28 research outputs found
Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial
The safety and effectiveness of a continuous, day-and-night automated glycaemic control system using insulin and glucagon has not been shown in a free-living, home-use setting. We aimed to assess whether bihormonal bionic pancreas initialised only with body mass can safely reduce mean glycaemia and hypoglycaemia in adults with type 1 diabetes who were living at home and participating in their normal daily routines without restrictions on diet or physical activity
Consensus Recommendations for the Use of Automated Insulin Delivery (AID) Technologies in Clinical Practice
International audienceThe significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past six years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage
Adjunctive Therapies to Optimize Closed-loop Glucose Control
Closed-loop insulin delivery systems are fast becoming the standard of care in the management of type 1 diabetes and have led to significant improvements in diabetes management. Nevertheless, there is still room for improvement for the closed-loop systems to optimize treatment and meet target glycemic control. Adjunct treatments have been introduced as an alternative method to insulin-only treatment methods to overcome diabetes treatment challenges and improve clinical and patient reported outcomes during closed-loop treatment. The adjunct treatment agents mostly consist of medications that are already approved for type 2 diabetes treatment and aim to complete the missing physiologic factors, such as the entero-endocrine system, that regulate glycemia in addition to insulin. This paper will review many of these adjunct therapies, including the basic mechanisms of action, potential benefits, side effects, and the evidence supporting their use during closed-loop treatment
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A Review of Continuous Glucose Monitoring Data Interpretation in the Age of Automated Insulin Delivery.
Using a continuous glucose monitor (CGM) improves glycemic control in patients with type 1 diabetes. The ambulatory glucose profile (AGP) has been recommended as a standard method for reporting CGM data. However, in recently developed automated insulin delivery (AID) systems, a standard format for reporting data has not yet been developed. Instead, reports are specific to each system being used. Currently, the only FDA approved AID system is a hybrid closed-loop insulin pump. In these systems, the patient is still required to announce a meal, respond to alerts, and keep the system in automated insulin delivery. The integrated pump and sensor information provides insights into how the system is performing, and how to make changes to tunable parameters, such as carbohydrate to insulin ratios. The reports also offer a window into human behavior related to performing diabetes tasks, responding to alarms, reasons for exiting HCL, and how glycemic goals are being met. This article reviews the pump and CGM data provided by several of the current closed-loop systems with a focus on systems that are currently approved in the United States (MiniMed™ 670G, Tandem Basal:IQ) and those used by patients using do-it-yourself systems. A step-wise approach to reviewing the nuances of these systems is provided. The comparison may reinforce the importance of the continued need for streamlining a standard report for providers to be able to interpret the CGM data of these systems
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The Minimally-Invasive Oral Glucose Minimal Model: Estimation of Gastric Retention, Glucose Rate of Appearance, and Insulin Sensitivity From Type 1 Diabetes Data Collected in Real-Life Conditions.
OBJECTIVE: Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R[Formula: see text]), and insulin sensitivity (S[Formula: see text]). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM. METHODS: Forty-seven individuals with T1D (weight =78±13 kg, age =42±10 yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. RESULTS: The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R[Formula: see text] model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S[Formula: see text] was satisfactory ( ρ =0.77). CONCLUSION: The MI-OMM is usable to estimate GR, R[Formula: see text], and S[Formula: see text] from data collected in real-life conditions with minimally-invasive technologies. SIGNIFICANCE: Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R[Formula: see text], and S[Formula: see text]. DSS could finally exploit this information to improve diabetes management
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Glycemic Outcomes in Baseline Hemoglobin A1C Subgroups in the International Diabetes Closed-Loop Trial.
Using a closed-loop system significantly improves time in range (TIR) 70-180 mg/dL in patients with type 1 diabetes (T1D). In a 6-month RCT, 112 subjects were randomly assigned to closed-loop control (Tandem Control-IQ) after obtaining 2 weeks of baseline Continuous glucose monitoring (CGM) data from sensor-augmented pump therapy. We compared glycemic outcomes from baseline to end of study among subgroups classified by baseline HbA1c levels. All HbA1c subgroups showed an improvement in TIR due to reduction of both hyperglycemia and hypoglycemia. Those with HbA1c <6.5% improved mostly by reducing nocturnal hypoglycemia due to the automated basal insulin adjustments. Those with HbA1c ≥8.5% improved mostly by reducing daytime and nocturnal hyperglycemia due to both automated basal insulin adjustments and correction boluses during the day. There does not appear to be any reason to exclude individuals with T1D from automated insulin delivery based on their HbA1c. Clinical Trial Identifier: NCT03563313
Effect of Fat Content on Postprandial Gastric Retention and Glucose Absorption in Subjects with Type 1 Diabetes during Daily Life Conditions: Assessment through a Computational Model
The Minimally-Invasive Oral Glucose Minimal Model: Estimation of Gastric Retention, Glucose Rate of Appearance, and Insulin Sensitivity from Type 1 Diabetes Data Collected in Real-life Conditions
Objective: Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R a), and insulin sensitivity (S I). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM. Methods: Forty-seven individuals with T1D (weight =78±13kg, age =42±10yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. Results: The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R a model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S I was satisfactory ( ρ =0.77). Conclusion: The MI-OMM is usable to estimate GR, R a, and S I from data collected in real-life conditions with minimally-invasive technologies. Significance: Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R a, and S I. DSS could finally exploit this information to improve diabetes management
New therapies towards a better glycemic control in youths with type 1 diabetes
Type 1 diabetes (T1D) is the most frequent form of diabetes in pediatric age, affecting more than 1.5 million people younger than age 20 years worldwide. Early and intensive control of diabetes provides continued protection against both microvascular and macrovascular complications, enhances growth, and ensures normal pubertal development. In the absence of definitive reversal therapy for this disease, achieving and maintaining the recommended glycemic targets is crucial. In the last 30 years, enormous progress has been made using technology to better treat T1D. In spite of this progress, the majority of children, adolescents and young adults do not reach the recommended targets for glycemic control and assume a considerable burden each day. The development of promising new therapeutic advances, such as more physiologic insulin analogues, pioneering diabetes technology including continuous glucose monitoring and closed loop systems as well as new adjuvant drugs, anticipate a new paradigm in T1D management over the next few years. This review presents insights into current management of T1D in youths
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New therapies towards a better glycemic control in youths with type 1 diabetes.
Type 1 diabetes (T1D) is the most frequent form of diabetes in pediatric age, affecting more than 1.5 million people younger than age 20 years worldwide. Early and intensive control of diabetes provides continued protection against both microvascular and macrovascular complications, enhances growth, and ensures normal pubertal development. In the absence of definitive reversal therapy for this disease, achieving and maintaining the recommended glycemic targets is crucial. In the last 30 years, enormous progress has been made using technology to better treat T1D. In spite of this progress, the majority of children, adolescents and young adults do not reach the recommended targets for glycemic control and assume a considerable burden each day. The development of promising new therapeutic advances, such as more physiologic insulin analogues, pioneering diabetes technology including continuous glucose monitoring and closed loop systems as well as new adjuvant drugs, anticipate a new paradigm in T1D management over the next few years. This review presents insights into current management of T1D in youths