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Your search keyword '"RANJAN, AJENTHEN"' showing total 195 results
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195 results on '"RANJAN, AJENTHEN"'

2. Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas

Author

Reenberg, Asbjørn Thode, Ritschel, Tobias K. S., Lindkvist, Emilie B., Laugesen, Christian, Svensson, Jannet, Ranjan, Ajenthen G., Nørgaard, Kirsten, and Jørgensen, John Bagterp

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control
Abstract

In this work, we present a switching nonlinear model predictive control (NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use maximum likelihood estimation (MLE) to identify model parameters. A dual-hormone AP consists of a continuous glucose monitor (CGM), a control algorithm, an insulin pump, and a glucagon pump. The AP is designed with a heuristic to switch between insulin and glucagon as well as state-dependent constraints. We extend an existing glucoregulatory model with glucagon and exercise for simulation, and we use a simpler model for control. We test the AP (NMPC and MLE) using in silico numerical simulations on 50 virtual people with type 1 diabetes. The system is identified for each virtual person based on data generated with the simulation model. The simulations show a mean of 89.3% time in range (3.9-10 mmol/L) and no hypoglycemic events., Comment: In submission, 7 pages, 6 figures

Published
2022

3. A one-size-fits-all artificial pancreas for people with type 1 diabetes based on physiological insight and feedback control

Author

Ritschel, Tobias K. S., Reenberg, Asbjørn Thode, Lindkvist, Emilie B., Laugesen, Christian, Svensson, Jannet, Ranjan, Ajenthen G., Nørgaard, Kirsten, Dammann, Bernd, and Jørgensen, John Bagterp

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control, Quantitative Biology - Quantitative Methods
Abstract

We propose a model-free artificial pancreas (AP) for people with type 1 diabetes. The algorithmic parameters are tuned to a virtual population of 1,000,000 individuals, and the AP repeatedly estimates the basal and bolus insulin requirements necessary for maintaining normal blood glucose levels. Therefore, the AP can be used without healthcare personnel or engineers customizing the algorithm to each user. The estimates are based on bodyweight, measurements from a continuous glucose monitor (CGM), and estimates of the meal carbohydrate contents. In a virtual clinical trial with all 1,000,000 individuals (i.e., a Monte Carlo closed-loop simulation), the AP achieves a mean time in range of more than 87% and almost 89% of the participants satisfy several glycemic targets., Comment: In submission, 6 pages, 7 figures, and 3 tables

Published
2022

9. Impact of Missed and Late Meal Boluses on Glycemic Outcomes in Automated Insulin Delivery-Treated Children and Adolescents with Type 1 Diabetes: A Two-Center, Population-Based, Cohort Study

Author

Laugesen, Christian, primary, Ritschel, Tobias, additional, Ranjan, Ajenthen, additional, Hsu, Liana J, additional, Jørgensen, John Bagterp, additional, Svensson, Jannet, additional, Ekhlaspour, Laya, additional, Buckingham, Bruce A, additional, and Norgaard, Kirsten, additional

Published
2024
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15. Effects of a Low-Carbohydrate-High-Protein Pre-Exercise Meal in Type 1 Diabetes—a Randomized Crossover Trial.

Author

Kristensen, Kasper B, Ranjan, Ajenthen G, McCarthy, Olivia M, Holst, Jens J, Bracken, Richard M, Nørgaard, Kirsten, and Schmidt, Signe

Subjects
LOW-carbohydrate diet, TYPE 1 diabetes, INSULIN pumps
Abstract

Context Current guidelines for exercise-related glucose management focus on reducing bolus and/or basal insulin doses and considering carbohydrate intake. Yet far less attention has been paid to the potential role of other macronutrients alongside carbohydrates on glucose dynamics around exercise. Objective To investigate the effects of a low-carbohydrate-high-protein (LCHP) compared with a high-carbohydrate-low-protein (HCLP) pre-exercise meal on the metabolic, hormonal, and physiological responses to exercise in adults with insulin pump–treated type 1 diabetes. Methods Fourteen adults (11 women, 3 men) with insulin pump–treated type 1 diabetes (median [range] HbA1c of 50 [43-59] mmol/mol (6.7% [6.1%-7.5%]), age of 49 [25-65] years, and body mass index of 24.0 [19.3-27.1] kg/m2) completed an unblinded, 2-arm, randomized, crossover study. Participants ingested isocaloric meals that were either LCHP (carbohydrate 21%, protein 52%, fat 27%) or HCLP (carbohydrate 52%, protein 21%, fat 27%) 90 minutes prior to undertaking 45 minutes of cycling at moderate intensity. Meal insulin bolus was dosed according to meal carbohydrate content but reduced by 25%. Basal insulin rates were reduced by 35% from meal ingestion to end of exercise. Results Around exercise the coefficient of variability was lower during LCHP (LCHP: 14.5 ± 5.3 vs HCLP: 24.9 ± 7.7%, P =.001). Over exercise, LCHP was associated with a lesser drop (LCHP: Δ−1.49 ± 1.89 vs HCLP: Δ−3.78 ± 1.95 mmol/L, P =.001). Mean insulin concentration was 30% lower during exercise for LCHP compared with HCLP (LCHP: 25.5 ± 11.0 vs HCLP: 36.5 ± 15.9 mU/L, P <.001). Conclusion Ingesting a LCHP pre-exercise meal lowered plasma glucose variability around exercise and diminished the drop in plasma glucose over exercise. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Glucose Monitoring Metrics in Individuals With Type 1 Diabetes Using Different Treatment Modalities: A Real-World Observational Study.

