Biomedical subjects
Publications and source records attributed to Kerstin Rebrin.
The approach used by Medtronic MiniMed to close the insulin delivery loop using the subcutaneous site for both glucose sensing and insulin delivery relies on modeling insulin action and beta-cell insulin secretion. This approach is contrasted with traditional control systems engineering.
A continuous closed-loop insulin delivery system using subcutaneous insulin delivery was evaluated in eight diabetic canines. Continuous glucose profiles were obtained by extrapolation of blood glucose measurements. Insulin delivery rate was calculated, using a model of beta-cell insulin secretion, and delivered with a Medtronic MiniMed subcutaneous infusion pump. The model acts like a classic proportional-integral-derivative controller, delivering insulin in proportion to glucose above target, history of past glucose values, and glucose rate of change. For each dog, a proportional gain was set relative to the open-loop total daily dose (TDD) of insulin. Additional gains based on 0.5 x TDD and 1.5 x TDD were also evaluated (gain dose response). Control was initiated 4 h before the meal with a target of 6.7 mmol/l. At the time of the meal, glucose was similar for all three gains (6.0 +/- 0.3, 5.2 +/- 0.3, and 4.9 +/- 0.5 mmol/l for 0.5 x TDD, TDD, and 1.5 x TDD, respectively; P > 0.05) with near-target values restored at the end of experiments (8.2 +/- 0.9, 6.0 +/- 0.6, and 6.0 +/- 0.5, respectively). The peak postprandial glucose level decreased significantly with increasing gain (12.1 +/- 0.6, 9.6 +/- 1.0, and 8.5 +/- 0.6 mmol/l, respectively; P < 0.05). The data demonstrate that closed-loop insulin delivery using the subcutaneous site can provide stable glycemic control within a range of gain.
An automated closed-loop insulin delivery system based on subcutaneous glucose sensing and subcutaneous insulin delivery was evaluated in 10 subjects with type 1 diabetes (2 men, 8 women, mean [+/-SD] age 43.4 +/- 11.4 years, duration of diabetes 18.2 +/- 13.5 years). Closed-loop control was assessed over approximately 30 h and compared with open-loop control assessed over 3 days. Closed-loop insulin delivery was calculated using a model of the beta-cell's multiphasic insulin response to glucose. Plasma glucose was 160 +/- 66 mg/dl at the start of closed loop and was thereafter reduced to 71 +/- 19 by 1:00 p.m. (preprandial lunch). Fasting glucose the subsequent morning on closed loop was not different from target (124 +/- 25 vs. 120 mg/dl, respectively; P > 0.05). Mean glucose levels were not different between the open and closed loop (133 +/- 63 vs. 133 +/- 52 mg/dl, respectively; P > 0.65). However, glucose was within the range 70-180 mg/dl 75% of the time under closed loop versus 63% for open loop. Incidence of biochemical hypoglycemia (blood glucose <60 mg/dl) was similar under the two treatments. There were no episodes of severe hypoglycemia. The data provide proof of concept that glycemic control can be achieved by a completely automated external closed-loop insulin delivery system.
Closed-loop insulin delivery in individuals with diabetes can potentially lead to near-normal glucose profiles. To this end, existing subcutaneous glucose sensors and external insulin pumps can be linked with an insulin delivery algorithm to create a completely automated closed-loop system. This paper reviews current research into the development of such a system, with particular emphasis on creating a system emulating the physiological properties of the beta-cell. Issues related to using subcutaneous interstitial fluid for glucose sensing and insulin delivery are reviewed. Criteria for optimising the system are discussed using historical data. Existing strategies for open-loop pump therapy are presented with the objective of defining a path to advance from the existing physician/patient determined insulin therapy to a completely automated system.
Modeling analysis of glucose, insulin, and C-peptide following a meal has been proposed as a means to estimate insulin sensitivity (S(i)) and beta-cell function from a single test. We compared the model-derived meal indexes with analogous indexes obtained from an intravenous glucose tolerance test (IVGTT) and hyperglycemic clamp (HGC) in 17 nondiabetic subjects (14 men, 3 women, aged 50 +/- 2 years [mean +/- SE], BMI 25.0 +/- 0.7 kg/m(2)). S(i) estimated from the meal was correlated with S(i) estimated from the IVGTT and the HGC (r = 0.59 and 0.76, respectively; P < 0.01 for both) but was approximately 2.3 and 1.4 times higher (P < 0.05 for both). The meal-derived estimate of the beta-cell's response to a steady-state change in glucose (static secretion index) was correlated with the HGC second-phase insulin response (r = 0.69; P = 0.002), but the estimated rate-of-change component (dynamic secretion index) was not correlated with first-phase insulin release from either the HGC or IVGTT. Indexes of beta-cell function obtained from the meal were significantly higher than those obtained from the HGC. In conclusion, insulin sensitivity and beta-cell indexes derived from a meal are not analogous to those from the clamp or IVGTT. Further work is needed before these indexes can be routinely used in clinical and epidemiological studies.
