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  1. Article ; Online: Automated Adaptation of Insulin Treatment in Type 1 Diabetes.

    Fabris, Chiara / Gautier, Thibault / Breton, Marc

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2021  Volume 2021, Page(s) 5039–5042

    Abstract: Individuals with type 1 diabetes (T1D) need life-long insulin therapy to compensate for the lack of endogenous insulin due to the autoimmune damage to pancreatic beta-cells. Treatment is based on basal and bolus insulin, to cover fasting and postprandial ...

    Abstract Individuals with type 1 diabetes (T1D) need life-long insulin therapy to compensate for the lack of endogenous insulin due to the autoimmune damage to pancreatic beta-cells. Treatment is based on basal and bolus insulin, to cover fasting and postprandial periods, respectively, according to three insulin dosing parameters: basal rate (BR), carbohydrate-to-insulin ratio (CR), and correction factor (CF). Suboptimal BR, CR, and CF profiles leading to incorrect insulin dosing may be the cause of undesired glycemic events, which carry dangerous short-term and long-term effects. Therefore, correct tuning of these parameters is of the utmost importance. In this work, we propose a new algorithm to optimize insulin dosing parameters in individuals with T1D who use a continuous glucose monitor and an insulin pump. The algorithm was tested using the University of Virginia/Padova T1D Simulator and led to an improvement in the quality of glycemic control. Future efforts will be devoted to test the algorithm in human clinical trials.
    MeSH term(s) Blood Glucose Self-Monitoring ; Computer Simulation ; Diabetes Mellitus, Type 1/drug therapy ; Humans ; Hypoglycemic Agents/therapeutic use ; Insulin
    Chemical Substances Hypoglycemic Agents ; Insulin
    Language English
    Publishing date 2021-12-10
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC46164.2021.9630191
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: The Differential and Combined Action of Insulin Glargine and Lixisenatide on the Fasting and Postprandial Components of Glucose Control.

    Gautier, Thibault / Umpierrez, Guillermo / Renard, Eric / Kovatchev, Boris

    Journal of diabetes science and technology

    2019  Volume 15, Issue 2, Page(s) 371–376

    Abstract: Background: iGlarLixi is an injectable combination of long acting insulin glargine (iGlar) and glucagon-like peptide 1 receptor agonist lixisenatide in a fixed ratio, which was proven safe and effective for the treatment of type 2 diabetes. Lixisenatide ...

    Abstract Background: iGlarLixi is an injectable combination of long acting insulin glargine (iGlar) and glucagon-like peptide 1 receptor agonist lixisenatide in a fixed ratio, which was proven safe and effective for the treatment of type 2 diabetes. Lixisenatide and iGlar act differently on fasting and postprandial plasma glucose (fasting plasma glucose [FPG] and postprandial glucose [PPG]). Here, we deconstruct quantitatively their respective FPG and PPG effects.
    Method: This post hoc study analyzes data from the Lixilan-O trial, where 1170 subjects with type 2 diabetes were randomly assigned to 30 weeks of once daily injections of lixisenatide, iGlar, and iGlarLixi (1:2:2). The FPG and PPG components of glucose control were assessed in terms of mean glucose (fasting mean plasma glucose [FMPG] and prandial mean plasma glucose [PMPG], respectively). The MPGP was computed across all meals as a delta between post- and premeal glucose; glucose variability was measured by the high blood glucose index (HBGI) (fasting HBGI and prandial HBGI [PHBGI], respectively), and glycemic exposure measured by area under the curve (AUC) computed overall. All metrics were derived from seven-point self-monitoring glucose profiles.
    Results: Insulin glargine lowered significantly FMPG by 15.3 mg/dL (
    Conclusion: Insulin glargine and lixisenatide act selectively on FPG and PPG. Their combination iGlarLixi offers more effective glucose control than its components due to the cumulative effect on FPG and PPG, which is evidenced by reduced average glycemia, glycemic exposure, and glucose variability.
    MeSH term(s) Blood Glucose ; Diabetes Mellitus, Type 2/drug therapy ; Fasting ; Glycated Hemoglobin A/analysis ; Humans ; Hypoglycemic Agents ; Insulin Glargine ; Peptides ; Postprandial Period
    Chemical Substances Blood Glucose ; Glycated Hemoglobin A ; Hypoglycemic Agents ; Peptides ; Insulin Glargine (2ZM8CX04RZ) ; lixisenatide (74O62BB01U)
    Language English
    Publishing date 2019-12-06
    Publishing country United States
    Document type Journal Article ; Randomized Controlled Trial ; Research Support, N.I.H., Extramural
    ISSN 1932-2968
    ISSN (online) 1932-2968
    DOI 10.1177/1932296819891170
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Artificial intelligence and diabetes technology: A review.

