Accuracy of Continuous Glucose Monitoring before, during, and after Aerobic and Anaerobic Exercise in Patients with Type 1 Diabetes Mellitus

[EN] Continuous glucose monitoring (CGM) plays an important role in treatment decisions for patients with type 1 diabetes under conventional or closed-loop therapy. Physical activity represents a great challenge for diabetes management as well as for CGM systems. In this work, the accuracy of CGM in the context of exercise is addressed. Six adults performed aerobic and anaerobic exercise sessions and used two Medtronic Paradigm Enlite-2 sensors under closed-loop therapy. CGM readings were compared with plasma glucose during different periods: one hour before exercise, during exercise, and four hours after the end of exercise. In aerobic sessions, the median absolute relative difference (MARD) increased from 9.5% before the beginning of exercise to 16.5% during exercise (p < 0.001), and then decreased to 9.3% in the first hour after the end of exercise (p < 0.001). For the anaerobic sessions, the MARD before exercise was 15.5% and increased without statistical significance to 16.8% during exercise realisation (p = 0.993), and then decreased to 12.7% in the first hour after the cessation of anaerobic activities (p = 0.095). Results indicate that CGM might present lower accuracy during aerobic exercise, but return to regular operation a few hours after exercise cessation. No significant impact for anaerobic exercise was found.This project was partially supported by the Spanish Government through grants DPI2013-46982-C2-1-R, DPI2013-46982-C2-2-R, DPI2016-78831-C2-1-R, and DPI2016-78831-C2-2-R, and by the National Council of Technological and Scientific Development, CNPq Brazil through grants 202050/2015-7 and 207688/2014-1.Biagi, L.; Bertachi, A.; Quirós, C.; Giménez, M.; Conget, I.; Bondía Company, J.; Vehí, J. (2018). Accuracy of Continuous Glucose Monitoring before, during, and after Aerobic and Anaerobic Exercise in Patients with Type 1 Diabetes Mellitus. Biosensors. 8(1):1-8. https://doi.org/10.3390/bios8010022S188

Impaired hypoglycaemia awareness in early pregnancy increases risk of severe hypoglycaemia in the mid-long term postpartum irrespective of breastfeeding status in women with type 1 diabetes

Information regarding the postpartum period in women with type 1 diabetes (T1D) is scarce. We aim to evaluate the relation of impaired hypoglycaemia awareness (IAH) in early pregnancy and breastfeeding status (its presence and duration) with severe postpartum hypoglycaemia (SH).Retrospective cohort study of women with T1D followed during pregnancy between 2012 and 2019. Data on SH were recorded before and during pregnancy. IAH was evaluated at the first antenatal visit. Data on breastfeeding and the long-term postpartum period were collected by questionnaire and from medical records.A total of 89 women with T1D were included with a median follow-up after pregnancy of 19.2 [8.7-30.5] months. Twenty-eight (32%) women had IAH at the first antenatal visit. At discharge, 74 (83%) started breastfeeding during a median of 8 [4.4-15] months. A total of 18 (22%) women experienced ≥1 SH during postpartum. The incidence of SH significantly increased from pregestational to the gestational and post-partum period (0.09, 0.15 and 0.25 episodes/patient-year, respectively). Postpartum SH rates were comparable in breastfeeding and non-breastfeeding women (21.4% vs. 25%, respectively, p>0.05). Clarke test score at the first antenatal visit was associated with postpartum SH (for each 1-point increase: OR 1.53; 95% CI, 1.06-2.21) adjusted for confounders. No other diabetes and pregnancy-related variables were identified as predictors of SH in this period.SH are common in the long-term postpartum period independently of breastfeeding. Assessing IAH in early pregnancy could identify those at an increased risk of SH in the postpartum period.Copyright © 2022 SEEN and SED. Published by Elsevier España, S.L.U. All rights reserved

Extensive assessment of blood glucose monitoring during postprandial period and its impact on closed-loop performance

