hypoglycemic events in 19 patients and hyperglycemic events in 21 patients, and the frequencies of hypo- and hyperglycemic events during CGM were 10.1 ± 15.7% and 11.9 ± 22.5%, respectively. Hypoglycemic events were mainly observed in the night-time in patients with normoglycemia at admission. Logistic regression analyses demonstrated significant associations between the blood glucose level at admission and hypo- and hyperglycemic events on CGM., Conclusions: This study of CGM found that many stroke patients have blood glucose levels outside the recommended guideline range in the acute phase. Blood glucose level on admission may be used as a predictor for hypo- and hyperglycemic events after admission., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Context: The association of inpatient glucose measurements with amputations in patients admitted with acute diabetic foot has not been described., Objective: To evaluate the relationship of hyperglycemia, hypoglycemia, and glucose variability during hospitalization with amputations in patients hospitalized with acute diabetic foot., Design: Retrospective cohort study., Setting: Academic tertiary hospital., Patients: We reviewed demographic, clinical, laboratory, and point-of-care glucose data in patients hospitalized with acute diabetic foot in the Diabetic Foot Unit during 2015 through 2017., Main Outcome Measures: The primary outcomes were any or major amputations during hospitalization. Secondary outcomes included length of hospitalization and in-hospital mortality., Results: During the study period, 418 patients were hospitalized in the Diabetic Foot Unit and 45,496 glucose measurements were taken. Patients experiencing any hyperglycemia and any or severe hypoglycemia were more likely to undergo any or major amputations during hospitalization. High glycemic variability was associated with major amputations. Peripheral vascular disease (PVD), high Wagner score, and hypoglycemia were independent predictors of amputations. Older age, PVD, previous amputation, elevated white blood cell level, high Wagner score, and hypoglycemia were independent predictors of major amputations., Conclusions: In-patient hypoglycemia emerged as an independent risk factor for any and major amputations. Although it is unclear whether hypoglycemia directly contributes to adverse outcomes or is simply a biomarker of disease severity, efforts to minimize in-hospital hypoglycemic events are warranted., (Copyright © 2019 Endocrine Society.)

Background: Current therapy for Type 1 diabetes (T1D) is characterized by significant glucose variability (GV). Pancreas transplantation (PT) is performed in certain T1D patients with and without end-stage renal disease. To date, GV has been examined to a limited extent after PT., Methods: We investigated GV using continuous glucose monitoring (CGM) 3-6 weeks after PT., Results: Eleven patients had simultaneous kidney pancreas transplantation (SPK), nine pancreas after kidney (PAK), and six pancreas transplantation alone (PTA). Mean CGM showed no difference between SPK, 126.5 ± 13.9, PAK 119.9 ± 12.8, and PTA 131.1 ± 29 mg/dL (P value .6). Percentage of time in range (TIR, 70-180 mg/dL) was 92% for SPK, 93.4% in PAK, and 88.5% in PTA with only 0.3%, 1.5%, and 0.3% of time <70 mg/dL. Percentage >180 mg/dL was 7.9% for SPK, 4.9% PAK, and 11% in PTA. Other measures of GV were similar in the three cohorts. In six patients, CGM was performed before and after PT and improved significantly. GV was also better compared with a matched cohort of T1D patients., Conclusions: All 3 types of PT resulted in excellent glucose control 3-6 weeks post-procedure. CGM outcomes represent an important objective outcome after PT., (© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Context: Use of continuous glucose monitoring (CGM) is increasing for insulin-requiring patients with diabetes. Although data on glycemic profiles of healthy, nondiabetic individuals exist for older sensors, assessment of glycemic metrics with new-generation CGM devices is lacking., Objective: To establish reference sensor glucose ranges in healthy, nondiabetic individuals across different age groups using a current generation CGM sensor., Design: Multicenter, prospective study., Setting: Twelve centers within the T1D Exchange Clinic Network., Patients or Participants: Nonpregnant, healthy, nondiabetic children and adults (age ≥6 years) with nonobese body mass index., Intervention: Each participant wore a blinded Dexcom G6 CGM, with once-daily calibration, for up to 10 days., Main Outcome Measures: CGM metrics of mean glucose, hyperglycemia, hypoglycemia, and glycemic variability., Results: A total of 153 participants (age 7 to 80 years) were included in the analyses. Mean average glucose was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those over 60 years, in whom mean average glucose was 104 mg/dL (5.8 mmol/L). The median time between 70 to 140 mg/dL (3.9 to 7.8 mmol/L) was 96% (interquartile range, 93 to 98). Mean within-individual coefficient of variation was 17 ± 3%. Median time spent with glucose levels >140 mg/dL was 2.1% (30 min/d), and median time spent with glucose levels <70 mg/dL (3.9 mmol/L) was 1.1% (15 min/d)., Conclusion: By assessing across age groups in a healthy, nondiabetic population, normative sensor glucose data have been derived and will be useful as a benchmark for future research studies., (Copyright © 2019 Endocrine Society.)