    Gautier, Thibault / Ziegler, Leah B / Gerber, Matthew S / Campos-Náñez, Enrique / Patek, Stephen D

    Metabolism: clinical and experimental

    2021  Volume 124, Page(s) 154872

    Abstract: Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability ...

    Abstract Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Blood Glucose Self-Monitoring/instrumentation ; Diabetes Mellitus/blood ; Diabetes Mellitus/diagnosis ; Diabetes Mellitus/drug therapy ; Humans ; Insulin Infusion Systems ; Machine Learning
    Language English
    Publishing date 2021-09-01
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 80230-x
    ISSN 1532-8600 ; 0026-0495
    ISSN (online) 1532-8600
    ISSN 0026-0495
    DOI 10.1016/j.metabol.2021.154872
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Modeling the Effect of Subcutaneous Lixisenatide on Glucoregulatory Endocrine Secretions and Gastric Emptying in Type 2 Diabetes to Simulate the Effect of iGlarLixi Administration Timing on Blood Sugar Profiles.

    Gautier, Thibault / Silwal, Rupesh / Saremi, Aramesh / Boss, Anders / Breton, Marc D

    Journal of diabetes science and technology

    2021  Volume 16, Issue 2, Page(s) 428–433

    Abstract: Background: As type 2 diabetes (T2D) progresses, intensification to combination therapies, such as iGlarLixi (a fixed-ratio GLP-1 RA and basal insulin combination), may be required. Here a simulation study was used to assess the effect of iGlarLixi ... ...

    Abstract Background: As type 2 diabetes (T2D) progresses, intensification to combination therapies, such as iGlarLixi (a fixed-ratio GLP-1 RA and basal insulin combination), may be required. Here a simulation study was used to assess the effect of iGlarLixi administration timing (am vs pm) on blood sugar profiles.
    Methods: Models of lixisenatide were built with a selection procedure, optimizing measurement fits and model complexity, and were included in a pre-existing T2D simulation platform containing glargine models. With the resulting tool, a simulated trial was conducted with 100 in-silico participants with T2D. Individuals were given iGLarLixi either before breakfast or before an evening meal for 2 weeks and daily glycemic profiles were analyzed. In the model, breakfast was considered the largest meal of the day.
    Results: A similar percentage of time within 24 hours was spent with blood sugar levels between 70 to 180 mg/dL when iGlarLixi was administered pre-breakfast or pre-evening meal (73% vs 71%, respectively). Overall percent of time with blood glucose levels above 180 mg/dL within a 24-hour period was similar when iGlarLixi was administered pre-breakfast or pre-evening meal (26% vs 28%, respectively). Rates of hypoglycemia were low in both regimens, with a blood glucose concentration of below 70 mg/dL only observed for 1% of the 24-hour time period for either timing of administration.
    Conclusions: Good efficacy was observed when iGlarlixi was administered pre-breakfast; however, administration of iGlarlixi pre-evening meal was also deemed to be effective, even though in the model the size of the evening meal was smaller than that of the breakfast.
    MeSH term(s) Blood Glucose ; Diabetes Mellitus, Type 2/drug therapy ; Drug Combinations ; Gastric Emptying ; Glycated Hemoglobin/analysis ; Humans ; Hypoglycemic Agents ; Insulin Glargine ; Peptides
    Chemical Substances Blood Glucose ; Drug Combinations ; Glycated Hemoglobin A ; Hypoglycemic Agents ; Peptides ; Insulin Glargine (2ZM8CX04RZ) ; lixisenatide (74O62BB01U)
    Language English
    Publishing date 2021-05-20
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1932-2968
    ISSN (online) 1932-2968
    DOI 10.1177/19322968211015671
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Replay Simulations with Personalized Metabolic Model for Treatment Design and Evaluation in Type 1 Diabetes.

