Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.

Measurement of glycated hemoglobin (HbA 1c ) has been the traditional method for assessing glycemic control. However, it does not reflect intra-and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA 1c testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA 1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.Glucose measurements are critical to effective diabetes management. Although measurement of glycated hemoglobin (HbA 1c ) has been the traditional method for assessing glycemic control, it does not reflect intra-and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Moreover, although self-monitoring of blood glucose (SMBG) has been shown to improve glycemic control and quality of life in both insulin-treated and noninsulin-treated diabetes when used within a structured testing regimen (1-4) [C,C,C,C], it cannot predict impending hypoglycemia or alert for hypoglycemia (5,6) [C,C] (7).Real-time continuous glucose monitoring (rtCGM) and intermittently viewed CGM (iCGM) address many of the limitations inherent in HbA 1c testing and SMBG. rtCGM uniformly tracks the glucose concentrations in the body's interstitial fluid, providing near real-time glucose data; iCGM uses similar methodology to show continuous glucose measurements retrospectively at the time of checking. Both rtCGM and iCGM facilitate monitoring of time spent in the target glucose range ("time in range"). However, only rtCGM can warn users if glucose is trending toward hypoglycemia or hyperglycemia. With iCGM, these trends can only be viewed after physically scanning the sensor. It is often difficult to distinguish between technologies regarding issues such as calibrations, alarms/alerts, human factors of applying and wearing sensors, and the cost, which are device specific. As these technological details are subject to constant change, the term CGM is used for all issues related to the device class unless indicated otherwis...

A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.

Among patients with type 1 diabetes, 12-week use of a closed-loop system, as compared with sensor-augmented pump therapy, improved glucose control, reduced hypoglycemia, and, in adults, resulted in a lower glycated hemoglobin level. (Funded by the JDRF and others; AP@home04 and APCam08 ClinicalTrials.gov numbers, NCT01961622 and NCT01778348.).

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We have separated the effect of insulin on glucose distribution/transport, glucose disposal, and endogenous production (EGP) during an intravenous glucose tolerance test (IVGTT) by use of a dualtracer dilution methodology. Six healthy lean male subjects (age 33 Ϯ 3 yr, body mass index 22.7 Ϯ 0.6 kg/m A new model described the kinetics of the two glucose tracers and native glucose with the use of a two-compartment structure for glucose and a onecompartment structure for insulin effects. Insulin sensitivities of distribution/transport, disposal, and EGP were similar (11.5 Ϯ 3.8 vs. 10.4 Ϯ 3.9 vs. 11.1 Ϯ 2.7 ϫ 10 Ϫ2 ml⅐kg Ϫ1 ⅐min Ϫ1per mU/l; P ϭ nonsignificant, ANOVA). When expressed in terms of ability to lower glucose concentration, stimulation of disposal and stimulation of distribution/transport accounted each independently for 25 and 30%, respectively, of the overall effect. Suppression of EGP was more effective (P Ͻ 0.01, ANOVA) and accounted for 50% of the overall effect. EGP was suppressed by 70% (52-82%) (95% confidence interval relative to basal) within 60 min of the IVGTT; glucose distribution/ transport was least responsive to insulin and was maximally activated by 62% (34-96%) above basal at 80 min compared with maximum 279% (116-565%) activation of glucose disposal at 20 min. The deactivation of glucose distribution/transport was slower than that of glucose disposal and EGP (P Ͻ 0.02) with half-times of 207 (84-510), 12 (7-22), and 29 (16-54) min, respectively. The minimal-model insulin sensitivity was tightly correlated with and linearly related to sensitivity of EGP (r ϭ 0.96, P Ͻ 0.005) and correlated positively but nonsignificantly with distribution/transport sensitivity (r ϭ 0.73, P ϭ 0.10) and disposal sensitivity (r ϭ 0.55, P ϭ 0.26). We conclude that, in healthy subjects during an IVGTT, the two peripheral insulin effects account jointly for approximately one-half of the overall insulin-stimulated glucose lowering, each effect contributing equally. Suppression of EGP matches the effect in the periphery.glucose kinetics; compartment modeling; D-[U-13 C]glucose; 3-O-methyl-D-glucose; insulin action; glucose transport; glucose disposal; endogenous glucose production; intravenous glucose tolerance test Glossary New Model EGP 0Endogenous glucose production extrapolated to zero insulin concentration (mmol/min) EGP b EGP at basal insulin concentration (mmol/min) F 01Total non-insulin-dependent glucose flux (mmol/min) g 1 (t), g 3 (t)Concentrations of D-[U-13 C]glucose and 3-O-methyl-D-glucose in the accessible compartment (mmol/l) G(t)Total glucose concentration in the accessible compartment (mmol/l) I(t), I b Plasma insulin and basal (preexperimental) plasma insulin (mU/l) k 03 Transfer rate constant of 3-O-methyl-D-glucose excretion (minTransfer rate constant from nonaccessible to accessible compart-Deactivation rate constants (minActivation rate constants (min Ϫ2per mU/l) q 1 (t), q 2 (t)Masses of D-[U-13 C]glucose in the two compartments (mmol) q 3 (t), q 4 (t)Masses of 3-O-methyl-D-glucose in ...

