Identification of intraday metabolic profiles during closed-loop glucose control in individuals with type 1 diabetes.

BACKGROUND: Algorithms for closed-loop insulin delivery can be designed and tuned empirically; however, a metabolic model that is predictive of clinical study results can potentially accelerate the process. METHODS: Using data from a previously conducted closed-loop insulin delivery study, existing models of meal carbohydrate appearance, insulin pharmacokinetics, and the effect on glucose metabolism were identified for each of the 10 subjects studied. Insulin's effects to increase glucose uptake and decrease endogenous glucose production were described by the Bergman minimal model, and compartmental models were used to describe the pharmacokinetics of subcutaneous insulin absorption and glucose appearance following meals. The composite model, comprised of only five equations and eight parameters, was identified with and without intraday variance in insulin sensitivity (S(I)), glucose effectiveness at zero insulin (GEZI), and endogenous glucose production (EGP) at zero insulin. RESULTS: Substantial intraday variation in SI, GEZI and EGP was observed in 7 of 10 subjects (root mean square error in model fit greater than 25 mg/dl with fixed parameters and nadir and/or peak glucose levels differing more than 25 mg/dl from model predictions). With intraday variation in these three parameters, plasma glucose and insulin were well fit by the model (R(2) = 0.933 +/- 0.00971 [mean +/- standard error of the mean] ranging from 0.879-0.974 for glucose; R(2) = 0.879 +/- 0.0151, range 0.819-0.972 for insulin). Once subject parameters were identified, the original study could be reconstructed using only the initial glucose value and basal insulin rate at the time closed loop was initiated together with meal carbohydrate information (glucose, R(2) = 0.900 +/- 0.015; insulin delivery, R(2) = 0.640 +/- 0.034; and insulin concentration, R(2) = 0.717 +/- 0.041). CONCLUSION: Metabolic models used in developing and comparing closed-loop insulin delivery algorithms will need to explicitly describe intraday variation in metabolic parameters, but the model itself need not be comprised by a large number of compartments or differential equations.

Use of the DirecNet Applied Treatment Algorithm (DATA) for diabetes management with a real-time continuous glucose monitor (the FreeStyle Navigator).

BACKGROUND: There are no published guidelines for use of real-time continuous glucose monitoring data by a patient; we therefore developed the DirecNet Applied Treatment Algorithm (DATA). The DATA provides algorithms for making diabetes management decisions using glucose values: (i) in real time which include the direction and rate of change of glucose levels, and (ii) retrospectively based on downloaded sensor data. OBJECTIVE: To evaluate the use and effectiveness of the DATA in children with diabetes using a real-time continuous glucose sensor (the FreeStyle Navigator). SUBJECTS: Thirty children and adolescents (mean +/- standard deviation age = 11.2 +/- 4.1 yr) receiving insulin pump therapy. METHODS: Subjects were instructed on use of the DATA and were asked to download their Navigator weekly to review glucose patterns. An Algorithm Satisfaction Questionnaire was completed at 3, 7, and 13 wk. RESULTS: At 13 wk, all of the subjects and all but one parent thought that the DATA gave good, clear directions for insulin dosing, and thought the guidelines improved their postprandial glucose levels. In responding to alarms, 86% of patients used the DATA at least 50% of the time at 3 wk, and 59% reported doing so at 13 wk. Similar results were seen in using the DATA to adjust premeal bolus doses of insulin. CONCLUSIONS: These results show the feasibility of implementing the DATA when real-time continuous glucose monitoring is initiated and support its use in future clinical trials of real-time continuous glucose monitoring.