232 results match your criteria: "Center for Diabetes Technology[Affiliation]"

Diabetes presents a significant challenge to healthcare due to the short- and long-term complications associated with poor blood sugar control. Computer simulation platforms have emerged as promising tools for advancing diabetes therapy by simulating patient responses to treatments in a virtual environment. The University of Virginia Virtual Lab (UVLab) is a new simulation platform engineered to mimic the metabolic behavior of individuals with type 2 diabetes (T2D) using a mathematical model of glucose homeostasis in T2D and a large population of 6062 virtual subjects.

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Using a multistep machine-learning procedure, add virtual continuous glucose monitoring (CGM) traces to the original sparse data of the landmark Diabetes Control and Complications Trial (DCCT). Assess the association of CGM metrics with the microvascular complications of type 1 diabetes observed during the DCCT and establish time-in-range (TIR) as a viable marker of glycemic control. Utilizing the DCCT glycated hemoglobin data obtained every 1 or 3 months plus quarterly 7-point blood glucose (BG) profiles in a multistep procedure: (i) utilized archival BG traces to model interday BG variability and estimate glycated hemoglobin; (ii) trained across the DCCT BG profiles and associated each profile with an archival BG trace; and (iii) used previously identified CGM "motifs" to associate a CGM trace to a BG trace, for each DCCT participant.

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Background: Diabetes ranks among the most common chronic conditions in childhood and adolescence. It is unique among chronic conditions, in that clinical outcomes are intimately tied to how the child or adolescent living with diabetes and their parents or carers react to and implement good clinical practice guidance. It is widely recognized that the individual's perspective about the impact of trying to manage the disease together with the burden of self-management should be addressed to achieve optimal health outcomes.

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Enhancing Type 1 Diabetes Immunological Risk Prediction with Continuous Glucose Monitoring and Genetic Profiling.

Diabetes Technol Ther

December 2024

Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, Virginia, USA.

Early identification of individuals at high risk for type 1 diabetes (T1D) is essential for timely intervention. Islet autoantibodies (AB) and continuous glucose monitoring (CGM) reveal early signs of glycemic dysregulation, while T1D genetic risk scores (GRS) further improve disease prediction. We use CGM data and T1D GRS to develop an AB classifier (1 AB vs.

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Toward Automation: The Road Traveled and Road Ahead for Integrating Automated Insulin Delivery into Inpatient Care.

Diabetes Technol Ther

December 2024

Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

The introduction of automated insulin delivery (AID) systems represents a significant advancement in diabetes care, offering substantial benefits in outpatient settings. Although clinical studies suggest that these systems can also help improve glycemic control in acutely ill patients, several barriers remain for the actual implementation and use of these technologies in clinical practice. Three main contexts for inpatient use are addressed, including: (a) continuation of personal AID systems, (b) initiation of AID during hospitalization, and (c) initiation of AID systems at discharge.

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Article Synopsis
  • A modified Control-IQ system for managing type 1 diabetes was tested in adults, adolescents, children, and preschoolers across a multicenter trial to evaluate the effects of a lower treatment range and a new late bolus feature.
  • The study involved 72 participants who used two different treatment ranges (standard and lower) for two weeks each, with meal challenges performed to assess the new bolus feature.
  • Results showed that using the lower treatment range significantly improved time in range (TIR) and time in tight range (TITR) for managing glucose levels, with no severe complications reported.
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As diabetes technologies continue to advance, their use is expanding beyond type 1 diabetes to include populations with type 2 diabetes, older adults, pregnant individuals, those with psychiatric conditions, and hospitalized patients. This review examines the psychosocial outcomes of these technologies across these diverse groups, with a focus on treatment satisfaction, quality of life, and self-management behaviors. Despite demonstrated benefits in glycemic outcomes, the adoption and sustained use of these technologies face unique challenges in each population.

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With increasing prevalence of obesity and cardiovascular diseases, there is a growing interest in the use of glucagon-like peptide-1 receptor agonists (GLP-1RAs) as an adjunct therapy in type 1 diabetes (T1D). The GLP-1RAs are currently not approved by the US Food and Drug Administration for the treatment of T1D in the absence of randomized controlled trials documenting efficacy and safety of these agents in this population. The Diabetes Technology Society convened a series of three consensus meetings of clinicians and researchers with expertise in diabetes technology, GLP-1RA therapy, and T1D management.

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Automated insulin delivery (AID) is widely available to people with type 1 diabetes (T1D), providing superior glycemic control versus traditional methods. The next generation of AID devices focus on minimizing user/device interactions, especially around meals ("full closed loop," [FCL]). Our goal was to assess the postprandial glycemic impact of the bolus priming system (BPS), an algorithm delivering fixed insulin doses based on the likelihood of a meal having occurred, in conjunction with UVA's latest AID.

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Many people with diabetes in the U.S. will seek or currently hold a license to drive.

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Objective: To examine the efficacy and safety of the tubeless Omnipod 5 automated insulin delivery (AID) system compared with pump therapy with a continuous glucose monitor (CGM) in adults with type 1 diabetes with suboptimal glycemic outcomes.

Research Design And Methods: In this 13-week multicenter, parallel-group, randomized controlled trial performed in the U.S.

