Nocturnal Hypoglycemic Events Research Articles

Association of physical activity patterns with nocturnal hypoglycemia events in youth with type 1 diabetes.

This study sought to elucidate the interactions among physical activity (PA) patterns, mean glucose concentrations, and the incidence of nocturnal hypoglycemia events in children and adolescents with type 1 diabetes, examining the moderating influence of daily dosage on these associations. Eighty-two participants aged 6 to 18 years (43.9% girls) from the Diactive-1 Cohort Study, diagnosed with type 1 diabetes, were included. Data collection involved continuous glucose monitoring, accelerometry to assess real-world PA, as well as documentation of daily insulin doses and carbohydrate counting over the same seven days. A total of 19 participants experienced at least one nocturnal hypoglycemia event over a span of 574 measurement days (106 days with and 451 days without nocturnal hypoglycemia). Higher levels of vigorous PA (VPA) were associated with lower same-day mean glucose levels (p = 0.014). Additionally, higher levels of moderate PA (p = 0.023), VPA (p = 0.011), and moderate-to-vigorous PA (p = 0.010) were associated with a greater number of nocturnal hypoglycemia events. Specifically, a significant association was identified between VPA and nocturnal hypoglycemia events when the daily insulin dose were at or above 1.04 units per kilogram of body weight per day (p = 0.016). Daily VPA is associated with glucose reductions, potentially leading to more hypoglycemic episodes, particularly when there's an excess of daily insulin. This highlights the need for careful insulin management in children and adolescents with type 1 diabetes engaging in VPA.

Efficacy and safety of basal insulins in people with type 2 diabetes mellitus: a systematic review and network meta-analysis of randomized clinical trials.

The comparative effectiveness of basal insulins has been examined in several studies. However, current treatment algorithms provide a list of options with no clear differentiation between different basal insulins as the optimal choice for initiation. A comprehensive search of MEDLINE, Embase, Cochrane Library, ISI, and Scopus, and a reference list of retrieved studies and reviews were performed up to November 2023. We identified phase III randomized controlled trials (RCTs) comparing the efficacy and safety of basal insulin regimens. The primary outcomes evaluated were HbA1c reduction, weight change, and hypoglycemic events. The revised Cochrane ROB-2 tool was used to assess the methodological quality of the included studies. A random-effects frequentist network meta-analysis was used to estimate the pooled weighted mean difference (WMD) and odds ratio (OR) with 95% confidence intervals considering the critical assumptions in the networks. The certainty of the evidence and confidence in the rankings was assessed using the GRADE minimally contextualized approach. Of 20,817 retrieved studies, 44 RCTs (23,699 participants) were eligible for inclusion in our network meta-analysis. We found no significant difference among various basal insulins (including Neutral Protamine Hagedorn (NPH), ILPS, insulin glargine, detemir, and degludec) in reducing HbA1c. Insulin glargine, 300 U/mL (IGlar-300) was significantly associated with less weight gain (mean difference ranged from 2.9 kg to 4.1 kg) compared to other basal insulins, namely thrice-weekly insulin degludec (IDeg-3TW), insulin degludec, 100 U/mL (IDeg-100), insulin degludec, 200 U/mL (IDeg-200), NPH, and insulin detemir (IDet), but with low to very low certainty regarding most comparisons. IDeg-100, IDeg-200, IDet, and IGlar-300 were associated with significantly lower odds of overall, nocturnal, and severe hypoglycemic events than NPH and insulin lispro protamine (ILPS) (moderate to high certainty evidence). NPH was associated with the highest odds of overall and nocturnal hypoglycemia compared to others. Network meta-analysis models were robust, and findings were consistent in sensitivity analyses. The efficacy of various basal insulin regimens is comparable. However, they have different safety profiles. IGlar-300 may be the best choice when weight gain is a concern. In contrast, IDeg-100, IDeg-200, IDet, and IGlar-300 may be preferred when hypoglycemia is the primary concern.

Efficacy and Safety of Insulin Degludec/Insulin Aspart versus Biphasic Insulin Aspart 30 in Patients with Type 2 Diabetes: A Meta-Analysis of Randomized Controlled Trials.