Author

Nørgaard, Kirsten, Ranjan, Ajenthen G., Laugesen, Christian, Tidemand, Katrine G., Green, Allan, Selmer, Christian, Svensson, Jannet, Andersen, Henrik U., Vistisen, Dorte, and Carstensen, Bendix

Abstract

OBJECTIVE: This study aimed to investigate the association between continuous glucose monitoring (CGM)-derived glycemic metrics and different insulin treatment modalities using real-world data. RESEARCH DESIGN AND METHODS: A cross-sectional study at Steno Diabetes Center Copenhagen, Denmark, included individuals with type 1 diabetes using CGM. Data from September 2021 to August 2022 were analyzed if CGM was used for at least 20% of a 4-week period. Individuals were divided into four groups: multiple daily injection (MDI) therapy, insulin pumps with unintegrated CGM (SUP), sensor-augmented pumps with low glucose management (SAP), and automated insulin delivery (AID). The MDI and SUP groups were further subdivided based on CGM alarm features. The primary outcome was percentage of time in range (TIR: 3.9–10.0 mmol/L) for each treatment group. Secondary outcomes included other glucose metrics and HbA1c. RESULTS: Out of 6,314 attendees, 3,184 CGM users were included in the analysis. Among them, 1,622 used MDI, 504 used SUP, 354 used SAP, and 561 used AID. Median TIR was 54.0% for MDI, 54.9% for SUP, 62,9% for SAP, and 72,1% for AID users. The proportion of individuals achieving all recommended glycemic targets (TIR >70%, time above range <25%, and time below range <4%) was significantly higher in SAP (odds ratio [OR] 2.4 [95% CI 1.6–3.5]) and AID (OR 9.4 [95% CI 6.7–13.0]) compared with MDI without alarm features. CONCLUSIONS: AID appears superior to other insulin treatment modalities with CGM. Although bias may be present because of indications, AID should be considered the preferred choice for insulin pump therapy. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy

Author

Pors, Anders, primary, Rasmussen, Kaspar G., additional, Inglev, Rune, additional, Jendrike, Nina, additional, Philipps, Amalie, additional, Ranjan, Ajenthen G., additional, Vestergaard, Vibe, additional, Henriksen, Jan E., additional, Nørgaard, Kirsten, additional, Freckmann, Guido, additional, Hepp, Karl D., additional, Gerstenberg, Michael C., additional, and Weber, Anders, additional

Published
2023
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24. Performance of a dual-hormone closed-loop system versus insulin-only closed-loop system in adolescents with type 1 diabetes. A single-blind, randomized, controlled, crossover trial

Author

Lindkvist, Emilie Bundgaard, primary, Laugesen, Christian, additional, Reenberg, Asbjørn Thode, additional, Ritschel, Tobias Kasper Skov, additional, Svensson, Jannet, additional, Jørgensen, John Bagterp, additional, Nørgaard, Kirsten, additional, and Ranjan, Ajenthen G., additional

Published
2023
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34. A collaborative comparison of international pediatric diabetes registries

Author

Lanzinger, Stefanie, Zimmermann, Anthony, Ranjan, Ajenthen G., Gani, Osman, Pons Perez, Saira, Akesson, Karin, Majidi, Shideh, Witsch, Michael, Hofer, Sabine, Johnson, Stephanie, Pilgaard, Kasper A., Kummernes, Siv Janne, Robinson, Holly, Eeg‐Olofsson, Katarina, Ebekozien, Osagie, Holl, Reinhard W., Svensson, Jannet, Skrivarhaug, Torild, Warner, Justin, Craig, Maria E., and Maahs, David

