Many patients with diabetes struggle with post-meal high blood glucose due to missed or untimely meal-related insulin doses. To address this challenge, our research aims to: (1) study mealtime patterns in patients with type 1 diabetes using wearable insulin pump data, and (2) develop personalized models for predicting future mealtimes to support timely insulin dose administration. Using two independent datasets with over 45,000 meal logs from 82 patients with diabetes, we find that the majority of people ( ∼ 60%) have irregular and inconsistent mealtime patterns that change notably through the course of each day and across months in their own historical data. We also show the feasibility of predicting future mealtimes with personalized LSTM-based models that achieve an average F1 score of > 95% with less than 0.25 false positives per day. Our research lays the groundwork for developing a meal prediction system that can nudge patients with diabetes to administer bolus insulin doses before meal consumption to reduce the occurrence of post-meal high blood glucose.

BACKGROUND: Multiple clinician adjustable parameters impact upon glycemia in people with type 1 diabetes (T1D) using Medtronic Mini Med 780G (MM780G) AHCL. These include glucose targets, carbohydrate ratios (CR), and active insulin time (AIT). Algorithm-based decision support advising upon potential settings adjustments may enhance clinical decision-making.
METHODS: Single-arm, two-phase exploratory study developing decision support to commence and sustain AHCL. Participants commenced investigational MM780G, then 8 weeks Phase 1-initial optimization tool evaluation, involving algorithm-based decision support with weekly AIT and CR recommendations. Clinicians approved or rejected CR and AIT recommendations based on perceived safety per protocol. Co-design resulted in a refined algorithm evaluated in a further identically configured Phase 2. Phase 2 participants also transitioned to commercial MM780G following \"Quick Start\" (algorithm-derived tool determining initial AHCL settings using daily insulin dose and weight). We assessed efficacy, safety, and acceptability of decision support using glycemic metrics, and the proportion of accepted CR and AIT settings per phase.
RESULTS: Fifty three participants commenced Phase 1 (mean age 24.4; Hba1c 61.5mmol/7.7%). The proportion of CR and AIT accepted by clinicians increased between Phases 1 and 2 respectively: CR 89.2% vs. 98.6%, p < 0.01; AIT 95.2% vs. 99.3%, p < 0.01. Between Phases, mean glucose percentage time < 3.9mmol (< 70mg/dl) reduced (2.1% vs. 1.4%, p = 0.04); change in mean TIR 3.9-10mmol/L (70-180mg/dl) was not statistically significant: 72.9% ± 7.8 and 73.5% ± 8.6. Quick start resulted in stable TIR, and glycemic metrics compared to international guidelines.
CONCLUSIONS: The co-designed decision support tools were able to deliver safe and effective therapy. They can potentially reduce the burden of diabetes management related decision making for both health care practitioners and patients.
BACKGROUND: Prospectively registered with Australia/New Zealand Clinical Trials Registry(ANZCTR) on 30th March 2021 as study ACTRN12621000360819.

We evaluated the effectiveness of the predictive low-glucose suspend (PLGS) algorithm in the DIA:CONN G8. Forty people with type 1 diabetes mellitus (T1DM) who used a DIA:CONN G8 for at least 2 months with prior experience using pumps without and with PLGS were retrospectively analyzed. The objective was to assess the changes in time spent in hypoglycemia (percent of time below range [%TBR]) before and after using PLGS. The mean age, sensor glucose levels, glucose threshold for suspension, and suspension time were 31.1±22.8 years, 159.7±23.2 mg/dL, 81.1±9.1 mg/dL, and 111.9±79.8 min/day, respectively. Overnight %TBR <70 mg/dL was significantly reduced after using the algorithm (differences=0.3%, from 1.4%±1.5% to 1.1%±1.2%, P=0.045). The glycemia risk index (GRI) improved significantly by 4.2 (from 38.8±20.9 to 34.6±19.0, P=0.002). Using the PLGS did not result in a change in the hyperglycemia metric (all P>0.05). Our findings support the PLGS in DIA:CONN G8 as an effective algorithm to improve night-time hypoglycemia and GRI in people with T1DM.

