Is Carbohydrate Counting Enough? Towards Perfection or Unwanted Complexity?
Barbara Davis Center for Childhood Diabetes, Colorado Children's Hospital, University of Colorado Anschutz Medical Campus , Aurora, Colorado. Diabetes Technology & Therapeutics
(Impact Factor: 2.11).
11/2011; 14(1):3-5. DOI: 10.1089/dia.2011.0234
Available from: Ewa Pańkowska
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ABSTRACT: 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.
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.
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.
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.
Diabetes Technology & Therapeutics 01/2012; 14(1):16-22. DOI:10.1089/dia.2011.0083 · 2.11 Impact Factor
Diabetes Technology & Therapeutics 06/2012; 14 Suppl 1(S1):S75-6. DOI:10.1089/dia.2012.0106 · 2.11 Impact Factor
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Abstract Objective: Controlled inpatient studies on the effects of food, physical activity (PA), and insulin dosing on glucose excursions exist, but such outpatient data are limited. We report here outpatient data on glucose excursions and its key determinants over 5 days in 30 adolescents with type 1 diabetes (T1D) as a proof-of-principle pilot study.
Subjects and methods:
Subjects (20 on insulin pumps, 10 receiving multiple daily injections; 15±2 years old; diabetes duration, 8±4 years; hemoglobin A1c, 8.1±1.0%) wore a continuous glucose monitor (CGM) and an accelerometer for 5 days. Subjects continued their existing insulin regimens, and time-stamped insulin dosing data were obtained from insulin pump downloads or insulin pen digital logs. Time-stamped cell phone photographs of food pre- and post-consumption and food logs were used to augment 24-h dietary recalls for Days 1 and 3. These variables were incorporated into regression models to predict glucose excursions at 1-4 h post-breakfast.
CGM data on both Days 1 and 3 were obtained in 57 of the possible 60 subject-days with an average of 125 daily CGM readings (out of a possible 144). PA and dietary recall data were obtained in 100% and 93% of subjects on Day 1 and 90% and 100% of subjects on Day 3, respectively. All of these variables influenced glucose excursions at 1-4 h after waking, and 56 of the 60 subject-days contributed to the modeling analysis.
Outpatient high-resolution time-stamped data on the main inputs of glucose variability in adolescents with T1D are feasible and can be modeled. Future applications include using these data for in silico modeling and for monitoring outpatient iterations of closed-loop studies, as well as to improve clinical advice regarding insulin dosing to match diet and PA behaviors.
Diabetes Technology & Therapeutics 08/2012; 14(8):658-64. DOI:10.1089/dia.2012.0053 · 2.11 Impact Factor
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