Subjects
Adult, Adolescent, Endocrinology, Diabetes and Metabolism, Diabetes mellitus Typ 1, Kind, registry, Netzwerk, quality improvement, Benchmarking, Diabetes mellitus, Diabetes mellitus, Type 1, Pediatrics, Perinatology and Child Health, diabetes mellitus, network, Internal Medicine, Jugend, Humans, Prospective Studies, ddc:610, Registries, benchmarking, Child, DDC 610 / Medicine & health, Aged
Abstract

Background An estimated 1.1 million children and adolescents aged under 20 years have type 1 diabetes worldwide. Principal investigators from seven well‐established longitudinal pediatric diabetes registries and the SWEET initiative have come together to provide an international collaborative perspective and comparison of the registries. Work Flow Information and data including registry characteristics, pediatric participant clinical characteristics, data availability and data completeness from the Australasian Diabetes Data Network (ADDN), Danish Registry of Childhood and Adolescent Diabetes (DanDiabKids), Diabetes prospective follow‐up registry (DPV), Norwegian Childhood Diabetes Registry (NCDR), National Paediatric Diabetes Audit (NPDA), Swedish Childhood Diabetes Registry (Swediabkids), T1D Exchange Quality Improvement Collaborative (T1DX‐QI), and the SWEET initiative was extracted up until 31 December 2020. Registry Objectives and Outcomes The seven diabetes registries and the SWEET initiative collectively show data of more than 900 centers and around 100,000 pediatric patients, the majority with type 1 diabetes. All share the common objectives of monitoring treatment and longitudinal outcomes, promoting quality improvement and equality in diabetes care and enabling clinical research. All generate regular benchmark reports. Main differences were observed in the definition of the pediatric population, the inclusion of adults, documentation of CGM metrics and collection of raw data files as well as linkage to other data sources. The open benchmarking and access to regularly updated data may prove to be the most important contribution from registries. This study describes aspects of the registries to enable future collaborations and to encourage the development of new registries where they do not exist., publishedVersion

Published
2022

37. Hemoglobin A1c and Fructosamine Evaluated in Patients with Type 2 Diabetes Receiving Peritoneal Dialysis Using Long-Term Continuous Glucose Monitoring

Author

Bomholt, Tobias, primary, Feldt-Rasmussen, Bo, additional, Butt, Rizwan, additional, Borg, Rikke, additional, Sarwary, Mir Hassan, additional, Elung-Jensen, Thomas, additional, Almdal, Thomas, additional, Knop, Filip K., additional, Nørgaard, Kirsten, additional, Ranjan, Ajenthen G., additional, Larsson, Anders, additional, Rix, Marianne, additional, and Hornum, Mads, additional

Published
2021
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41. Changes in the lipidome in type 1 diabetes following low carbohydrate diet: Post‐hoc analysis of a randomized crossover trial

Author

Al‐Sari, Naba, primary, Schmidt, Signe, additional, Suvitaival, Tommi, additional, Kim, Min, additional, Trošt, Kajetan, additional, Ranjan, Ajenthen G., additional, Christensen, Merete B., additional, Overgaard, Anne J., additional, Pociot, Flemming, additional, Nørgaard, Kirsten, additional, and Legido‐Quigley, Cristina, additional

Published
2021
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43. The Use of HbA1c, Glycated Albumin and Continuous Glucose Monitoring to Assess Glucose Control in the Chronic Kidney Disease Population Including Dialysis

Author

Bomholt, Tobias, primary, Adrian, Therese, additional, Nørgaard, Kirsten, additional, Ranjan, Ajenthen G., additional, Almdal, Thomas, additional, Larsson, Anders, additional, Vadstrup, Mette, additional, Rix, Marianne, additional, Feldt-Rasmussen, Bo, additional, and Hornum, Mads, additional

Published
2020
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46. The Glycemic Effect of Liraglutide Evaluated by Continuous Glucose Monitoring in Persons with Type 2 Diabetes Receiving Dialysis

Author

Bomholt, Tobias, primary, Idorn, Thomas, additional, Knop, Filip K., additional, Jørgensen, Morten B., additional, Ranjan, Ajenthen G., additional, Resuli, Marsela, additional, Hansen, Pernille M., additional, Borg, Rikke, additional, Persson, Frederik, additional, Feldt-Rasmussen, Bo, additional, and Hornum, Mads, additional

Published
2020
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49. Changes in the lipidome in type 1 diabetes following low carbohydrate diet: a randomized crossover trial

Author

Al-Sari, Naba, primary, Schmidt, Signe, additional, Suvitaival, Tommi, additional, Kim, Min, additional, Trošt, Kajetan, additional, Ranjan, Ajenthen G., additional, Christensen, Merete B., additional, Overgaard, Anne Julie, additional, Pociot, Flemming, additional, Nørgaard, Kirsten, additional, and Legido-Quigley, Cristina, additional

Published
2020
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