We systematically reviewed and analyzed the efficacy and safety of insulin degludec/insulin as-part (IDegAsp) versus biphasic insulin aspart 30 (BIAsp 30) in patients with type 2 diabetes (T2D). We used computers to search the Embase, PubMed, Clinical Trials, and the Cochrane Library database, and collected randomized controlled trials (RCTs) on the treatment of IDegAsp versus BIAsp 30 in T2D patients. The research period was from the establishment of the database to May 19, 2023. We used Review Manager 5.20 statistical software for systematic meta-analysis. We included 8 RCTs with 2281 participants. IDegAsp was better to BIAsp30 in improving fasting plasma glucose (FPG) levels (P<0.001) and reducing the endpoint daily average insulin dose (P<0.01). Furthermore, compared with BIAsp30, IDegAsp significantly reduced the risk of nocturnal hypoglycemic events (P<0.001). However, there was no significant difference in the improvement of body weight change (P=0.99), glycosylated hemoglobin (P=0.50), the overall risk of hypoglycemic events (P=0.57) and adverse events (P=0.89) between the two groups. Compared with BIAsp30, IDegAsp could significantly reduce FPG levels, insulin dosage, and the risk of nocturnal hypoglycemic events in T2D patients, without increasing the overall risk of adverse events.

Glycemic Management Around Postprandial Exercise in People With Type 1 Diabetes: Challenge Accepted.

The precise glycemic impact and clinical relevance of postprandial exercise in type 1 diabetes (T1D) has not been clarified yet. This work aimed to examine acute, subacute, and late effects of postprandial exercise on blood glucose (BG). A randomized, controlled trial comprised 4 laboratory visits, with 24-hour follow-up at home. Participants included adults with T1D (n = 8), aged 44 ± 13 years, with body mass index of 24 ± 2.1. Intervention included 30 minutes of rest (CONTROL), walking (WALK), moderate-intensity (MOD), or intermittent high-intensity (IHE) exercise performed 60 minutes after a standardized meal. Main outcome measures included BG change during exercise/control (acute), and secondary outcomes included the subacute (≤2 h after) and late glycemic effects (≤24 h after). Exercise reduced postprandial glucose (PPG) excursion compared to CONTROL, with a consistent BG decline in all patients for all modalities (mean declines -45 ± 24, -71 ± 39, and -35 ± 21 mg/dL, during WALK, MOD, and IHE, respectively (P < .001). For this decline, clinical superiority was demonstrated separately for each exercise modality vs CONTROL. Noninferiority of WALK vs MOD was not demonstrated, noninferiority of WALK vs IHE was demonstrated, and equivalence of IHE vs MOD was not demonstrated. Hypoglycemia did not occur during exercise. BG increased in the hour after exercise (more than after CONTROL; P < .001). More than half of participants showed hyperglycemia after exercise necessitating insulin correction. There were more nocturnal hypoglycemic events after exercise vs CONTROL (P < .05). Postprandial exercise of all modalities is effective, safe, and feasible if necessary precautions are taken (ie, prandial insulin reductions), as exercise lowered maximal PPG excursion and caused a consistent and clinically relevant BG decline during exercise while there was no hypoglycemia during or shortly after exercise. However, there seem to be 2 remaining challenges: subacute postexercise hyperglycemia and nocturnal hypoglycemia.

Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections.

