Mealtime Insulin Doses Research Articles

Pramlintide Improved Measures of Glycemic Control and Body Weight in Patients With Type I Diabetes Mellitus Undergoing Continuous Subcutaneous Insulin Infusion Therapy

Objective: To assess the safety and efficacy of the addition of pramlintide to continuous subcutaneous insulin infusion (CSII) therapy in patients with type 1 diabetes mellitus (T1DM). Research Design and Methods: We conducted a post hoc analysis of 2 studies: a 29-week, multicenter, randomized, double-blind, placebo-controlled trial (referred to as RCT) (pramlintide, n = 82; placebo, n = 73) and an open-ended, multicenter, open-label, single-arm, observational study (referred to as clinical practice trial) (n =150), which assessed the addition of pramlintide to CSII therapy in patients with T1DM. Pramlintide was initiated at 15 μg and titrated to 30 or 60 μg with major meals. The mealtime insulin dose was reduced by 30% to 50% at initiation, and then adjusted to optimize glycemic control. Endpoints at 29 weeks (RCT) and 6 months (clinical practice trial) included change in glycated hemoglobin (HbA1c) level, insulin dose, body weight, pre- and postprandial blood glucose level, and tolerability and safety. Results: In both studies, mean baseline age was approximately 42 years, duration of diabetes was 20 to 24 years, and HbA1c level was approximately 8%. Pramlintide reduced blood glucose excursions and improved the percentage of recorded postprandial blood glucose levels < 180 mg/dL. Mean (+ standard deviation) reduction in HbA1c level in the clinical practice trial was −0.3% ± 0.1 % (P < 0.0001), and in the RCT was similar between pramlintide- and placebo-treated patients (−0.4% ± 0.1% and −0.3% ± 0.1%, respectively). Glycemic improvements were accomplished, with reductions in mealtime insulin doses (RCT: pramlintide, −23.8% ± 5.2%; placebo, −3.2% ± 4.1%; P < 0.0005; clinical practice trial: −27.5% ± 2.9%; P < 0.0001) and body weight (RCT: pramlintide, −2.2 kg ± 0.5 kg; placebo, ± 1.4 kg ± 0.3 kg; P < 0.0001; clinical practice trial: −3.2 kg ± 0.4 kg; P < 0.0001). Short-lived nausea, primarily mild to moderate in intensity, was the most common adverse event associated with pramlintide therapy. Severe hypoglycemic events occurred at a rate of 0.56 and 0.34 events per patient-year in pramlintide- and placebo-treated patients, respectively, in the RCT, and at a rate of 0.12 events per patient-year in the clinical practice trial. Conclusion: Addition of pramlintide to CSII therapy was safe and effective in patients with T1DM. Pramlintide should be considered for patients who are not able to optimize glycemic control with CSII therapy alone, particularly those with difficulty controlling postprandial blood glucose levels and/or body weight. Trial registration: www.ClinicalTrials.gov identifiers: NCT00042458, NCT00108004

Better glycemic control and weight loss with the novel long-acting basal insulin LY2605541 compared with insulin glargine in type 1 diabetes: a randomized, crossover study.

OBJECTIVETo compare effects of LY2605541 versus insulin glargine on daily mean blood glucose as part of a basal-bolus regimen for type 1 diabetes.RESEARCH DESIGN AND METHODSIn this randomized, Phase 2, open-label, 2 × 2 crossover study, 137 patients received once-daily basal insulin (LY2605541 or glargine) plus mealtime insulin for 8 weeks, followed by crossover treatment for 8 weeks. Daily mean blood glucose was obtained from 8-point self-monitored blood glucose profiles. The noninferiority margin was 10.8 mg/dL.RESULTSLY2605541 met noninferiority and superiority criteria compared with insulin glargine in daily mean blood glucose (144.2 vs. 151.7 mg/dL, least squares mean difference = −9.9 mg/dL [90% CI −14.6 to −5.2], P < 0.001). Fasting blood glucose variability and A1C were reduced with LY2605541 compared with insulin glargine (both P < 0.001). Mealtime insulin dose decreased with LY2605541 and increased with insulin glargine. Mean weight decreased 1.2 kg with LY2605541 and increased 0.7 kg with insulin glargine (P < 0.001). The total hypoglycemia rate was higher for LY2605541 (P = 0.04) and the nocturnal hypoglycemia rate was lower (P = 0.01), compared with insulin glargine. Adverse events (including severe hypoglycemia) were similar, although more gastrointestinal-related events occurred with LY2605541 (15% vs. 4%, P < 0.001). Mean changes (all within normal range) were higher for alanine aminotransferase, aspartate aminotransferase, triglycerides, and LDL-cholesterol and lower for HDL-cholesterol with LY2605541 compared with insulin glargine (all P < 0.02).CONCLUSIONSIn type 1 diabetes, compared with insulin glargine, LY2605541, a novel, long-acting basal insulin, demonstrated greater improvements in glycemic control, increased total hypoglycemia, and reduced nocturnal hypoglycemia, as well as reduced weight and lowered mealtime insulin doses.