[EN] Background: Closed-loop (CL) systems aims to outperform usual treatments in blood glucose control and continuous glucose monitors (CGM) are a key component in such systems. Meals represents one of the main disturbances in blood glucose control, and postprandial period (PP) is a challenging situation for both CL system and CGM accuracy. Methods: We performed an extensive analysis of sensor¿s performance by numerical accuracy and precision during PP, as well as its influence in blood glucose control under CL therapy. Results: During PP the mean absolute relative difference (MARD) for both sensors presented lower accuracy in the hypoglycemic range (19.4 ± 12.8%) than in other ranges (12.2 ± 8.6% in euglycemic range and 9.3 ± 9.3% in hyperglycemic range). The overall MARD was 12.1 ± 8.2%. We have also observed lower MARD for rates of change between 0 and 2 mg/dl. In CL therapy, the 10 trials with the best sensor spent less time in hypoglycemia (PG < 70 mg/dl) than the 10 trials with the worst sensors (2 ± 7 minutes vs 32 ± 38 minutes, respectively). Conclusions: In terms of accuracy, our results resemble to previously reported. Furthermore, our results showed that sensors with the lowest MARD spent less time in hypoglycemic range, indicating that the performance of CL algorithm to control PP was related to sensor accuracy.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has been partially supported by the Spanish Government through Grants DPI 2013-46982-C2-1-R, DPI 2016-78831-C2-1-R, DPI 2013-46982-C2-2-R, and DPI 2016-78831-C2-2-R, the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7 and 207688/2014-1.Biagi, L.; Hirata-Bertachi, A.; Conget, I.; Quirós, C.; Giménez, M.; Ampudia-Blasco, F.; Rossetti, P.... (2017). Extensive assessment of blood glucose monitoring during postprandial period and its impact on closed-loop performance. Journal of Diabetes Science and Technology. 11(6):1089-1095. https://doi.org/10.1177/1932296817714272S10891095116Doyle, F. J., Huyett, L. M., Lee, J. B., Zisser, H. C., & Dassau, E. (2014). Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms. Diabetes Care, 37(5), 1191-1197. doi:10.2337/dc13-2108Cengiz, E., & Tamborlane, W. V. (2009). A Tale of Two Compartments: Interstitial Versus Blood Glucose Monitoring. Diabetes Technology & Therapeutics, 11(S1), S-11-S-16. doi:10.1089/dia.2009.0002Cobelli, C., Schiavon, M., Dalla Man, C., Basu, A., & Basu, R. (2016). Interstitial Fluid Glucose Is Not Just a Shifted-in-Time but a Distorted Mirror of Blood Glucose: Insight from an In Silico Study. Diabetes Technology & Therapeutics, 18(8), 505-511. doi:10.1089/dia.2016.0112Castle, J. R., & Ward, W. K. (2010). Amperometric Glucose Sensors: Sources of Error and Potential Benefit of Redundancy. Journal of Diabetes Science and Technology, 4(1), 221-225. doi:10.1177/193229681000400127Basu, A., Dube, S., Veettil, S., Slama, M., Kudva, Y. C., Peyser, T., … Basu, R. (2014). Time Lag of Glucose From Intravascular to Interstitial Compartment in Type 1 Diabetes. Journal of Diabetes Science and Technology, 9(1), 63-68. doi:10.1177/1932296814554797Keenan, D. B., Grosman, B., Clark, H. W., Roy, A., Weinzimer, S. A., Shah, R. V., & Mastrototaro, J. J. (2011). Continuous Glucose Monitoring Considerations for the Development of a Closed-Loop Artificial Pancreas System. Journal of Diabetes Science and Technology, 5(6), 1327-1336. doi:10.1177/193229681100500603Van Bon, A. C., Jonker, L. D., Koebrugge, R., Koops, R., Hoekstra, J. B. L., & DeVries, J. H. (2012). Feasibility of a Bihormonal Closed-Loop System to Control Postexercise and Postprandial Glucose Excursions. Journal of Diabetes Science and Technology, 6(5), 1114-1122. doi:10.1177/193229681200600516Rossetti, P., Quirós, C., Moscardó, V., Comas, A., Giménez, M., Ampudia-Blasco, F. J., … Vehí, J. (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. doi:10.1089/dia.2016.0443Bailey, T., Zisser, H., & Chang, A. (2009). New Features and Performance of a Next-Generation SEVEN-Day Continuous Glucose Monitoring System with Short Lag Time. Diabetes Technology & Therapeutics, 11(12), 749-755. doi:10.1089/dia.2009.0075Zschornack, E., Schmid, C., Pleus, S., Link, M., Klötzer, H.-M., Obermaier, K., … Freckmann, G. (2013). Evaluation of the Performance of a Novel System for Continuous Glucose Monitoring. Journal of Diabetes Science and Technology, 7(4), 815-823. doi:10.1177/193229681300700403Pleus, S., Schmid, C., Link, M., Zschornack, E., Klötzer, H.-M., Haug, C., & Freckmann, G. (2013). Performance Evaluation of a Continuous Glucose Monitoring System under Conditions Similar to Daily Life. Journal of Diabetes Science and Technology, 7(4), 833-841. doi:10.1177/193229681300700405Zisser, H. C., Bailey, T. S., Schwartz, S., Ratner, R. E., & Wise, J. (2009). Accuracy of the SEVEN® Continuous Glucose Monitoring System: Comparison with Frequently Sampled Venous Glucose Measurements. Journal of Diabetes Science and Technology, 3(5), 1146-1154. doi:10.1177/193229680900300519Obermaier, K., Schmelzeisen-Redeker, G., Schoemaker, M., Klötzer, H.-M., Kirchsteiger, H., Eikmeier, H., & del Re, L. (2013). Performance Evaluations of Continuous Glucose Monitoring Systems: Precision Absolute Relative Deviation is Part of the Assessment. Journal of Diabetes Science and Technology, 7(4), 824-832. doi:10.1177/193229681300700404Clarke, W. L., Cox, D., Gonder-Frederick, L. A., Carter, W., & Pohl, S. L. (1987). Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose. Diabetes Care, 10(5), 622-628. doi:10.2337/diacare.10.5.622Martin Bland, J., & Altman, D. (1986). STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT. The Lancet, 327(8476), 307-310. doi:10.1016/s0140-6736(86)90837-8Breton, M., & Kovatchev, B. (2008). Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors. Journal of Diabetes Science and Technology, 2(5), 853-862. doi:10.1177/193229680800200517Kropff, J., Bruttomesso, D., Doll, W., Farret, A., Galasso, S., Luijf, Y. M., … DeVries, J. H. (2014). Accuracy of two continuous glucose monitoring systems: a head‐to‐head comparison under clinical research centre and daily life conditions. Diabetes, Obesity and Metabolism, 17(4), 343-349. doi:10.1111/dom.12378Reddy, M., Herrero, P., Sharkawy, M. E., Pesl, P., Jugnee, N., Pavitt, D., … Oliver, N. S. (2015). Metabolic Control With the Bio-inspired Artificial Pancreas in Adults With Type 1 Diabetes. Journal of Diabetes Science and Technology, 10(2), 405-413. doi:10.1177/1932296815616134Pleus, S., Schoemaker, M., Morgenstern, K., Schmelzeisen-Redeker, G., Haug, C., Link, M., … Freckmann, G. (2015). Rate-of-Change Dependence of the Performance of Two CGM Systems During Induced Glucose Swings. Journal of Diabetes Science and Technology, 9(4), 801-807. doi:10.1177/193229681557871