Purpose of Review: This review aims to provide a summary of the evaluation and treatment of older adults (≥ 65 years) with type 2 diabetes and/or hyperglycemia in the hospital., Recent Findings: Caring for these older adults requires special considerations. Diabetes is a risk factor for hospitalization and hyperglycemia in the hospital is associated with increased complications and mortality. Treatment plans for hospitalized older adults with diabetes should include a comprehensive geriatric assessment. This team-based approach aims to develop an individualized care plan, with consideration of the patients' personal goals, comorbidities, functional status, life expectancy, and hypoglycemia risk. Studies from hospitalized middle age and older adults with hyperglycemia can help guide diabetes treatment goals and management in older adults. Further studies, examining both glucose targets and care management assessments and treatment plan specifically targeting older adults in the hospital setting, are needed.

Objective and Background: In 2015 approximately 5.0 million people were estimated to have died from diabetes. Poor glycemic control is the most determinant of diabetes-related complication and death. The percentage of patients whose blood glucose level are not well controlled remains high yet. The aim of this study is to identify the determinants of poor glycemic control at the diabetes clinic of the Jimma University Medical Center from April 01 to June 30/2017., Methods: Facility-based case-control study design was conducted on patients with type 2 diabetes mellitus on follow-up at the diabetes clinic of Jimma University medical center. The consecutive sampling technique was employed and data were collected from April to June 2017. The data were entered using Epidata manager version 4.0.2 and exported to SPSS Version 21 for analysis. Logistic regression analysis was performed and variables with the p-value of less than 0.05 were considered as statistically significant determinants of poor glycemic control., Result: The study was conducted on 410 patients, of which 228 males and 182 females. The determinants of poor glycemic control were comorbidities [Adjusted odd ratio(AOR) = 2.56, 95%CI = 1.10-5.96], lack of self-monitoring blood glucose [AOR = 3.44,95%CI = 1.33-8.94], total cholesterol level of 200 mg/dl or more [AOR = 3.62, 95%CI = 1.46-8.97], diabetes duration of greater than 7 years [AOR = 3.08, 95%CI = 1.33-7.16], physical activity of three or less than three days [AOR = 4.79, 95%CI = 1.70-13.53], waist to hip ratio of 0.9 or greater for male and 0.85 or greater for female [AOR = 3.52, 95%CI = 1.23-10.11], being on metformin plus insulin [AOR = 9.22, 95%CI = 2.90-29.35] and being on insulin [AOR = 4.48, 95%CI = 1.52-13.16]., Conclusion: Lack of Self-monitoring blood glucose, presence of comorbidities, duration of diabetes mellitus, physical activity of three or less than three days, total cholesterol of 200 mg/dl or more, waist to hip ratio of 0.9 or greater for male and 0.85 or greater for female, and types of antidiabetic medication were the independent predictors of poor glycemic control. Effort should be made towards reducing these factors by the concerned body.

Aims: To evaluate the impact of a prepregnancy care (PPC) programme, beyond HbA 1c , on hypoglycaemia awareness and glycaemic variability (GV)., Methods: Prospective pilot study. We selected women with Type 1 diabetes who initiated a PPC programme with normal hypoglycaemia awareness (n = 24). Hypoglycaemia awareness, hypoglycaemic events and GV derived from masked-continuous glucose monitoring were evaluated in the first visit and within 2 weeks after pregnancy confirmation., Results: The duration was 16.5 ± 13.0 months. HbA 1c significantly decreased (-0.8 ± 0.7; p < 0.001). The Clarke score increased (0[0-1] vs. 1[0-2] points, p = 0.164), 2 out of 24 were reclassified as having impaired awareness of hypoglycaemia and 2 presented severe hypoglycaemia. GV decreased: standard deviation (p = 0.008), coefficient of variation (p = 0.021), mean amplitude of glycaemic excursions (p = 0.007), average daily risk range (p < 0.001), J-index (p = 0.010), high blood glucose index (HBGI) (p = 0.004), continuous overall net glycaemic action (CONGA) (p = 0.018), mean of daily differences (p = 0.045) and glycaemic risk assessment diabetes equation (p = 0.012). Final HbA 1c was associated with baseline J-index, CONGA and HBGI (β = 0.535, β = 0.466, β = 0.534, respectively; p < 0.05)., Conclusions: A PPC programme improved HbA 1c as well as GV with no significant impact on hypoglycaemia awareness. Moreover, GV could help to identify women less likely to achieve glycaemic targets. Larger studies are needed to confirm these results., (Copyright © 2019 Elsevier B.V. All rights reserved.)