Evaluation of a Novel Continuous Glucose Monitoring-Based Method for Mealtime Insulin Dosing—the iBolus—in Subjects with Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion Therapy: A Randomized Controlled Trial

Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy. An individual patient's model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t(0)) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG(0-5h)) and variability as the individual coefficient of variation (CV) of AUC-PG(0-5h). The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t(0) (PG(t0)), and insulin dose to AUC-PG(0-5h) and its CV was also investigated. AUC-PG(0-5h) was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5±127.5 vs. 689.2±180.7 mg/dL·h) or 100-g (752.1±237.7 vs. 760.0±263.2 mg/dL·h) CHO meals. A multiple regression analysis revealed a significant model only for the tBolus, with PG(t0) being the best predictor of AUC-PG(0-5h) explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7±15.3% vs. 10.1±12.5%) but independent of the factors studied. A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identified and analyzed for further optimization of postprandial glycemic control.

Does the Fat-Protein Meal Increase Postprandial Glucose Level in Type 1 Diabetes Patients on Insulin Pump: The Conclusion of a Randomized Study

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.

Improving the Estimation of Mealtime Insulin Dose in Adults With Type 1 Diabetes

OBJECTIVEAlthough carbohydrate counting is routine practice in type 1 diabetes, hyperglycemic episodes are common. A food insulin index (FII) has been developed and validated for predicting the normal insulin demand generated by mixed meals in healthy adults. We sought to compare a novel algorithm on the basis of the FII for estimating mealtime insulin dose with carbohydrate counting in adults with type 1 diabetes.RESEARCH DESIGN AND METHODSA total of 28 patients using insulin pump therapy consumed two different breakfast meals of equal energy, glycemic index, fiber, and calculated insulin demand (both FII = 60) but approximately twofold difference in carbohydrate content, in random order on three consecutive mornings. On one occasion, a carbohydrate-counting algorithm was applied to meal A (75 g carbohydrate) for determining bolus insulin dose. On the other two occasions, carbohydrate counting (about half the insulin dose as meal A) and the FII algorithm (same dose as meal A) were applied to meal B (41 g carbohydrate). A real-time continuous glucose monitor was used to assess 3-h postprandial glycemia.RESULTSCompared with carbohydrate counting, the FII algorithm significantly decreased glucose incremental area under the curve over 3 h (–52%, P = 0.013) and peak glucose excursion (–41%, P = 0.01) and improved the percentage of time within the normal blood glucose range (4–10 mmol/L) (31%, P = 0.001). There was no significant difference in the occurrence of hypoglycemia.CONCLUSIONSAn insulin algorithm based on physiological insulin demand evoked by foods in healthy subjects may be a useful tool for estimating mealtime insulin dose in patients with type 1 diabetes.

Can children with Type 1 diabetes and their caregivers estimate the carbohydrate content of meals and snacks?

Carbohydrate (CHO) counting allows children with Type 1 diabetes to adjust mealtime insulin dose to carbohydrate intake. Little is known about the ability of children to count CHO and whether a particular method for assessing CHO quantity is better than others. We investigated how accurately children and their caregivers estimate carbohydrate, and whether counting in gram increments improves accuracy compared with CHO portions or exchanges. One hundred and two children and adolescents (age range 8.3-18.1 years) on intensive insulin therapy and 110 caregivers independently estimated the CHO content of 17 standardized meals (containing 8-90 g CHO), using whichever method of carbohydrate quantification they had been taught (gram increments, 10-g portions or 15-g exchanges). Seventy-three per cent (n = 2530) of all estimates were within 10-15 g of actual CHO content. There was no relationship between the mean percentage error and method of carbohydrate counting or glycated haemoglobin (HbA(1c)) (P > 0.05). Mean gram error and meal size were negatively correlated (r = -0.70, P < 0.0001). The longer children had been CHO counting the greater the mean percentage error (r = 0.173, P = 0.014). Core foods in non-standard quantities were most frequently inaccurately estimated, while individually labelled foods were most often accurately estimated. Children with Type 1 diabetes and their caregivers can estimate the carbohydrate content of meals with reasonable accuracy. Teaching CHO counting in gram increments did not improve accuracy compared with CHO portions or exchanges. Large meals tended to be underestimated and snacks overestimated. Repeated age-appropriate education appears necessary to maintain accuracy in carbohydrate estimations.