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

The Antidiabetic Effect of MSCs Is Not Impaired by Insulin Prophylaxis and Is Not Improved by a Second Dose of Cells

Type 1 diabetes mellitus (T1D) is due to autoimmune destruction of pancreatic beta-cells. Previously, we have shown that intravenously administered bone marrow-derived multipotent mesenchymal stromal cells (MSCs) allows pancreatic islet recovery, improves insulin secretion and reverts hyperglycemia in low doses streptozotocin (STZ)-induced diabetic mice. Here we evaluate whether insulin prophylaxis and the administration of a second dose of cells affect the antidiabetic therapeutic effect of MSC transplantation. Insulitis and subsequent elimination of pancreatic beta-cells was promoted in C57BL/6 mice by the injection of 40 mg/kg/day STZ for five days. Twenty-four days later, diabetic mice were distributed into experimental groups according to if they received or not insulin and/or one or two doses of healthy donor-derived MSCs. Three and half months later: glycemia, pancreatic islets number, insulinemia, glycated hemoglobin level and glucose tolerance were determined in animals that did not received exogenous insulin for the last 1.5 months. Also, we characterized MSCs isolated from mice healthy or diabetic. The therapeutic effect of MSC transplantation was observed in diabetic mice that received or not insulin prophylaxis. Improvements were similar irrespective if they received one or two doses of cells. Compared to MSCs from healthy mice, MSCs from diabetic mice had the same proliferation and adipogenic potentials, but were less abundant, with altered immunophenotype and no osteogenic potential

The Impact of Insulin Pump Therapy on Glycemic Profiles in Patients with Type 2 Diabetes: Data from the OpT2mise Study

Background: The OpT2mise randomized trial was designed to compare the effects of continuous subcutaneous insulin infusion (CSII) and multiple daily injections (MDI) on glucose profiles in patients with type 2 diabetes. Research Design and Methods: Patients with glycated hemoglobin (HbA1c) levels of ≥8% (64 mmol/mol) and ≤12% (108 mmol/mol) despite insulin doses of 0.7-1.8 U/kg/day via MDI were randomized to CSII (n=168) or continued MDI (n=163). Changes in glucose profiles were evaluated using continuous glucose monitoring data collected over 6-day periods before and 6 months after randomization. Results: After 6 months, reductions in HbA1c levels were significantly greater with CSII (-1.1±1.2% [-12.0±13.1 mmol/mol]) than with MDI (-0.4±1.1% [-4.4±12.0 mmol/mol]) (P&lt;0.001). Similarly, compared with patients receiving MDI, those receiving CSII showed significantly greater reductions in 24-h mean sensor glucose (SG) (treatment difference, -17.1 mg/dL; P=0.0023), less exposure to SG &gt;180 mg/dL (-12.4%; P=0.0004) and SG &gt;250 mg/dL (-5.5%; P=0.0153), and more time in the SG range of 70-180 mg/dL (12.3%; P=0.0002), with no differences in exposure to SG&lt;70 mg/dL or in glucose variability. Changes in postprandial (4-h) glucose area under the curve &gt;180 mg/dL were significantly greater with CSII than with MDI after breakfast (-775.9±1,441.2 mg/dL/min vs. -160.7±1,074.1 mg/dL/min; P=0.0015) and after dinner (-731.4±1,580.7 mg/dL/min vs. -71.1±1,083.5 mg/dL/min; P=0.0014). Conclusions: In patients with suboptimally controlled type 2 diabetes, CSII significantly improves selected glucometrics, compared with MDI, without increasing the risk of hypoglycemia