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Article Synopsis
  • An error grid is a tool that helps compare glucose levels measured by devices to see if they are correct and to identify any risks.
  • Experts created a new error grid called the DTS Error Grid that works for both blood glucose monitors (BGMs) and continuous glucose monitors (CGMs), organizing accuracy into five risk zones.
  • The results showed that the DTS Error Grid provides a clearer picture of how accurate these devices are and includes a separate matrix to evaluate how well CGMs track glucose trends over time.
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Article Synopsis
  • Diabetes is a widespread chronic condition, and the article reviews recent advancements in non-invasive blood glucose monitoring, focusing specifically on microwave glucose sensors.
  • Microwave sensors stand out due to their quick response times, low user intervention, and potential for continuous monitoring, making them particularly useful for everyday use.
  • The article evaluates the effectiveness of these sensors in terms of accuracy and user comfort, while also discussing the challenges they face and potential future developments, especially in wearable technology.
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The promise of once-weekly insulins for type 1 diabetes: update on progress.

Lancet

September 2024

Division of Endocrinology, University of Virginia, Charlottesville, VA 22903, USA; Center for Diabetes Technology, University of Virginia, Charlottesville, VA 22903, USA. Electronic address:

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Aim: Automated insulin delivery (AID) systems have demonstrated improved glycaemic outcomes in people with type 1 diabetes (T1D), yet limited data exist on these systems in very young children and their impact on caregivers. We evaluated psychosocial outcomes following use of the tubeless Omnipod® 5 AID System in caregivers of very young children.

Materials And Methods: This 3-month single-arm, multicentre, pivotal clinical trial enrolled 80 children aged 2.

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Introduction: Insulin pump therapy can be adversely affected by interruption of insulin flow, leading to a rise in blood glucose (BG) and subsequently of blood beta-hydroxybutyrate (BHB) ketone levels.

Methods: We performed a PubMed search for English language reports (January 1982 to July 2024) estimating the rate of rise in BG and/or BHB after ≥ 60 minutes of interruption of continuous subcutaneous insulin infusion (CSII) in persons with type 1 diabetes (PwT1D). We also simulated the rise in BG in a virtual population of 100 adults with T1D following suspension of continuous subcutaneous insulin infusion.

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Real-Life Use of Automated Insulin Delivery in Individuals With Type 2 Diabetes.

J Diabetes Sci Technol

August 2024

Center for Diabetes Technology, Department of Psychiatry and Neurobehavioral Sciences, School of Medicine, University of Virginia, Charlottesville, VA, USA.

Background: The objective of this work is to document performance of automated insulin delivery (AID) during real-life use in type 2 diabetes (T2D).

Methods: A retrospective analysis was performed of continuous glucose monitoring and insulin delivery data from 796 individuals with T2D, who transitioned from 1-month predictive low-glucose suspend (PLGS) use to 3-month AID use, in real-life settings. Primary outcome was change of time in range (TIR = 70-180 mg/dL) from PLGS to AID.

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Context: Insulin sensitivity (SI) varies with age in Type 1 diabetes (T1D).

Objective: To compare postprandial glucose turnover and insulin sensitivity between adolescents and adults with T1D.

Design: Cross-sectional comparison.

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Diabetes is the leading cause and a common comorbidity of advanced chronic kidney disease. Glycaemic management in this population is challenging and characterised by frequent excursions of hypoglycaemia and hyperglycaemia. Current glucose monitoring tools, such as HbA, fructosamine and glycated albumin, have biases in this population and provide information only on mean glucose exposure.

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Use of diabetes technology in children.

Diabetologia

October 2024

Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.

Children with type 1 diabetes and their caregivers face numerous challenges navigating the unpredictability of this complex disease. Although the burden of managing diabetes remains significant, new technology has eased some of the load and allowed children with type 1 diabetes to achieve tighter glycaemic management without fear of excess hypoglycaemia. Continuous glucose monitor use alone improves outcomes and is considered standard of care for paediatric type 1 diabetes management.

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Article Synopsis
  • The study aimed to assess the long-term effects of using a hybrid closed loop (HCL) system on glucose levels and body mass index (BMI) in children with type 1 diabetes during puberty.* -
  • Over 30 months, data showed that improvements in HbA1c levels were maintained and BMI scores remained stable in participants, with no severe hypoglycemic events and only one unrelated ketoacidosis case.* -
  • Findings suggest that prolonged HCL usage can help manage glucose control during puberty without negatively affecting BMI in children with type 1 diabetes.*
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Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics.

J Clin Endocrinol Metab

December 2024

Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA.

Context: Static measures of continuous glucose monitoring (CGM) data, such as time spent in specific glucose ranges (70-180 mg/dL or 70-140 mg/dL), do not fully capture the dynamic nature of blood glucose, particularly the subtle gradual deterioration of glycemic control over time in individuals with early-stage type 1 diabetes.

Objective: Develop a diabetes diagnostic tool based on 2 markers of CGM dynamics: CGM entropy rate (ER) and Poincaré plot (PP) ellipse area (S).

Methods: A total of 5754 daily CGM profiles from 843 individuals with type 1, type 2 diabetes, or healthy individuals with or without islet autoantibody status were used to compute 2 individual dynamic markers: ER (in bits per transition; BPT) of daily probability matrices describing CGM transitions between 8 glycemic states, and the area S (mg2/dL2) of individual CGM PP ellipses using standard PP descriptors.

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Diabetes is unique among chronic diseases because clinical outcomes are intimately tied to how the person living with diabetes reacts to and implements treatment recommendations. It is further characterised by widespread social stigma, judgement and paternalism. This physical, social and psychological burden collectively influences self-management behaviours.

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The role of the social, physical, and organisational environments in shaping how patients and their caregivers perform work remains largely unexplored in human factors/ergonomics literature. This study recruited 19 dyads consisting of a parent and their child with type 1 diabetes to be interviewed individually and analysed using a macroergonomic framework. Our findings aligned with the macroergonomic factors as presented in previous models, while highlighting the need to expand upon certain components to gain a more comprehensive representation of the patient work system as relevant to dyadic management.

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