Can children with type 1 diabetes and their caregivers estimate the carbohydrate content of meals and snacks?

Diabet. Med. 27, 348–353 (2010) Abstract Aims Carbohydrate (CHO) counting allows children with Type 1 diabetes to adjust mealtime insulin dose to carbohydrate intake. Little is known about the ability of children to count CHO and whether a particular method for assessing CHO quantity is better than others. We investigated how accurately children and their caregivers estimate carbohydrate, and whether counting in gram increments improves accuracy compared with CHO portions or exchanges. Methods One hundred and two children and adolescents (age range 8.3–18.1 years) on intensive insulin therapy and 110 caregivers independently estimated the CHO content of 17 standardized meals (containing 8–90 g CHO), using whichever method of carbohydrate quantification they had been taught (gram increments, 10-g portions or 15-g exchanges). Results Seventy-three per cent (n = 2530) of all estimates were within 10–15 g of actual CHO content. There was no relationship between the mean percentage error and method of carbohydrate counting or glycated haemoglobin (HbA1c) (P > 0.05). Mean gram error and meal size were negatively correlated (r = −0.70, P < 0.0001). The longer children had been CHO counting the greater the mean percentage error (r = 0.173, P = 0.014). Core foods in non-standard quantities were most frequently inaccurately estimated, while individually labelled foods were most often accurately estimated. Conclusions Children with Type 1 diabetes and their caregivers can estimate the carbohydrate content of meals with reasonable accuracy. Teaching CHO counting in gram increments did not improve accuracy compared with CHO portions or exchanges. Large meals tended to be underestimated and snacks overestimated. Repeated age-appropriate education appears necessary to maintain accuracy in carbohydrate estimations.

Real-life application and validation of flexible intensive insulin-therapy algorithms in type 1 diabetes patients

Aims Flexible intensive insulin therapy (FIT) has become the reference standard in type 1 diabetes. Besides carbohydrate counting (CHO), it requires the use of algorithms to adjust prandial insulin doses to the number of CHO portions. As recourse to standard algorithms is usual when initiating FIT, the use of personalized algorithms would also allow more precise adjustments to be made. The aim of the present study was to validate personalized prandial algorithms for FIT as proposed by Howorka et al. in 1990. Methods We conducted a 4-month observational study of 35 patients with type 1 diabetes, treated with FIT for at least 6 months, who were already using Howorka's prandial algorithms (meal-related and correctional insulin doses for blood glucose increases induced by CHO). These patients were asked to use a personal digital assistant (PDA) phone with an electronic diary (instead of a paper one) to take advantage of the computerized data-collection system to assess the quality of postprandial metabolic control. Results Whatever the number of CHO portions, mean postprandial blood glucose values remained close to the target of 7.8 mmol/L, and the compensatory algorithm allowed precise correction of preprandial hyperglycaemia. In fact, the algorithms for meal-related and correctional insulin doses at the end of the study did not differ significantly from those initially calculated, but they generally differed from one patient to another. Conclusion In type 1 diabetic patients treated with FIT, the use of individualized parameters permits fast and accurate adjustment of mealtime insulin doses, leading to good control of the postprandial state.

Children and adolescents on intensive insulin therapy maintain postprandial glycaemic control without precise carbohydrate counting

Carbohydrate (CHO) quantification is used to adjust pre-meal insulin in intensive insulin regimens. However, the precision in CHO quantification required to maintain postprandial glycaemic control is unknown. We determined the effect of a +/-10-g variation in CHO amount, with an individually calculated insulin dose for 60 g CHO, on postprandial glycaemic control. Thirty-one children and adolescents (age range 9.5-16.8 years), 17 using continuous subcutaneous insulin infusion (CSII) and 14 using multiple daily injections (MDI), participated. Each subject consumed test lunches of equal macronutrient content, differing only in carbohydrate quantity (50, 60, 70 g CHO), in random order on three consecutive days. For each participant, the insulin dose was the same for each meal, based on their usual insulin : CHO ratio for 60 g CHO. Activity was standardized. Continuous glucose monitoring was used. The CSII and MDI subjects demonstrated no difference in postprandial blood glucose levels (BGLs) for comparable carbohydrate loads (P > 0.05). The 10-g variations in CHO quantity resulted in no differences in BGLs or area under the glucose curves for 2.5 h (P > 0.05). Hypoglycaemic episodes were not significantly different (P = 0.32). The 70-g meal produced higher glucose excursions after 2.5 h, with a maximum difference of 1.9 mmol/l at 3 h (P = 0.01), but the BGLs remained within international postprandial targets. In patients using intensive insulin therapy, an individually calculated insulin dose for 60 g of carbohydrate maintains postprandial BGLs for meals containing between 50 and 70 g of carbohydrate. A single mealtime insulin dose will cover a range in carbohydrate amounts without deterioration in postprandial control.