Nocturnal hypoglycemia is a known challenge for people with type 1 diabetes, especially for physically active individuals or those on multiple daily injections. We developed an evidential neural network (ENN) to predict at bedtime the probability and timing of nocturnal hypoglycemia (0-4 vs 4-8h after bedtime) based on several glucose metrics and physical activity patterns. We utilized these predictions in silico to prescribe bedtime carbohydrates with a Smart Snack intervention specific to the predicted minimum nocturnal glucose and timing of nocturnal hypoglycemia. We leveraged free-living datasets collected from 366 individuals from the T1DEXI Study and Glooko. Inputs to the ENN used to model nocturnal hypoglycemia were derived from demographic information, continuous glucose monitoring, and physical activity data. We assessed the accuracy of the ENN using area under the receiver operating curve, and the clinical impact of the Smart Snack intervention through simulations. The ENN achieved an area under the receiver operating curve of 0.80 and 0.71 to predict nocturnal hypoglycemic events during 0-4 and 4-8h after bedtime, respectively, outperforming all evaluated baseline methods. Use of the Smart Snack intervention reduced probability of nocturnal hypoglycemia from 23.9 ± 14.1% to 14.0 ± 13.3% and duration from 7.4 ± 7.0% to 2.4 ± 3.3% in silico. Our findings indicate that the ENN-based Smart Snack intervention has the potential to significantly reduce the frequency and duration of nocturnal hypoglycemic events. A decision support system that combines prediction of minimum nocturnal glucose and proactive recommendations for bedtime carbohydrate intake might effectively prevent nocturnal hypoglycemia and reduce the burden of glycemic self-management.

Efficacy and safety of once-weekly versus once-daily basal insulin analogues in the treatment of type 2 diabetes mellitus: A systematic review and meta-analysis.

To summarize the evidence of recently published randomized controlled trials (RCTs) studying efficacy, in terms of glycaemic control, and safety of the newly developed once-weekly basal insulin analogues. A systematic literature search was conducted through Medline (via PubMed), Cochrane Library and Google Scholar until June 30, 2023. Double-independent study selection, data extraction and quality assessment were performed. Results were summarized with random-effects meta-analysis. A total of 3962 patients with type 2 diabetes mellitus (T2DM) among nine RCTs were analysed. All RCTs had low risk of bias according to the Cochrane Collaboration risk-of-bias tool (RoB2). Once-weekly insulins demonstrated better efficacy in glycated haemoglobin (HbA1c) reduction (mean difference [MD] -0.13%, 95% confidence interval [CI] -0.23, -0.03; P = 0.08) and a significantly greater time in range compared with once-daily insulin analogues (MD 3.54%, 95% CI 1.56, 5.53; P = 0.005). Based on subgroup analyses, the reduction in HbA1c and the odds of achieving an end-of-treatment HbA1c <6.5% were significantly greater for icodec compared to the once-daily insulin (MD -0.18%, 95% CI -0.27, -0.09 [P < 0.001] and odds ratio [OR] 1.75, 95% CI 1.34, 2.29 [P < 0.001], respectively). Once-weekly insulins were associated with higher odds of level 1 hypoglycaemia during the 24-hour period (OR 1.3, 95% CI 1.04, 1.64; P = 0.02) but were safer in terms of level 2 or 3 nocturnal hypoglycaemic events (OR 0.74, 95% CI 0.56, 0.97; P = 0.03). No difference was observed regarding serious adverse events between the two groups. The once-weekly basal insulin analogues seem to be at least equally efficient in glycaemic management and safe compared to once-daily injections in people with T2DM. Phase 4 RCTs are expected to shed further light on the effectiveness and safety of once-weekly insulin therapy over the long term.

Differential correlation between time in range and eGFR or albuminuria in type 2 diabetes

IntroductionAs a CGM-derived indicator, ‘time in range’ (TIR) is emerging as a key indicator for accurate assessment of glycaemic control. However, there is few report focusing on the correlation of TIR with albumuria and renal fuction. The aim of this work was to investigate whether TIR, as well as nocturnal TIR and hypoglycaemic events is related to the presence and severity of albuminuria and decrease of eGFR in type 2 diabetes.Research design and methodsA total of 823 patients were enrolled in this study. All patients received continuous glucose monitoring, TIR indicating the percentage of time that blood glucose was in the range of 3.9–10.0 mmol/L. The Spearman analysis was applied to analyze the relationship between TIR (or nocturnal TIR) and ACR. Logistic regression was used to explore whether TIR (or nocturnal TIR) is an independent risk factor for albuminuria.ResultsThe prevalence of albuminuria decreased with increasing TIR quartiles. Binary logistic regression revealed that TIR as well as nocturnal TIR was obviously related to the presence of albuminuria. Multiple regression analysis found that only nocturnal TIR was obviously related to the severity of albuminuria. In our study, eGFR was significantly associated with the number of hypoglycemic events.ConclusionsIn T2DM patients, TIR and nocturnal TIR is associated with the presence of albuminuria independent of HbA1c and GV metrics. Nocturnal TIR shows better correlation than TIR. The role of TIR especially nocturnal TIR in the evaluation of diabetes kidney disease should be emphasized.

