Literature DB >> 22013887

Does the fat-protein meal increase postprandial glucose level in type 1 diabetes patients on insulin pump: the conclusion of a randomized study.

Ewa Pańkowska1, Marlena Błazik, Lidia Groele.   

Abstract

BACKGROUND: Our study examines the hypothesis that in addition to sugar starch-type diet, a fat-protein meal elevates postprandial glycemia as well, and it should be included in calculated prandial insulin dose accordingly. The goal was to determine the impact of the inclusion of fat-protein nutrients in the general algorithm for the mealtime insulin dose calculator on 6-h postprandial glycemia. SUBJECTS AND METHODS: Of 26 screened type 1 diabetes patients using an insulin pump, 24 were randomly assigned to an experimental Group A and to a control Group B. Group A received dual-wave insulin boluses for their pizza dinner, consisting of 45 g/180 kcal of carbohydrates and 400 kcal from fat-protein where the insulin dose was calculated using the following algorithm: n Carbohydrate Units×ICR+n Fat-Protein Units×ICR/6 h (standard+extended insulin boluses), where ICR represents the insulin-to-carbohydrate ratio. For the control Group B, the algorithm used was n Carbohydrate Units×ICR. The glucose, C-peptide, and glucagon concentrations were evaluated before the meal and at 30, 60, 120, 240, and 360 min postprandial.
RESULTS: There were no statistically significant differences involving patients' metabolic control, C-peptide, glucagon secretion, or duration of diabetes between Group A and B. In Group A the significant glucose increment occurred at 120-360 min, with its maximum at 240 min: 60.2 versus -3.0 mg/dL (P=0.04), respectively. There were no significant differences in glucagon and C-peptide concentrations postprandial.
CONCLUSIONS: A mixed meal effectively elevates postprandial glycemia after 4-6 h. Dual-wave insulin bolus, in which insulin is calculated for both the carbohydrates and fat proteins, is effective in controlling postprandial glycemia.

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Year:  2011        PMID: 22013887     DOI: 10.1089/dia.2011.0083

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  26 in total

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Authors:  Paolo Rossetti; Josep Vehí; Ana Revert; Remei Calm; Jorge Bondia
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Review 2.  Boluses in Insulin Therapy.

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3.  Should the amounts of fat and protein be taken into consideration to calculate the lunch prandial insulin bolus? Results from a randomized crossover trial.

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Journal:  Diabetes Technol Ther       Date:  2012-12-21       Impact factor: 6.118

Review 4.  The current status of bolus calculator decision-support software.

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Journal:  J Diabetes Sci Technol       Date:  2012-09-01

5.  Absorption patterns of meals containing complex carbohydrates in type 1 diabetes.

Authors:  D Elleri; J M Allen; J Harris; K Kumareswaran; M Nodale; L Leelarathna; C L Acerini; A Haidar; M E Wilinska; N Jackson; A M Umpleby; M L Evans; D B Dunger; R Hovorka
Journal:  Diabetologia       Date:  2013-02-23       Impact factor: 10.122

6.  Associations of nutrient intake with glycemic control in youth with type 1 diabetes: differences by insulin regimen.

Authors:  Michelle L Katz; Sanjeev Mehta; Tonja Nansel; Heidi Quinn; Leah M Lipsky; Lori M B Laffel
Journal:  Diabetes Technol Ther       Date:  2014-04-25       Impact factor: 6.118

7.  Factors Beyond Carbohydrate to Consider When Determining Meantime Insulin Doses: Protein, Fat, Timing, and Technology.

Authors:  Alison B Evert
Journal:  Diabetes Spectr       Date:  2020-05

8.  Bolus Calculator Settings in Well-Controlled Prepubertal Children Using Insulin Pumps Are Characterized by Low Insulin to Carbohydrate Ratios and Short Duration of Insulin Action Time.

Authors:  Ragnar Hanas; Peter Adolfsson
Journal:  J Diabetes Sci Technol       Date:  2016-07-29

Review 9.  App-Based Insulin Calculators: Current and Future State.

Authors:  Leslie Eiland; Meghan McLarney; Thiyagarajan Thangavelu; Andjela Drincic
Journal:  Curr Diab Rep       Date:  2018-10-04       Impact factor: 4.810

10.  Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App.

Authors:  Melanie B Gillingham; Zoey Li; Roy W Beck; Peter Calhoun; Jessica Castle; Mark Clements; Eyal Dassau; Francis J Doyle; Robin L Gal; Peter Jacobs; Susana R Patton; Michael R Rickels; Michael Riddell; Corby K Martin
Journal:  Diabetes Technol Ther       Date:  2020-09-29       Impact factor: 6.118

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