Hypoglycemia associated with off-label sitagliptin use

To describe a case of hypoglycemia induced by off-label use of sitagliptin in an adult patient with type 2 diabetes. Addition of sitagliptin to metformin, glimepiride, and NovoLog(®) 70/30 Mix induced hypoglycemia in a 55-year-old Caucasian female. Hypoglycemia improved, although still occurred periodically, following sulfonylurea discontinuation and a 28% insulin dose reduction. Hypoglycemic symptoms were absent during a 3-day dechallange but occurred again upon sitagliptin reinitiation. Although the mechanism of action of sitagliptin does not predispose patients to hypoglycemic events, when combined with hypoglycemia-inducing medications, eg, sulfonylureas or insulin, and possibly meglitinides, the incidence likely increases. Caution should be used when combining sitagliptin with either sulfonylureas or insulin, and possibly meglitinides, for the treatment of type 2 diabetes as hypoglycemia may ensue. Hypoglycemia due to off-label combinations with insulin and sitagliptin may be prevented by reducing meal-time insulin doses. Prescribers and patients should vigilantly monitor for hypoglycemic events when using sitagliptin off-label with similar pharmacologic combinations such as meglitinides and other rapid-acting insulin products. Additionally, clinicians may encounter resistance from insurance companies to cover such off-label combinations.

Pramlintide as An Adjunct to Insulin in Patients with Type 2 Diabetes in A Clinical Practice Setting Reduced A1C, Postprandial Glucose Excursions, And Weight

This study was designed to assess the safety and efficacy of pramlintide therapy in patients with type 2 diabetes in a clinical practice setting. In this open-label study, 166 insulin-treated patients with type 2 diabetes added pramlintide therapy (120 microg) during an initiation period in which mealtime insulin was reduced by 30-50%. Insulin doses were subsequently adjusted to optimize glycemic control. Endpoints included safety, as well as change in A1C, postprandial glucose, weight, insulin dose, and patient satisfaction following 6 months of pramlintide treatment. At 6 months, the change in A1C from baseline (8.3%) was -0.56% (P < 0.05; n = 59). Pramlintide treatment significantly reduced mean postprandial glucose excursions (P < 0.05) and weight (-2.8 kg; P < 0.05; n = 125). Glycemic benefits were achieved with lower mealtime insulin doses (-10.3%; P < 0.05; n = 104). Nausea, primarily mild to moderate, was reported by 29.5% of patients (severe nausea in 2.4%). Rates of severe hypoglycemia were low (0.04 events/patient-year). In this uncontrolled, open-label setting, pramlintide initiation while reducing mealtime insulin, followed by insulin dose optimization, resulted in improvements in postprandial glucose excursions and A1C. These improvements in glycemic control were accompanied by weight loss.

Pramlintide acetate in the treatment of Type 2 and Type 1 diabetes mellitus

Maintenance of glucose homeostasis involves the interplay of multiple glucoregulatory factors, including the pancreatic β-cell hormones insulin and amylin. Amylin, a neuroendocrine hormone, is secreted in response to nutrient intake and suppresses postprandial glucagon secretion, helps regulate gastric emptying, and reduces food intake. Patients with diabetes are deficient in both insulin and amylin – contributing to postprandial hyperglycemia. Pramlintide, an analogue of amylin, is the active ingredient in the SYMLIN® (pramlintide acetate) injection. SYMLIN is indicated as an adjunctive treatment for insulin-using patients with diabetes and works through mechanisms similar to those of amylin. Clinical trials have demonstrated that pramlintide therapy improves postprandial glucose control, lowers A1C, and is accompanied by a reduction in body weight. Mild-to-moderate nausea and an increased risk of insulin-induced severe hypoglycemia have been the most common side effects. These side effects can be mitigated by gradual pramlintide dose escalation during treatment initiation and proactive mealtime insulin dose reduction upon initiation of pramlintide therapy.