The safety of sotagliflozin in the therapy of diabetes mellitus type 1 and type 2: A meta-analysis of randomized trials.

BackgroundDiabetes mellitus (DM) is a global health problem, and it has become a shocking threat in the contemporary era. The objective of this study was to analyze the safety of sotagliflozin in patients with DM systematically and intuitively.MethodsOn November 15, 2021, literature retrieval was performed on PubMed, Web of Science, EBSCO, and Cochrane libraries. The meta-analysis results included genital mycotic infection, related-to-acidosis events, and other related adverse events, including diarrhea, severe nocturnal hypoglycemia event, and volume depletion. In addition, a subgroup analysis was also conducted based on different doses of sotagliflozin. Moreover, the patient-treated years analyzed in the study were 12 weeks, 24 weeks, and 52 weeks, respectively, for type 1 diabetes, and were 12 weeks, 22 weeks, and 52 weeks, respectively, for type 2 diabetes.ResultsThe results of this meta-analysis illustrated that sotagliflozin could increase the risk of genital mycotic infection for patients with T1D and T2D (RR: 3.49, 95% Cl: 2.54-4.79, p < 0.001; RR: 2.83, 95% Cl: 2.04-3.93, p < 0.001; respectively). In addition, the subgroup analysis showed that the drug doses that could increase the risk of genital mycotic infection were 400 mg and 200 mg (RR: 3.63, 95% Cl: 2.46-5.36, p < 0.001; RR: 3.21, 95% Cl: 1.84-5.62, p < 0.001; respectively) in T1D. Moreover, sotagliflozin could increase the risk of events related to acidosis in the patients of T1D, including acidosis-related adverse events, positively adjudicated diabetic ketoacidosis, acidosis-related event, and diabetic ketoacidosis (RR: 7.49, 95% Cl: 3.20-17.52, p < 0.001; RR: 6.05, 95% Cl: 2.56-14.30, p < 0.001; RR: 4.83, 95% Cl: 3.13-7.45, p < 0.001; RR: 8.12, 95% Cl: 3.06-21.52, p < 0.001; respectively). In the patients of T2D, sotagliflozin could not increase the risk of DKA (RR: 1.30, 95% Cl: 0.34-4.99, p = 0.70). About serious of acidosis-related adverse events, positively adjudicated diabetic ketoacidosis (DKA) and acidosis-related event, the included studies were not reported for T2D patients. As for the other related adverse events, sotagliflozin was found to be a risk factor for diarrhea and volume depletion in T1D patients (RR: 1.44, 95% Cl: 1.09-1.90, p = 0.01; RR: 2.50, 95% Cl: 1.33-4.69, p < 0.01; respectively) and T2D patients (RR: 1.44, 95% Cl: 1.26-1.64, p < 0.001; RR: 1.25, 95% Cl: 1.07-1.45, p < 0.01; respectively).ConclusionsThis meta-analysis showed that the adverse events of sotagliflozin were tolerable to patients with DM, in terms of the incidence of genital mycotic infection, related-to-acidosis events, diarrhea, volume depletion, and severe nocturnal hypoglycemia events. In addition, the subgroup analysis of sotagliflozin dosage is considered to have great clinical significance for future guidance of sotagliflozin application in patients with DM.

Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models.

In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of prediction models based on machine learning techniques. Experiments were performed on two cohorts of patients suffering from type 1 diabetes mellitus with significant differences in their clinical outcomes. In the first contribution, we have demonstrated that the chosen methodology is able to replicate the intrinsic characteristics of individual patients following the statistical distributions of the original data. Next, a second contribution demonstrates the potential of synthetic data to improve the performance of machine learning approaches by testing and comparing different prediction models for the problem of predicting nocturnal hypoglycemic events in type 1 diabetic patients. The results obtained for both generative and predictive models are quite encouraging and set a precedent in the use of generative techniques to train new machine learning models.

