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Background: This study aimed to evaluate the accuracy of Dexcom G6 (DG6) and FreeStyle Libre-2 (FSL2) during aerobic training and high-intensity interval training (HIIT) in individuals with type 1 diabetes. Methods: Twenty-six males (mean age 29.3 ± 6.3 years and mean duration of diabetes 14.9 ± 6.1 years) participated in this study. Interstitial glucose levels were measured using DG6 and FSL2, while plasma glucose levels were measured every 10 min using YSI 2500 as the reference for glucose measurements in this study. The measurements began 20 min before the start of exercise and continued for 20 min after exercise. Seven measurements were taken for each subject and exercise. Results: Both DG6 and FSL2 devices showed significant differences compared to YSI glucose data for both aerobic and HIIT exercises. Continuous glucose monitoring (CGM) devices exhibited superior performance during HIIT than aerobic training, with DG6 showing a mean absolute relative difference of 14.03% versus 31.98%, respectively. In the comparison between the two devices, FSL2 demonstrated significantly higher effectiveness in aerobic training, yet its performance was inferior to DG6 during HIIT. According to the 40/40 criteria, both sensors performed similarly, with marks over 93% for all ranges and both exercises, and above 99% for HIIT and in the >180 mg/dL range, which is in accordance with FDA guidelines. Conclusions: The findings suggest that the accuracy of DG6 and FSL2 deteriorates during and immediately after exercise but remains acceptable for both devices during HIIT. However, accuracy is compromised with DG6 during aerobic exercise. This study is the first to compare the accuracy of two CGMs, DG6, and FSL2, during two exercise modalities, using plasma glucose YSI measurements as the gold standard for comparisons. It was registered at clinicaltrials.gov (NCT06080542).

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 36.5 ± 9.1 years, HbA 1c 7.9 ± 0.4% [62.8 ± 2.02 mmol/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 1 mU/(kg·min) (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 ± 10.05 pmol/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.

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 algorithm-driven 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 (<70, 70-180, >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 = CL2mean 123 ± 47 and 125 ± 44 vs. 152 ± 53 and 159 ± 54 mg/dL) and C max OL 217.1 ± 67.0 mg/dL versus CL 183.3 ± 63.9 mg/dL, P < 0.0001. Time-in-range was higher with CL versus OL (80% vs. 64%; P < 0.001). Neither the time below 70 mg/dL (CL 6.1% vs. OL 3.2%; P > 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.

Objective: Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy., Subjects and Methods: An individual patient's model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t(0)) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG(0-5h)) and variability as the individual coefficient of variation (CV) of AUC-PG(0-5h). The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t(0) (PG(t0)), and insulin dose to AUC-PG(0-5h) and its CV was also investigated., Results: AUC-PG(0-5h) was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5±127.5 vs. 689.2±180.7 mg/dL·h) or 100-g (752.1±237.7 vs. 760.0±263.2 mg/dL·h) CHO meals. A multiple regression analysis revealed a significant model only for the tBolus, with PG(t0) being the best predictor of AUC-PG(0-5h) explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7±15.3% vs. 10.1±12.5%) but independent of the factors studied., Conclusions: A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identified and analyzed for further optimization of postprandial glycemic control.

Background: Continuous glucose monitoring (CGM) devices estimate plasma glucose (PG) from measurements in compartments alternative to blood. The accuracy of currently available CGM is yet unsatisfactory and may depend on the implemented calibration algorithms, which do not compensate adequately for the differences of glucose dynamics between the compartments. Here we propose and validate an innovative calibration algorithm for the improvement of CGM performance., Methods: CGM data from GlucoDay(®) (A. Menarini, Florence, Italy) and paired reference PG have been obtained from eight subjects without diabetes during eu-, hypo-, and hyperglycemic hyperinsulinemic clamps. A calibration algorithm based on a dynamic global model (GM) of the relationship between PG and CGM in the interstitial space has been obtained. The GM is composed by independent local models (LMs) weighted and added. LMs are defined by a combination of inputs from the CGM and by a validity function, so that each LM represents to a variable extent a different metabolic condition and/or sensor-subject interaction. The inputs best suited for glucose estimation were the sensor current I and glucose estimations Ĝ, at different time instants [I(k), I(k)(-1), Ĝ(k)(-1)] (IIG). In addition to IIG, other inputs have been used to obtain the GM, achieving different configurations of the calibration algorithm., Results: Even in its simplest configuration considering only IIG, the new calibration algorithm improved the accuracy of the estimations compared with the manufacturer's estimate: mean absolute relative difference (MARD)=10.8±1.5% versus 14.7±5.4%, respectively (P=0.012, by analysis of variance). When additional exogenous signals were considered, the MARD improved further (7.8±2.6%, P<0.05)., Conclusions: The LM technique allows for the identification of intercompartmental glucose dynamics. Inclusion of these dynamics into the calibration algorithm improves the accuracy of PG estimations.

Type 2 diabetes is characterized by insulin resistance and progressive β-cell deterioration. As β-cell function declines, most patients with type 2 diabetes treated with oral agents, in monotherapy or combination, will require insulin therapy. Addition of basal insulin (glargine, detemir, or NPH/neutral protamine lispro insulin) to previous treatment is accepted as the simplest way to start insulin therapy in those patients. But even when basal insulin is adequately titrated, some patients will also need prandial insulin to achieve or maintain individual glycemic targets over time. Starting with premixed insulin is an effective option, but it is frequently associated with increased hypoglycemia risk, fixed meal schedules, and weight gain. As an alternative, a novel approached known as "basal plus strategy" has been developed. This approach considers the addition of increasing injections of prandial insulin, beginning with the meal that has the major impact on postprandial glucose values. Finally, if this is not enough intensification to basal-bolus will be necessary. In reducing hyperglycemia, this modality still remains the most effective option, even in people with type 2 diabetes. This article will review the currently evidence on the basal plus strategy and also its progression to basal-bolus therapy. In addition, practical recommendations to start and adjust basal plus therapy will be provided.