Parallel Control of an artificial pancreas with coordinated insulin, glucagon and rescue carbohydrate control actions

[EN] Background: An artificial pancreas with insulin and glucagon delivery has the potential to reduce the risk of hypo- and hyperglycemia in people with type 1 diabetes. However, a maximum dose of glucagon of 1 mg/d is recommended, potentially still requiring rescue carbohydrates in some situations. This work presents a parallel control structure with intrinsic insulin, glucagon, and rescue carbohydrates coordination to overcome glucagon limitations when needed. Methods: The coordinated controller that combines insulin, glucagon, and rescue carbohydrate suggestions (DH-CC-CHO) was compared with the insulin and glucagon delivery coordinated controller (DH-CC). The impact of carbohydrate quantization for practical delivery was also assessed. An in silico study using the UVA-Padova simulator, extended to include exercise and various sources of variability, was performed. Results: DH-CC and DH-CC-CHO performed similarly with regard to mean glucose (126.25 [123.43; 130.73] vs 127.92 [123.99; 132.97] mg/dL, P = .088), time in range (93.04 [90.00; 95.92] vs 92.91 [90.05; 95.75]%, P = .508), time above 180 mg/dL (4.94 [2.72; 7.53] vs 4.99 [2.93; 7.24]%, P = .966), time below 70 mg/dL (0.61 [0.09; 1.75] vs 0.96 [0.23; 2.17]%, P = .1364), insulin delivery (43.50 [38.68; 51.75] vs 42.86 [38.58; 51.36] U/d, P = .383), and glucagon delivery (0.75 [0.40; 1.83] vs 0.76 [0.43; 0.99] mg/d, P = .407). Time below 54 mg/dL was different (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.16]%, P = .036), although non-clinically significant. This was due to the carbs quantization effect in a specific patient, as no statistical difference was found when carbs were not quantized (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.00]%, P = .265). Conclusions: The new strategy of automatic rescue carbohydrates suggestion in coordination with insulin and glucagon delivery to overcome constraints on daily glucagon delivery was successfully evaluated in an in silico proof of concept.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) through grant number DPI2016-78831-C2-1-R and the European Union through FEDER funds. Vanessa Moscardó was recipient of an FPU grant, FPU13/04253.Moscardo-Garcia, V.; Diez, J.; Bondía Company, J. (2019). Parallel Control of an artificial pancreas with coordinated insulin, glucagon and rescue carbohydrate control actions. Journal of Diabetes Science and Technology. 13(6):1026-1034. https://doi.org/10.1177/1932296819879093S1026103413

Effect of meal composition and alcohol consumption on postprandial glucose concentration in subjects with type 1 diabetes: a randomized crossover trial

[EN] Introduction Meal composition is known to affect glycemic variability and glucose control in type 1 diabetes. The objective of this work was to evaluate the effect of high carbohydrate meals of different nutritional composition and alcohol on the postprandial glucose response in patients with type 1 diabetes. Research design and methods Twelve participants were recruited to this randomized crossover trial. Following a 4-week run-in period, participants received a mixed meal on three occasions with the same carbohydrate content but different macronutrient composition: high protein-high fat with alcohol (0.7g/kg body weight, beer), high protein-high fat without alcohol, and low protein-low fat without alcohol at 2-week intervals. Plasma and interstitial glucose, insulin, glucagon, growth hormone, cortisol, alcohol, free fatty acids, lactate, and pH concentrations were measured during 6 hours. A statistical analysis was then carried out to determine significant differences between studies. Results Significantly higher late postprandial glucose was observed in studies with higher content of fats and proteins (p=0.0088). This was associated with lower time in hypoglycemia as compared with the low protein and fat study (p=0.0179), at least partially due to greater glucagon concentration in the same period (p=0.04). Alcohol significantly increased lactate, decreased pH and growth hormone, and maintained free fatty acids suppressed during the late postprandial phase (p<0.001), without significant changes in plasma glucose. Conclusions Our data suggest that the addition of proteins and fats to carbohydrates increases late postprandial blood glucose. Moreover, alcohol consumption together with a mixed meal has relevant metabolic effects without any increase in the risk of hypoglycemia, at least 6 hours postprandially.This paper presents independent research financially supported by Ministerio de Economia y Competitividad (MINECO) (DPI2016--78831-C2--1--R), Agencia Estatal de Investigacion (PID2019--107722RB--C21/AEI/10.13039/501100011033), and the European Union through FEDER funds.García, A.; Moscardo-Garcia, V.; Ramos-Prol, A.; Diaz, J.; Boronat, M.; Bondía Company, J.; Rossetti, P. (2021). Effect of meal composition and alcohol consumption on postprandial glucose concentration in subjects with type 1 diabetes: a randomized crossover trial. BMJ Open Diabetes Research & Care. 9(1):1-8. https://doi.org/10.1136/bmjdrc-2021-002399189

Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target

This is a copy of an article published in the Diabetes Technology & Therapeutics © 2017 [copyright Mary Ann Liebert, Inc.]; Diabetes Technology & Therapeutics is available online at: https://www.liebertpub.com/.[EN] Background: Postprandial (PP) control remains a challenge for closed-loop (CL) systems. Few studies with inconsistent results have systematically investigated the PP period. Objective: To compare a new CL algorithm with current pump therapy (open loop [OL]) in the PP glucose control in type 1 diabetes (T1D) subjects. Methods: A crossover randomized study was performed in two centers. Twenty T1D subjects (F/M 13/7, age 40.7 -10.4 years, disease duration 22.6 +/- 9.9 years, and A1c 7.8% +/- 0.7%) underwent an 8-h mixed meal test on four occasions. In two (CL1/CL2), after meal announcement, a bolus was given followed by an algorithmdriven basal infusion based on continuous glucose monitoring (CGM). Alternatively, in OL1/OL2 conventional pump therapy was used. Main outcome measures were as follows: glucose variability, estimated with the coefficient of variation (CV) of the area under the curve (AUC) of plasma glucose (PG) and CGM values, and from the analysis of the glucose time series; mean, maximum (C-max), and time to C-max glucose concentrations and time in range (180 mg/dL). Results: CVs of the glucose AUCs were low and similar in all studies (around 10%). However, CL achieved greater reproducibility and better PG control in the PP period: CL1 = CL2 0.05) nor the need for oral glucose was significantly different (CL 40.0% vs. OL 22.5% of meals; P = 0.054). Conclusions: This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov: study number NCT02100488.This work was supported by the Spanish Ministry of Economy and Competitiveness through Grants DPI2013-46982-C2-1-R and DPI2013-46982-C2-2-R, and the EU through FEDER funds. C.Q. is the recipient of a grant from the Hospital Clinic i Universitari of Barcelona ("Ajut a la recerca Josep Font 2014-2017").Rossetti, P.; Quirós, C.; Moscardo-Garcia, V.; Comas, A.; Giménez, M.; Ampudia-Blasco, F.; León, F.... (2017). Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target. Diabetes Technology & Therapeutics. 19(6):355-362. https://doi.org/10.1089/dia.2016.0443S35536219

Revisiting the Relationships Between Measures of Glycemic Control and Hypoglycemia in Continuous Glucose Monitoring Data Sets