Novel Device Used to Monitor Hand Tremors during Nocturnal Hypoglycemic Events

Diabetes is one of the lifelong diseases that require systematic medical care to avoid life-menacing ramifications. Uncontrolled diabetes can cause severe damage to most internal body organs, probably leading to death. Particularly, nocturnal hypoglycemic that occur usually at night during sleep. Severe cases of these events can lead to seizures, fainting, loss of consciousness, and death. The current medical devices lack to give the warning to reduce the risk of acquiring nocturnal hypoglycemic events because they use only for glucose monitoring during waking times. Consequently, the main goal of this work is to design and implement a new wearable device to detect and monitor tremors, which occur when a user has hypoglycemia (low blood sugar). The device can detect a frequency range of 4–12 Hz by using the accelerometer of Arduino Nano 33 BLE. It can send a signal to the phone application (app) via Bluetooth Low Energy (BLE). Once the phone receives a signal, the phone application can activate an alarm system to wake up the patient, call three selected contacts number, and universal emergency number. In case of the user is unresponsive, the app can provide the patient’s location, name, and date of birth to the emergency contacts numbers and universal emergency number. Additionally, the device cost is economically feasible and competitive compared to other medical devices.

A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin.

Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives.

Nocturnal Hypoglycemia Associated with Bedtime Administration of Premixed Insulin Preparations in Hospitalized Patients.

Patients with diabetes mellitus for whom premixed insulin preparations (PMIPs) are ordered in the hospital setting may be at risk of hypoglycemia if the PMIP is incorrectly administered at bedtime (instead of suppertime). The primary objective was to determine, retrospectively, the incidence of bedtime administration of PMIPs at a tertiary teaching hospital. The secondary objective was to investigate whether bedtime administration of PMIPs led to an increase in nocturnal hypoglycemia. Inpatient PMIP orders for the period April 1, 2013, to March 31, 2017, were extracted from the pharmacy information system of the Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia. Two hundred randomly selected inpatient admissions were audited, and instances of PMIP administration after 2000 (8 pm) were recorded. Data from an additional random sample of inpatient admissions, from January 1, 2016, to December 31, 2017, were reviewed to determine whether bedtime administration of PMIPs was associated with increased incidence of nocturnal hypoglycemia, relative to suppertime administration. In the randomly selected sample of 200 inpatient admissions, a PMIP was administered at bedtime at least once during 47 admissions (24%). In the additional sample of 123 inpatient admissions during which a PMIP had been administered, the mean nocturnal hypoglycemia rate was 4.15% for suppertime administration and 14.85% for bedtime administration (p = 0.13). For a substantial proportion of patients, PMIPs were inappropriately ordered and administered at bedtime in this hospital setting and may have been associated with nocturnal hypoglycemic events. Recommendations to reduce this practice include ongoing education and a review of preprinted order sets.

Improved Glycemic Control Observed in Children with Type 1 Diabetes Following the Introduction of Smart Insulin Pens: A Real-World Study.

IntroductionMany challenges are associated with optimizing glycemic control in pediatric patients with type 1 diabetes (T1D); combining data from smart insulin pens and continuous glucose monitoring (CGM) could mitigate some of these obstacles.MethodsThis one-arm, prospective, observational study investigated the effects of introducing a smart pen on glycemic control in pediatric patients with T1D who were using CGM. Children and adolescents with T1D who had been prescribed a smart pen for basal and/or bolus insulin injections were enrolled from three clinics in Sweden. Outcomes compared between baseline and follow-up (≥ 12 months) included: mean numbers of daily (over 24 h) and nocturnal hypoglycemic or hyperglycemic events; time above range (TAR; > 180 mg/dL); time below range (TBR; level 1: 54 to < 70 mg/dL; level 2: < 54 mg/dL); time in range (TIR; 70–180 mg/dL); and missed bolus-dose (MBD) meals.ResultsOverall, 39 patients were included. Mean numbers of daily hypoglycemic events (− 31.4%; p = 0.00035) and nocturnal hypoglycemic events (− 24.4%; p = 0.043) were significantly reduced from baseline to follow-up. Mean daily TBR level 2 was reduced from 2.82% at baseline to 2.18% at follow-up (− 0.64 percentage points; p = 0.025). There were no statistically significant changes in number of daily hyperglycemic events, MBD meals, TIR, TAR, or TBR level 1.ConclusionsIntroducing smart insulin pens was associated with a reduced number of hypoglycemic events and decreases in TBR level 2, demonstrating a potential benefit for glycemic control in pediatric patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13300-021-01177-w.