    Hughes, Jonathan / Gautier, Thibault / Colmegna, Patricio / Fabris, Chiara / Breton, Marc D

    Journal of diabetes science and technology

    2020  Volume 15, Issue 6, Page(s) 1326–1336

    Abstract: Background: The capacity to replay data collected in real life by people with type 1 diabetes mellitus (T1DM) would lead to individualized (vs population) assessment of treatment strategies to control blood glucose and possibly true personalization. ... ...

    Abstract Background: The capacity to replay data collected in real life by people with type 1 diabetes mellitus (T1DM) would lead to individualized (vs population) assessment of treatment strategies to control blood glucose and possibly true personalization. Patek et al introduced such a technique, relying on regularized deconvolution of a population glucose homeostasis model to estimate a residual additive signal and reproduce the experimental data; therefore, allowing the subject-specific replay of
    Methods: A subject-specific model personalization of insulin sensitivity and meal-absorption parameters is performed. The University of Virginia (UVa)/Padova T1DM simulator is used to generate experimental scenarios and test the ability of the methodology to accurately reproduce changes in glucose concentration to alteration in meal and insulin inputs. Method performance is assessed by comparing true (UVa/Padova simulator) and replayed glucose traces, using the mean absolute relative difference (MARD) and the Clarke error grid analysis (CEGA).
    Results: Model personalization led to a 9.08 and 6.07 decrease in MARD over a prior published method of replaying altered insulin scenarios for basal and bolus changes, respectively. Replay simulations achieved high accuracy, with MARD <10% and more than 95% of readings falling in the CEGA A-B zones for a wide range of interventions.
    Conclusions: In silico studies demonstrate that the proposed method for replay simulation is numerically and clinically valid over broad changes in scenario inputs, indicating possible use in treatment optimization.
    MeSH term(s) Algorithms ; Blood Glucose ; Blood Glucose Self-Monitoring ; Computer Simulation ; Diabetes Mellitus, Type 1/drug therapy ; Humans ; Hypoglycemic Agents/therapeutic use ; Insulin/therapeutic use ; Insulin Infusion Systems
    Chemical Substances Blood Glucose ; Hypoglycemic Agents ; Insulin
    Language English
    Publishing date 2020-11-20
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1932-2968
    ISSN (online) 1932-2968
    DOI 10.1177/1932296820973193
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Safety and Efficacy of Initializing the Control-IQ Artificial Pancreas System Based on Total Daily Insulin in Adolescents with Type 1 Diabetes.

    Schoelwer, Melissa J / Robic, Jessica L / Gautier, Thibault / Fabris, Chiara / Carr, Kelly / Clancy-Oliveri, Mary / Brown, Sue A / Anderson, Stacey M / DeBoer, Mark D / Cherñavvsky, Daniel R / Breton, Marc D

    Diabetes technology & therapeutics

    2020  Volume 22, Issue 8, Page(s) 594–601

    Abstract: Objective: ...

    Abstract Objective:
    MeSH term(s) Adolescent ; Blood Glucose ; Blood Glucose Self-Monitoring ; Cross-Over Studies ; Diabetes Mellitus, Type 1/drug therapy ; Humans ; Hypoglycemic Agents/therapeutic use ; Insulin/therapeutic use ; Insulin Infusion Systems ; Pancreas, Artificial
    Chemical Substances Blood Glucose ; Hypoglycemic Agents ; Insulin
    Language English
    Publishing date 2020-03-02
    Publishing country United States
    Document type Journal Article ; Randomized Controlled Trial ; Research Support, Non-U.S. Gov't
    ZDB-ID 1452816-2
    ISSN 1557-8593 ; 1520-9156
    ISSN (online) 1557-8593
    ISSN 1520-9156
    DOI 10.1089/dia.2019.0471
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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