[EN] OBJECTIVE The Diabetes Control and Complications Trial (DCCT) identified an inverse relationship between HbA(1c) and severe hypoglycemia. We investigated the relationship between hypoglycemia and HbA(1c) in a large type 1 diabetes cohort on multiple daily injection or insulin pump therapy using blinded continuous glucose monitoring (CGM) data. The impact of real-time CGM on these relationships and how these relationships differ with biochemical definitions of hypoglycemia have also been assessed. RESEARCH DESIGN AND METHODS CGM data were obtained from the JDRF CGM randomized control trial. Baseline blinded CGM data were used to assess time in hypoglycemia in all individuals. End point data fromthe CGMintervention group were used to assess the impact of CGM. Percentage of time spent below 3.9, 3.3, 3.0, and 2.8 mmol/L were calculated and quadratic regression plots drawn. Relationships were analyzed visually, and ANOVA was used to assess relationships between glycemia and time below threshold. RESULTS J-shaped relationships were observed for all biochemical hypoglycemia thresholds, with the lowest hypoglycemia risk occurring at HbA(1c) values between 8.1 and 8.6% (65-70 mmol/mol). The use of an average of 5 days/week of CGM flattened the relationships for 3.3, 3.0, and 2.8mmol/L, and ANOVA confirmed the loss of relationship for the 3.3 mmol/L threshold using CGM. CONCLUSIONS The relationship between hypoglycemia and HbA1c in a population with type 1 diabetes is J-shaped. Lower HbA(1c) values are still associated with increased hypoglycemia risk, although the magnitude of risk depends on biochemical threshold. Realtime CGM may reduce the percentage time spent in hypoglycemia, changing the relationship between HbA(1c) and hypoglycemia.Giménez, M.; Tannen, AJ.; Reddy, M.; Moscardo-Garcia, V.; Conget, I.; Oliver, N. (2018). Revisiting the Relationships Between Measures of Glycemic Control and Hypoglycemia in Continuous Glucose Monitoring Data Sets. Diabetes Care. 41(2):326-332. https://doi.org/10.2337/dc17-1597S32633241

Plasma Insulin Levels and Hypoglycemia Affect Subcutaneous Interstitial Glucose Concentration

[EN] Background: Continuous glucose monitoring (CGM) accuracy during hypoglycemia is suboptimal. This might be partly explained by insulin or hypoglycemia-induced changes in the plasma interstitial subcutaneous (SC) fluid glucose gradient. The aim of the present study was to assess the role of plasma insulin (PI) and hypoglycemia itself in the plasma and interstitial SC fluid glucose concentration in patients with type 1 diabetes mellitus. Methods: Eleven subjects with type 1 diabetes (age 36.59.1 years, HbA(1c) 7.90.4% [62.8 +/- 2.02mmol/mol]; mean +/- standard deviation) were evaluated under hyperinsulinemic euglycemia and hypoglycemia. Each subject underwent two randomized crossover clamps with either a primed 0.3 (low insulin) or 1mU/(kgmin) (high insulin) insulin infusion. The raw CGM signal was normalized with median preclamp values to obtain a standardized measure of the interstitial glucose (IG) concentration before statistical analysis. Results: The mean PI concentration was greater in high insulin studies (HISs) versus low insulin studies (LISs) (412.89 +/- 13.63 vs. 177.22 +/- 10.05pmol/L). During hypoglycemia, glucagon, adrenaline, free fatty acids, glycerol, and beta-OH-butyrate were higher in the LIS (P<0.0001). Likewise, the IG concentration was significantly different (P<0.0001). This was due to lower IG concentration than plasma glucose (PG) concentration during the euglycemic hyperinsulinemic phases in the HIS. In contrast, no difference was observed during hypoglycemia. This was the result of an unchanged PG/IG gradient during the entire LIS, while in the HIS, this gradient increased during the hyperinsulinemic euglycemia phase. Conclusion: Both PI levels and hypoglycemia affect the relationship between IG and PG concentration. ClinicalTrials.gov Identifier: NCT01714895.The research leading to these results received funding from the European Union Seventh Framework Programme (FP7/2007/2013) under the grant agreement 252085 and FEDER funds, as well as the Spanish Ministry of Economy and Competitiveness (MINECO) under the grants DPI2013-46982-C2-1-R and DPI2016-78831-C2-1-R.V.M. is recipient of an FPU grant ref FPU13/04253. We are grateful to Mrs. Sara Correa, Fundacion INCLIVA-Hospital Clinico Universitario de Valencia, and Mrs. Geles Viguer, Hospital Clinico Universitario de Valencia, for their invaluable help in conducting the study.Moscardo-Garcia, V.; Bondía Company, J.; Ampudia-Blasco, FJ.; Fanelli, CG.; Lucidi, P.; Rossetti, P. (2018). Plasma Insulin Levels and Hypoglycemia Affect Subcutaneous Interstitial Glucose Concentration. Diabetes Technology & Therapeutics. 20(4):263-273. https://doi.org/10.1089/dia.2017.0219S26327320