The glucose monitor remains the main limiting factor in the development of a commercially viable closed-loop system, as presently available monitors fail to demonstrate satisfactory characteristics in terms of reliability and/or accuracy. Regulatory issues are the second limiting factor. Closed-loop systems are likely to be used first by health-care professionals in controlled environments such as intensive care units.

ObjectiveTo evaluate the efficacy and safety of artificial pancreas treatment in non-pregnant outpatients with type 1 diabetes.DesignSystematic review and meta-analysis of randomised controlled trials.Data sourcesMedline, Embase, Cochrane Library, and grey literature up to 2 February 2018.Eligibility criteria for selecting studiesRandomised controlled trials in non-pregnant outpatients with type 1 diabetes that compared the use of any artificial pancreas system with any type of insulin based treatment. Primary outcome was proportion (%) of time that sensor glucose level was within the near normoglycaemic range (3.9-10 mmol/L). Secondary outcomes included proportion (%) of time that sensor glucose level was above 10 mmol/L or below 3.9 mmol/L, low blood glucose index overnight, mean sensor glucose level, total daily insulin needs, and glycated haemoglobin. The Cochrane Collaboration risk of bias tool was used to assess study quality.Results40 studies (1027 participants with data for 44 comparisons) were included in the meta-analysis. 35 comparisons assessed a single hormone artificial pancreas system, whereas nine comparisons assessed a dual hormone system. Only nine studies were at low risk of bias. Proportion of time in the near normoglycaemic range (3.9-10.0 mmol/L) was significantly higher with artificial pancreas use, both overnight (weighted mean difference 15.15%, 95% confidence interval 12.21% to 18.09%) and over a 24 hour period (9.62%, 7.54% to 11.7%). Artificial pancreas systems had a favourable effect on the proportion of time with sensor glucose level above 10 mmol/L (−8.52%, −11.14% to −5.9%) or below 3.9 mmol/L (−1.49%, −1.86% to −1.11%) over 24 hours, compared with control treatment. Robustness of findings for the primary outcome was verified in sensitivity analyses, by including only trials at low risk of bias (11.64%, 9.1% to 14.18%) or trials under unsupervised, normal living conditions (10.42%, 8.63% to 12.2%). Results were consistent in a subgroup analysis both for single hormone and dual hormone artificial pancreas systems.ConclusionsArtificial pancreas systems are an efficacious and safe approach for treating outpatients with type 1 diabetes. The main limitations of current research evidence on artificial pancreas systems are related to inconsistency in outcome reporting, small sample size, and short follow-up duration of individual trials.