Efficacy and Safety of Insulin Degludec/Insulin Aspart Compared with a Conventional Premixed Insulin or Basal Insulin: A Meta-Analysis.

Insulin degludec/insulin aspart (IDegAsp) is a novel co-formulation of 70% insulin degludec and 30% insulin aspart. The present meta-analysis was conducted to assess the efficacy and safety of IDegAsp compared with a conventional premixed insulin or basal insulin. We extracted data from citation databases, including PubMed, EMBASE, and the Cochrane Library, since inception to 2021. We calculated the mean differences for hemoglobin A1c (HbA1c), fasting plasma glucose (FPG), self-measured mean glucose, and postprandial glucose (PPG) and odds ratios for confirmed hypoglycemia events. Compared with twice-daily conventional premixed insulin, twice-daily IDegAsp showed a similar effect on changes in HbA1c, but it significantly reduced FPG and self-measured mean glucose levels. Furthermore, compared to once-daily basal insulin, once-daily IDegAsp had a similar effect on changes in HbA1c, but it significantly reduced self-measured mean glucose and PPG levels. The risk of overall confirmed hypoglycemia was similar between treatments; however, the risk of nocturnal hypoglycemia events was significantly lower with IDegAsp than with conventional premixed insulin and basal insulin. Thus, IDegAsp was more effective than conventional premixed insulin and basal insulin at reducing blood glucose with fewer nocturnal hypoglycemia events.

Continuous Glucose Monitoring for the Detection of Hypoglycemia in Patients With Diabetes of the Exocrine Pancreas.

Detailed evaluations of hypoglycemia and associated indices based on continuous glucose monitoring (CGM) are limited in patients with diabetes of the exocrine pancreas. Our study sought to evaluate the frequency and pattern of hypoglycemic events and to investigate hypoglycemia-specific indices in this population. This was a cross-sectional study comprising 83 participants with diabetes of the exocrine pancreas. CGM and self-monitoring of blood glucose (SMBG) were performed on all participants for a minimum period of 72 hours. The frequency and pattern of hypoglycemic events, as well as hypoglycemia-related indices, were evaluated. Hypoglycemia was detected in 90.4% of patients using CGM and 38.5% of patients using SMBG. Nocturnal hypoglycemic events were more frequent (1.9 episodes/patient) and prolonged (142 minutes) compared with day-time events (1.1 episodes/patient; 82.8 minutes, P < 0.05). The mean low blood glucose index was 2.1, and glycemic risk assessment diabetes equation hypoglycemia was 9.1%. The mean time spent below (TSB) <70 mg/dL was 9.2%, and TSB <54 mg/dL was 3.7%. The mean area under curve (AUC) <70 mg/dL was 1.7 ± 2.5 mg/dL/hour and AUC <54 mg/dL was 0.6 ± 1.3 mg/dL/hour. All of the CGM-derived hypoglycemic indices were significantly more deranged at night compared with during the day (P < 0.05). Patients with diabetes of the exocrine pancreas have a high frequency of hypoglycemic episodes that are predominantly nocturnal. CGM is superior to SMBG in the detection of nocturnal and asymptomatic hypoglycemic episodes. CGM-derived hypoglycemic indices are beneficial in estimating the risk of hypoglycemia.

A Novel CGM Metric-Gradient and Combining Mean Sensor Glucose Enable to Improve the Prediction of Nocturnal Hypoglycemic Events in Patients with Diabetes.

Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, and it is often asymptomatic. A novel CGM metric-gradient was proposed in this paper, and a method of combining mean sensor glucose (MSG) and gradient was presented for the prediction of nocturnal hypoglycemia. For this purpose, the data from continuous glucose monitoring (CGM) encompassing 1,921 patients with diabetes were analyzed, and a total of 302 nocturnal hypoglycemic events were recorded. The MSG and gradient values were calculated, respectively, and then combined as a new metric (i.e., MSG+gradient). In addition, the prediction was conducted by four algorithms, namely, logistic regression, support vector machine, random forest, and long short-term memory. The results revealed that the gradient of CGM showed a downward trend before hypoglycemic events happened. Additionally, the results indicated that the specificity and sensitivity based on the proposed method were better than the conventional metrics of low blood glucose index (LBGI), coefficient of variation (CV), mean absolute glucose (MAG), lability index (LI), etc., and the complex metrics of MSG+LBGI, MSG+CV, MSG+MAG, and MSG+LI, etc. Specifically, the specificity and sensitivity were greater than 96.07% and 96.03% at the prediction horizon of 15 minutes and greater than 87.79% and 90.07% at the prediction horizon of 30 minutes when the proposed method was adopted to predict nocturnal hypoglycemic events in the aforementioned four algorithms. Therefore, the proposed method of combining MSG and gradient may enable to improve the prediction of nocturnal hypoglycemic events. Future studies are warranted to confirm the validity of this metric.

Predicting and Preventing Nocturnal Hypoglycemia in Type 1 Diabetes Using Big Data Analytics and Decision Theoretic Analysis.

Background: Despite new glucose sensing technologies, nocturnal hypoglycemia is still a problem for people with type 1 diabetes (T1D) as symptoms and sensor alarms may not be detected while sleeping. Accurately predicting nocturnal hypoglycemia before sleep may help minimize nighttime hypoglycemia. Methods: A support vector regression (SVR) model was trained to predict, before bedtime, the overnight minimum glucose and overnight nocturnal hypoglycemia for people with T1D. The algorithm was trained on continuous glucose measurements and insulin data collected from 124 people (22,804 valid nights of data) with T1D. The minimum glucose threshold for announcing nocturnal hypoglycemia risk was derived by applying a decision theoretic criterion to maximize expected net benefit. Accuracy was evaluated on a validation set from 10 people with T1D during a 4-week trial under free-living sensor-augmented insulin-pump therapy. The primary outcome measures were sensitivity and specificity of prediction, the correlation between predicted and actual minimum nocturnal glucose, and root-mean-square error. The impact of using the algorithm to prevent nocturnal hypoglycemia is shown in-silico. Results: The algorithm predicted 94.1% of nocturnal hypoglycemia events (<3.9 mmol/L, 95% confidence interval [CI], 71.3-99.9) with an area under the receiver operating characteristic curve of 0.86 (95% CI, 0.75-0.98). Correlation between actual and predicted minimum glucose was high (R = 0.71, P < 0.001). In-silico simulations showed that the algorithm could reduce nocturnal hypoglycemia by 77.0% (P = 0.006) without impacting time in target range (3.9-10 mmol/L). Conclusion: An SVR model trained on a big data set and optimized using decision theoretic criterion can accurately predict at bedtime if overnight nocturnal hypoglycemia will occur and may help reduce nocturnal hypoglycemia.

A pragmatic study of mid-mixture insulin and basal insulin treatment in patients with type 2 diabetes uncontrolled with oral antihyperglycaemic medications: A lesson from real-world experience.