People living with diabetes mellitus can be supported in the daily management by diabetes technology with automated insulin delivery (AID) systems to reduce the risk of hypoglycemia and improve glycemic control as well as the quality of life. Due to barriers in the availability of AID-systems, the use and development of open-source AID-systems have internationally increased. This technology provides a necessary alternative to commercially available products, especially when approved systems are inaccessible or insufficiently adapted to the specific needs of the users. Open-source technology is characterized by worldwide free availability of codes on the internet, is not officially approved and therefore the use is on the individual\'s own responsibility. In the clinical practice a lack of expertise with open-source AID technology and concerns about legal consequences, lead to conflict situations for health-care professionals (HCP), sometimes resulting in the refusal of care of people living with diabetes mellitus. This position paper provides an overview of the available evidence and practical guidance for HCP to minimize uncertainties and barriers. People living with diabetes mellitus must continue to be supported in education and diabetes management, independent of the chosen diabetes technology including open-source technology. Check-ups of the metabolic control, acute and chronic complications and screening for diabetes-related diseases are necessary and should be regularly carried out, regardless of the chosen AID-system and by a multidisciplinary team with appropriate expertise.
UNASSIGNED: Menschen mit Diabetes mellitus können im alltäglichen Management durch Diabetestechnologie mittels automatisierter Insulinabgabesysteme (AID-Systeme) unterstützt werden und dadurch das Hypoglykämierisiko reduzieren und die glykämische Kontrolle sowie die Lebensqualität verbessern. Aufgrund von unterschiedlichsten Barrieren in der AID-Verfügbarkeit hat sich international die Nutzung von Open-source-AID-Systemen entwickelt. Diese Technologien bieten eine notwendige Alternative zu kommerziellen Produkten, insbesondere, wenn zugelassene Systeme unzugänglich oder unzureichend auf die spezifischen Bedürfnisse der Anwendenden angepasst sind. Open-source-Technologie zeichnet sich durch global freie Verfügbarkeit von Codes im Internet aus, durchläuft kein offizielles Zulassungsverfahren, und die Verwendung erfolgt daher auf eigene Verantwortung. In der klinischen Praxis führen fehlende Expertise zu den unterschiedlichen Systemen und Bedenken vor juristischen Konsequenzen zu Konfliktsituationen für Behandler:innen und mitunter zur Ablehnung in der Betreuung von Menschen mit Diabetes mellitus, die Open-source-Technologie nutzen möchten. Im vorliegenden Positionspapier sollen eine Übersicht zu vorhandener Evidenz sowie praktische Orientierungshilfen für medizinisches Fachpersonal geboten werden, um Unsicherheiten und Barrieren zu minimieren. Menschen mit Diabetes mellitus müssen – unabhängig von der von ihnen gewählten Diabetestechnologie – weiterhin in Schulung, Umgang und Management ihrer Erkrankung unterstützt werden, auch wenn sie sich für die Verwendung eines Open-source-Systems entschieden haben. Medizinische Kontrollen der metabolischen Einstellung, akuter und chronischer Komplikationen sowie das Screening auf assoziierte Erkrankungen sind unabhängig vom gewählten AID-System notwendig und sollen durch multidisziplinäre Teams mit entsprechender Expertise erfolgen.

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The increased utilization of continuous glucose monitors (CGM) and smart insulin pens (SIP) among people with type 2 diabetes generates significant health data. This study explored possible patterns in long term CGM and SIP data.

UNASSIGNED: Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations.
UNASSIGNED: The app\'s functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models\' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226).
UNASSIGNED: On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively.
UNASSIGNED: The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.

