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Gómez-Peralta F, Valledor X, López-Picado A, Abreu C, Fernández-Rubio E, Cotovad L, Pujante P, García-Fernández E, Azriel S, Corcoy R, Pérez-González J, Ruiz-Valdepeñas L. Ultrarapid Insulin Use Can Reduce Postprandial Hyperglycemia and Late Hypoglycemia, Even in Delayed Insulin Injections: A Connected Insulin Cap-Based Real-World Study. Diabetes Technol Ther 2024; 26:1-10. [PMID: 37902762 DOI: 10.1089/dia.2023.0321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
Objectives: Reaching optimal postprandial glucose dynamics is a daily challenge for people with type 1 diabetes (T1D). This study aimed to analyze the postprandial hyperglycemic excursion (PHEs) and late postprandial hypoglycemia (LPH) risk according to prandial insulin time and type. Research Design and Methods: Real-world, retrospective study in T1D using multiple daily injections (MDI) analyzing 5 h of paired continuous glucose monitoring and insulin injections data collected from the connected cap Insulclock®. Meal events were identified using the rate of change detection methodology. Postprandial glucometrics and LPH (glucose <70 mg/dL 2-5 h after a meal) were evaluated according to insulin injection time and rapid (RI) or ultrarapid analog, Fiasp® (URI), use. Results: Meal glycemic excursions (n = 2488), RI: 1211, 48.7%; UR: 1277, 51.3%, in 82 people were analyzed according to injection time around the PHE: -45 to -15 min; -15 to 0 min; and 0 to +45 min. In 63% of the meals, insulin was injected after the PHE started. Lower PHE was observed with URI versus RI (glucose peak-baseline; mg/dL; mean ± standard deviation): 106.7 ± 35.2 versus 111.2 ± 40.3 (P = 0.003), particularly in 0/+45 injections: 111.6 ± 40.2 versus 118.1 ± 43.3; (P = 0.002). One third (29.1%) of participants added a second (correction) injection. The use of URI and avoiding a second injection were independently associated with less LPH risk, even in delayed injections (0/+45), (-36%, odds ratio [OR] 0.641; confidence interval [CI]: 0.462-0.909; P = 0.012) and -56% (OR 0.641; CI: 0.462-0.909 P = 0.038), respectively. Conclusions: URI analog use as prandial insulin reduces postprandial hyper- and hypoglycemia, even in delayed injections.
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Affiliation(s)
| | - Xoan Valledor
- Research and Development Unit, Insulcloud S.L., Madrid, Spain
| | - Amanda López-Picado
- Research and Development Unit, Insulcloud S.L., Madrid, Spain
- Faculty of Health, International University of La Rioja, Logroño, Spain
| | - Cristina Abreu
- Endocrinology and Nutrition Unit, Hospital General de Segovia, Segovia, Spain
| | - Elsa Fernández-Rubio
- Endocrinology and Nutrition Service, Cruces University Hospital, Barakaldo, Spain
| | - Laura Cotovad
- Endocrinology and Nutrition Service, Hospital Arquitecto Marcide, Ferrol (A Coruña), Ferrol, Spain
| | - Pedro Pujante
- Endocrinology and Nutrition Service, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Elena García-Fernández
- Endocrinology and Nutrition Service, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Sharona Azriel
- Endocrinology and Nutrition Service, Hospital Universitario Infanta Sofía, San Sebastián de los Reyes, Spain
| | - Rosa Corcoy
- Institut de Recerca, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain
- CIBER-BBN, Madrid, Spain
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Diaz C. JL, Colmegna P, Breton MD. Maximizing Glycemic Benefits of Using Faster Insulin Formulations in Type 1 Diabetes: In Silico Analysis Under Open- and Closed-Loop Conditions. Diabetes Technol Ther 2023; 25:219-230. [PMID: 36595379 PMCID: PMC10066764 DOI: 10.1089/dia.2022.0468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Background: Ultrarapid-acting insulin analogs that could improve or even prevent postprandial hyperglycemia are now available for both research and clinical care. However, clear glycemic benefits remain elusive, especially when combined with automated insulin delivery (AID) systems. In this work, we study two insulin formulations in silico and highlight adjustments of both open-loop and closed-loop insulin delivery therapies as a critical step to achieve clinically meaningful improvements. Methods: Subcutaneous insulin transport models for two faster analogs, Fiasp (Novo Nordisk, Bagsværd, Denmark) and AT247 (Arecor, Saffron Walden, United Kingdom), were identified using data collected from prior clamp experiments, and integrated into the UVA/Padova type 1 diabetes simulator (adult cohort, N = 100). Pump therapy parameters and the aggressiveness of our full closed-loop algorithm were adapted to the new insulin pharmacokinetic and pharmacodynamic profiles through a sequence of in silico studies. Finally, we assessed these analogs' glycemic impact with and without modified therapy parameters in simulated conditions designed to match clinical trial data. Results: Simply switching to faster insulin analogs shows limited improvements in glycemic outcomes. However, when insulin acceleration is accompanied by therapy adaptation, clinical significance is found comparing time-in-range (70-180 mg/dL) with Aspart versus AT247 in open-loop (+5.1%); and Aspart versus Fiasp (+5.4%) or AT247 (+10.6%) in full closed-loop with no clinically significant differences in the exposure to hypoglycemia. Conclusion: In silico results suggest that properly adjusting intensive insulin therapy profiles, or AID tuning, to faster insulin analogs is necessary to obtain clinically significant improvements in glucose control.
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Affiliation(s)
- Jenny L. Diaz C.
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
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