Glucose sensing and insulin delivery technology can potentially be linked to form a closed-loop insulin delivery system. Ideally, such a system would establish normal physiologic glucose profiles. To this end, a model of beta-cell secretion can potentially provide insight into the preferred structure of the insulin delivery algorithm. Two secretion models were evaluated for their ability to describe plasma insulin dynamics during hyperglycemic clamps (humans; n=7), and for their ability to establish and maintain fasting euglycemia under conditions simulated by the minimal model. The first beta-cell model (SD) characterized insulin secretion as a static component that had a delayed response to glucose, and a dynamic component that responded to the rate of increase of glucose. The second model (PID) described the response in terms of a proportional component without delay, an integral component that adjusted basal delivery in proportion to hyper/hypoglycemia, and a derivative component that responded to the rate of glucose change. Both models fit the beta-cell response during the clamp, and established fasting euglycemia under simulated closed-loop conditions; however, the SD model did not maintain euglycemia following simulated changes in insulin sensitivity or glucose appearance, whereas the PID model did. The PID model was more stable under the simulated closed-loop conditions. Both the SD and PID models described beta-cell secretion in response to a rapid increase in glucose. However, the PID model could maintain fasting euglycemia and was more stable under closed-loop conditions, and thus is more suited for such conditions.
Continuous glucose monitoring has the potential to improve glucose management and reduce the risk of hypoglycemia in individuals with diabetes. Accurate sensors may also allow the development of a closed-loop insulin delivery system. The purpose of this work was to determine the delay time associated with a subcutaneous glucose sensor during rapidly changing glucose excursions. Subcutaneous glucose sensors (Medtronic MiniMed, Inc., Northridge, CA) were inserted in five healthy men. After a 2-h stabilization period, a 3-h hyperglycemic (approximately 11 mM) clamp was performed followed by a 90-min period in which plasma glucose was allowed to decline to as low as 2.8 mM. Sensors were calibrated using two points (basal and hyperglycemia), and the calibrated sensor glucose measurements were compared with those from a reference analyzer (Beckman Instruments, Fullerton, CA). Response time was estimated from a first-order kinetic model. Plasma glucose levels, determined with the subcutaneous sensor, were highly correlated with those obtained with the reference glucose analyzer (r(2) = 0.91, p < 0.001; mean absolute difference of approximately 8%). The half-time for the sensor response was estimated to be 4.0 +/- 1.0 min. The subcutaneous glucose sensor has the potential to facilitate the detection of hypoglycemia and improve overall glycemic control when used in a real-time monitor. The rapid response should be sufficient to allow a fully automated closed-loop insulin delivery system to be developed based on the subcutaneous sensing site.
In vivo subcutaneous glucose sensor accuracy depends on the calibration method. Sensor accuracy was assessed during standard oral glucose tolerance tests in six non-diabetic subjects each wearing six subcutaneous glucose sensors (Medtronic MiniMed). Paired blood glucose (B(G)) and sensor current readings were used for retrospective sensor calibration using either B(G) or sensor current as the independent variable. Sensor accuracy after calibration was assessed using three criteria: linear regression between B(G) and sensor glucose (S(G)); correlation; and mean absolute difference (MAD), defined as 100 x |B(G) - S(G)|/B(G). Calibration with B(G) as the independent variable resulted in unbiased estimates of regression slope (1.02, not different than 1, p< 0.01) and y-intercept (-1.06 mg/dL, not different than 0, p< 0.01). In contrast, calibration with sensor current as the independent variable resulted in biased estimates of slope (0.76, different than 1, p< 0.01) and y-intercept (31.25 mg/dL, different than 0, p< 0.01). However, with sensor current as the independent variable, the MAD was lower than the corresponding value for calibration with B(G) at the x-axis (15.00 +/- 0.47% vs. 18.35 +/- 0.63%, p< 0.01). The Pearson correlation coefficient between B(G) and S(G) was higher when using sensor current as the independent variable (R = 0.82 vs. R = 0.79 when using glucose on the x-axis). We suggest that despite the fact that calibration with sensor current as the independent variable leads to a bias in the estimate of B(G), it is a more appropriate calibration method when the primary concern is minimization of the MAD between S(G) and B(G).