BackgroundChinese guidelines for the treatment of type 2 diabetes (T2D) recommend basal or premixed insulins as insulin starters after failed oral antihyperglycaemic medication (OAM). This pragmatic study compared effectiveness and safety of add‐on basal insulin analog (BI) and mid‐mixture insulin analog (MMI; 50:50 premixed insulin) as starter insulin regimens in Chinese patients with T2D in a real‐world setting.Materials and MethodsThis was a multicentre, open‐label, randomized, parallel, pragmatic trial. Patients receiving OAMs were randomized 1:1 to BI (n = 410) or MMI (n = 404) for 24 weeks. Insulin titration and OAM adjustment were determined by investigators following usual standard‐of‐care. The primary outcome was change in glycated haemoglobin (HbA1c) from baseline.ResultsLeast‐squares mean changes in HbA1c from baseline to week 24 were −2.00% and −2.15% for BI and MMI groups, respectively (P = .13). The MMI group demonstrated a greater reduction in concomitant OAM therapies used than BI group (53.8% vs. 35.3%, respectively; P < .001). Very limited daily insulin dose increments were observed from baseline to week 24 in both BI and MMI groups (2.5 U/day and 1.8 U/day, respectively). Although both insulin analogs were well‐tolerated without severe hypoglycaemia, small weight gains were seen with both treatments. Higher total hypoglycaemia rates were noticed with the MMI group, while nocturnal hypoglycaemia events were comparable.ConclusionsIn real‐world settings, BI and MMI provided similar improvement in glucose control without conceding hypoglycaemia. The BI group received a greater number of OAMs in real‐world settings. Limited insulin dose titration was observed, while more adjustments occurred with OAM usage.

A Review of Predictive Low Glucose Suspend and Its Effectiveness in Preventing Nocturnal Hypoglycemia.

To evaluate the effectiveness of predictive low glucose suspend (PLGS) systems within sensor-augmented insulin infusion pumps at preventing nocturnal hypoglycemia in patients with type 1 diabetes (DM1), we performed a systematic review and meta-analysis of randomized crossover trials. Pubmed and Google Scholar were searched for randomized crossover trials, published between January 2013 and July 2018, in nonpregnant outpatients with DM1, which compared event rates during PLGS overnight periods and non-PLGS overnight periods. The primary outcome was the proportion of overnight periods with one or more hypoglycemic measurement. When available, individual patient data were used to assess the effect of clustering measurements within patients. Four studies (272 patients, 10,735 patient-nights: 5422 PLGS and 5313 non-PLGS) were included in the meta-analysis. Two studies reported patient-level data that permitted assessment of the effect of clustering measurements within patients. The effect on the risk difference was minimal. The proportion of overnight periods with one or more episodes of hypoglycemia was 19.6% for the PLGS periods and 27.8% for the non-PLGS periods. Based on the pooled estimate, PLGS overnight periods were associated with an 8.8% lower risk of hypoglycemia (risk difference -0.088; 95% CI -0.119 to -0.056, I2 = 67.4%, τ2 = 0.0006, 4 studies). PLGS systems can reduce nocturnal hypoglycemic events in patients with DM1.

Prevalence of Hypoglycaemia among Insulin-Treated Pregnant Women with Diabetes- who achieved Tight Glycaemic Control

ObjectivesTo determine the prevalence of hypoglycaemia using continuous glucose monitoring system (CGMS) among insulin-treated pregnant women with diabetes whose glycosylated haemoglobin (HbA1c) were <6.0% and identify the risk factors associated with hypoglycaemia occurrence.MethodologyWe conducted a cross-sectional study using 6-days CGMS to detect the prevalence of hypoglycaemia in 31 insulin-treated pregnant women with diabetes who achieved HbA1c <6.0%. Patients were required to log-keep their self-monitoring blood glucose (SMBG) readings and hypoglycaemia events.ResultsEight women experienced confirmed hypoglycaemia with additional seven experienced relative hypoglycaemia, giving rise to prevalence rate of 45.2% (one had both confirmed and relative hypoglycaemia). Nine relative hypoglycaemia and 17 confirmed hypoglycaemic events were recorded. Sixteen (94%) out of 17 confirmed hypoglycaemia events recorded by CGMS were asymptomatic and were missed despite performing regular SMBG. Nocturnal hypoglycaemia events were recorded in seven women. Univariable analysis did not identify any association between conventional risk factors and hypoglycaemia events in our cohort.ConclusionInsulin-treated pregnant women with diabetes who achieved HbA1c <6.0% were associated with high prevalence of hypoglycaemia. Asymptomatic hypoglycaemia is common in our cohort and frequently missed despite regular SMBG. Present study did not identify any association between conventional risk factors and hypoglycaemia events in our cohort.