Interstitial fluid glucose dynamics during insulin-induced hypoglycaemia

Steil, Garry M.; Rebrin, Kerstin; Hariri, Farzam; Jinagonda, S.; Tadros, Sameh; Darwin, Christine; Saad, Mohammed F.

doi:10.1007/s00125-005-1852-x

Cited by 142 publications

(142 citation statements)

References 25 publications

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“…This resulted in the algorithm suspending insulin delivery until plasma glucose began to return to target. The ability of the sensor to follow the hypoglycemic excursions during closed-loop insulin delivery, without substantial delay, is consistent with our observations on the kinetics of interstitial fluid glucose during controlled hypoglycemia in normal subjects (29). While there were no cases of severe hypoglycemia during closed-loop control, it is important to note that the subjects were carefully monitored.…”

Section: Discussionsupporting

confidence: 88%

Feasibility of Automating Insulin Delivery for the Treatment of Type 1 Diabetes

et al. 2006

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O ptimal treatment of type 1 diabetes should achieve normoglycemia at all times, without risk of hypoglycemia. Such a treatment should dramatically reduce or prevent diabetes complications and significantly improve patients' quality of life. This goal may be accomplished through pancreatic or islet cell transplantation, but availability of these tissues is limited, survival and function are unpredictable, and longterm immunosuppressive therapy is required (1). The potential for an automated closed-loop system, or artificial ␤-cell, to achieve round-the-clock glycemic control, has not been fully explored.An artificial ␤-cell requires a glucose sensor, an insulindelivery pump, and an algorithm for calculating insulin delivery. Technological and scientific advances have made sensors and pumps available, but linking the two as a "closed loop" has been challenging (2). Lingering questions remain regarding the suitability of different glucosesensing sites (subcutaneous versus intravascular), insulindelivery sites (subcutaneous versus intravascular versus intraperitoneal), and sensor reliability. In addition, no one algorithm has been universally accepted as optimal for insulin delivery (3).Herein, we describe the feasibility of achieving glycemic control in patients with type 1 diabetes using a system comprised of a subcutaneous glucose sensor, an external insulin pump, and an algorithm emulating the ␤-cell's multiphasic glucose-induced insulin release (4 -6). ). Subjects had been treated with continuous subcutaneous insulin infusion (CSII) using Lispro insulin (Lilly, Indianapolis, IN) for at least 6 months before study enrollment and were required to have an HbA 1c Ͻ9%. Data from a previously published study (7) characterizing insulin secretion over a 24-h period in nondiabetic subjects are included for comparison of the glucose profiles (n ϭ 17) obtained with a similar diet. The study was approved by the University of California, Los Angeles Institutional Review Board, and all patients gave written informed consent. RESEARCH DESIGN AND METHODSGlycemic control under CSII therapy was characterized over a 3-day outpatient period using a continuous glucose monitoring system (CGMS) (Medtronic MiniMed, Northridge, CA). The CGMS records sensor current every 5 min and glucose profiles are obtained retrospectively (8). Patients were instructed to keep their daily routine but to take a minimum of seven fingerstick blood glucose readings per day (preprandial and 2-h postprandial and at bedtime) with their home glucose meters. Patients were also instructed to record meal carbohydrate content, physical activity, and any hypoglycemic episodes or supplemental carbohydrate in a logbook.To evaluate the closed-loop insulin delivery system, patients were admitted to the general clinical research center at ϳ5:00 P.M., and their insulin pump was replaced with a Medtronic 511 Paradigm Pump capable of communicating telemetrically with a laptop computer. Two subcutaneous glucose sensors were inserted in the abdominal area and connected to...

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Section: Discussionsupporting

confidence: 88%

Feasibility of Automating Insulin Delivery for the Treatment of Type 1 Diabetes

et al. 2006

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“…This analysis showed an optimal time shift of 12.6 min, consistent with previously published studies using multicompartment diffusion models (13,20,21).…”

Section: Clinical Accuracy Overallsupporting

confidence: 90%

Accuracy of the 5-Day FreeStyle Navigator Continuous Glucose Monitoring System

et al. 2007

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OBJECTIVE -The purpose of this study was to compare the accuracy of measurements of glucose in interstitial fluid made with the FreeStyle Navigator Continuous Glucose Monitoring System with Yellow Springs Instrument laboratory reference measurements of venous blood glucose.RESEARCH DESIGN AND METHODS -Fifty-eight subjects with type 1 diabetes, aged 18 -64 years, were enrolled in a multicenter, prospective, single-arm study. Each subject wore two sensors simultaneously, which were calibrated with capillary fingerstick measurements at 10, 12, 24, and 72 h after insertion. Measurements from the FreeStyle Navigator system were collected at 1-min intervals and compared with venous measurements taken once every 15 min for 50 h over the 5-day period of sensor wear in an in-patient clinical research center. Periods of high rates of change of glucose were induced by insulin and glucose challenges. RESULTS -Comparison of the FreeStyle Navigator measurements with the laboratory ref-erence method (n ϭ 20,362) gave mean and median absolute relative differences (ARDs) of 12.8 and 9.3%, respectively. The percentage in the clinically accurate Clarke error grid A zone was 81.7% and that in the in the benign error B zone was 16.7%. During low rates of change (ϽϮ1 mg ⅐ dl Ϫ1 ⅐ min Ϫ1 ), the percentage in the A zone was higher (84.9%) and the mean and median ARDs were lower (11.7 and 8.5%, respectively).CONCLUSIONS -Measurements with the FreeStyle Navigator system were found to be consistent and accurate compared with venous measurements made using a laboratory reference method over 5 days of sensor wear (82.5% in the A zone on day 1 and 80.9% on day 5).

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“…Presumably, IG fluctuations are related to BG via a diffusion process, which results in a well-defined codependence allowing BG changes to be deduced from IG dynamics. [12][13][14] To account for the gradient between BG and IG, CGM glucose is calibrated using capillary glucose measurements to match CGM glucose and BG levels.…”

Section: Introductionmentioning

confidence: 99%

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Kovatchev

Patek

Ortiz

et al. 2015

Diabetes Technology & Therapeutics

191

154

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Background: The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes. Materials and Methods: A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to ''replay'' real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows. Results: In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels £ 50 mg/dL and BG levels £ 39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD = 10%. Similarly, hyperglycemia (frequency of BG levels ‡ 250 mg/dL and BG levels ‡ 400 mg/dL) increased and displayed an abrupt slope change at MARD = 10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively. Conclusions: Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD = 10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.

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Interstitial fluid glucose dynamics during insulin-induced hypoglycaemia

Cited by 142 publications

References 25 publications

Feasibility of Automating Insulin Delivery for the Treatment of Type 1 Diabetes

Feasibility of Automating Insulin Delivery for the Treatment of Type 1 Diabetes

Accuracy of the 5-Day FreeStyle Navigator Continuous Glucose Monitoring System

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

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