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

OBJECTIVE: This study investigated the onset and the choice of treatment in children with very early onset of type 1 diabetes mellitus (T1D).
METHODS: The study included 5,763 patients from the German Diabetes Patient Follow-up registry with onset of T1D in the first 4 years of life from January 2010 - June 2022. The analysis included diabetes-specific parameters, anthropometric data, and mode of treatment at onset, within the first and second year of T1D. Three groups were compared according to age at onset (G1: 223 patients 6-<12 months, G2: 1519 patients 12-<24 months, G3: 4001 patients 24-48 months).
RESULTS: In 12.3% of all cases in childhood and adolescence, the incidence of diabetes in the first 4 years of life was rare. At the onset, clinical status was worse and diabetic ketoacidosis (DKA) rates were higher in G1 and G2 (52.3% and 46.5%, respectively) compared to G3 (27.3% (p<0.001)). G1 and G2 were significantly more likely to be treated with insulin pump therapy (CSII) 2 years after onset (98.1% and 94.1%, respectively)) compared to G3 (85.8%, p<0.001). Median HbA1c after 2 years did not differ between groups (G1: 7.27% (56.0 mmol/mol), G2: 7.34% (56.7 mmol/mol) and G3: 7.27% (56.0 mmol/mol)) or when comparing CSII vs MDI. The rate of severe hypoglycemia (SH) and DKA during the first 2 years of treatment did not differ among the three groups, ranging from 1.83-2.63/100 patient-years (PY) for DKA and 9.37-24.2/100 PY for SH. Children with T1D under 4 years of age are more likely to be diagnosed with celiac disease but less likely to have thyroiditis than older children with T1DM.
CONCLUSIONS: Young children with T1D had high rates of DKA at onset and were predominantly treated with insulin pump therapy during the first 2 years. The median HbA1c for all three groups was<7.5% (58 mmol/mol) without increased risk of SH or DKA. The use of continuous glucose monitoring (CGM) was not associated with lower HbA1c in children under 48 months.

BACKGROUND: Longer outpatient studies have demonstrated that hybrid closed loop (HCL) use has led to a concomitant reduction in glycated hemoglobin(HbA1c) by 0.3%-0.7%. However, reports have also indicated that HbA1c levels are not declined in the long-term use of HCL. Therefore, we wonder that 3 months use of HCL could improve glycated hemoglobin levels in adolescents and children with T1D.
METHODS: Relevant studies were searched electronically in the Cochrane Library, PubMed, and Embase utilizing the key words \"Pediatrics or Child or Adolescent\", \"Insulin Infusion Systems\" and \"Diabetes Mellitus\" from inception to 17th March 2024 to evaluate the performance of HCL on HbA1c in adolescents, and children with T1D.
RESULTS: Nine studies involving 927 patients were identified. Three months use of HCL show a beneficial effect on HbA1c management (p <0.001) as compared to standard of care in adolescents and children with T1D, without evidence of heterogeneity between articles (I2 = 40%, p = 0.10). HCL did significantly increase the overall average percentage of hypoglycemic time between 70 and 180 mg/dL (TIR) (p <0.001; I2 = 51%). HCL did not show a beneficial effect on hypoglycemic time <70 mg/dL and <54 mg/dL (p >0.05). The overall percentage of hyperglycemic time was significantly decreased in HCL group compared to the control group when it was defined as >180 mg/dL (p <0.001; I2 = 83%), >250 mg/dL (p = 0.007, I2 = 86%) and >300 mg/dL (p = 0.005; I2 = 76%). The mean glucose level was significantly decreased by HCL (p <0.001; I2 = 58%), however, no significant difference was found in coefficient of variation of sensor glucose (p = 0.82; I2 = 71%) and daily insulin dose (p = 0.94; I2 <0.001) between the HCL group and the control group.
CONCLUSIONS: HCL had a beneficial effect on HbA1c management and TIR without increased hypoglycemic time as compared to standard of care in adolescents and children with T1D when therapy duration of HCL was not less than three months.
UNASSIGNED: CRD42022367493; https://www.crd.york.ac.uk/PROSPERO, Principal investigator: Zhen-feng Zhou, Date of registration: October 30, 2022.