Practical implementation of automated closed-loop insulin delivery: A French position statement.

Automated closed-loop (CL) insulin therapy has come of age. This major technological advance is expected to significantly improve the quality of care for adults, adolescents and children with type 1 diabetes. To improve access to this innovation for both patients and healthcare professionals (HCPs), and to promote adherence to its requirements in terms of safety, regulations, ethics and practice, the French Diabetes Society (SFD) brought together a French Working Group of experts to discuss the current practical consensus. The result is the present statement describing the indications for CL therapy with emphasis on the idea that treatment expectations must be clearly defined in advance. Specifications for expert care centres in charge of initiating the treatment were also proposed. Great importance was also attached to the crucial place of high-quality training for patients and healthcare professionals. Long-term follow-up should collect not only metabolic and clinical results, but also indicators related to psychosocial and human factors. Overall, this national consensus statement aims to promote the introduction of marketed CL devices into standard clinical practice.

Modeling the variability of insulin sensitivity for people with Type 1 Diabetes based on clinical data from an artificial pancreas study.

Type 1 Diabetes is an autoimmune disease that eliminates endogenous insulin production. Without the crucial hormone insulin, which is necessary to equilibrate the blood glucose level, the patient must inject insulin subcutaneously. Treatment must be personalized (timing and size of insulin delivery) to achieve glycaemic equilibrium and avoid long-term comorbidities. Patients are educated on Functional Insulin Therapy (FIT) in order to independently adjust insulin delivery several times a day (at least prior to each meal and physical activity). Among personalized parameters, the Correction Factor is used to occasionally correct hyperglycemia via the injection of an insulin dose (bolus) and its value determines the bolus size. Although well-known in common diabetes practice for chronically poorly controlled patients, the phenomenon of "hyperglycemia induces insulin resistance" on a short term basis in patients with rather well controlled diabetes is presented here. Using a new database of evidence, we show that the insulin sensitivity factor, depends on the current level of glycaemia. This opens the door to refining dosing rules for patients and insulin delivery devices in artificial pancreas systems.

Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry.

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions. NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated. Here we developed and validated an activity-specific model which, coupled with an automatic activity-recognition algorithm, improved the variance explained by the predictions from accelerometry counts by 43% of daily PAEE compared with models relying on a simple relationship between accelerometry counts and EE.

Practical implementation, education and interpretation guidelines for continuous glucose monitoring: A French position statement.

The use by diabetes patients of real-time continuous interstitial glucose monitoring (CGM) or the FreeStyle Libre® (FSL) flash glucose monitoring (FGM) system is becoming widespread and has changed diabetic practice. The working group bringing together a number of French experts has proposed the present practical consensus. Training of professionals and patient education are crucial for the success of CGM. Also, institutional recommendations must pay particular attention to the indications for and reimbursement of CGM devices in populations at risk of hypoglycaemia. The rules of good practice for CGM are the precursors of those that need to be enacted, given the oncoming emergence of artificial pancreas devices. It is necessary to have software combining user-friendliness, multiplatform usage and average glucose profile (AGP) presentation, while integrating glucose and insulin data as well as events. Expression of CGM data must strive for standardization that facilitates patient phenotyping and their follow-up, while integrating indicators of variability. The introduction of CGM involves a transformation of treatment support, rendering it longer and more complex as it also includes specific educational and technical dimensions. This complexity must be taken into account in discussions of organization of diabetes care.

Personalization of a compartmental physiological model for an artificial pancreas through integration of patient's state estimation.

Artificial Pancreas (AP) are developed for patients with Type 1 diabetes. This medical device system consists in the association of a subcutaneous continuous glucose monitor (CGM) providing a proxy of the patient's glycaemia and a control algorithm offering the real-time modification of the insulin delivery with an automatic command of the subcutaneous insulin pump. The most complex algorithms are based on a compartmental model of the glucoregulatory system of the patient coupled to an approach of MPC (Model-Predictive-Control) for the command. The automatic and unsupervised control of insulin regulation constitutes a major challenge in AP projects. A given model with its parameterization on the shelf will not directly represent the patient's data behavior and the personalization of the model is a prerequisite before using it in a MPC. The present paper focuses on the personalization of a compartmental showing a method where taking into account the estimation of the patient's state in addition to the parameter estimation improves the results in terms of mean quadratic error.

An original piecewise model for computing energy expenditure from accelerometer and heart rate signals.

OBJECTIVE: Activity energy expenditure (EE) plays an important role in healthcare, therefore, accurate EE measures are required. Currently available reference EE acquisition methods, such as doubly labeled water and indirect calorimetry, are complex, expensive, uncomfortable, and/or difficult to apply on real time. To overcome these drawbacks, the goal of this paper is to propose a model for computing EE in real time (minute-by-minute) from heart rate and accelerometer signals. APPROACH: The proposed model, which consists of an original branched model, uses heart rate signals for computing EE on moderate to vigorous physical activities and a linear combination of heart rate and counts per minute for computing EE on light to moderate physical activities. Model parameters were estimated from a given data set composed of 53 subjects performing 25 different physical activities (light-, moderate- and vigorous-intensity), and validated using leave-one-subject-out. A different database (semi-controlled in-city circuit), was used in order to validate the versatility of the proposed model. Comparisons are done versus linear and nonlinear models, which are also used for computing EE from accelerometer and/or HR signals. MAIN RESULTS: The proposed piecewise model leads to more accurate EE estimations ([Formula: see text], [Formula: see text] and [Formula: see text] J kg-1 min-1 and [Formula: see text], [Formula: see text], and [Formula: see text] J kg-1 min-1 on each validation database). SIGNIFICANCE: This original approach, which is more conformable and less expensive than the reference methods, allows accurate EE estimations, in real time (minute-by-minute), during a large variety of physical activities. Therefore, this model may be used on applications such as computing the time that a given subject spent on light-intensity physical activities and on moderate to vigorous physical activities (binary classification accuracy of 0.8155).

Indication, organization, practical implementation and interpretation guidelines for retrospective CGM recording: A French position statement.

AIM: The benefits of retrospective continuous glucose monitoring (retroCGM) recording have been widely explored in clinical studies, and many diabetes physicians routinely use this examination. However, the method of interpretation of CGM recordings has never been precisely described. METHOD: An expert French panel of physicians met for two days to discuss several aspects of retroCGM use and to produce a position statement. RESULTS: The guidelines cover the indications for retroCGM, the general organization and practical implementation of CGM recordings, a description of the different devices available and guidelines for the interpretation of retroCGM recordings. CONCLUSION: This consensus document should help clinicians in the proper use of retroCGM.

Insulin-based strategies to prevent hypoglycaemia during and after exercise in adult patients with type 1 diabetes on pump therapy: the DIABRASPORT randomized study.

AIMS: To validate strategies to prevent exercise-induced hypoglycaemia via insulin-dose adjustment in adult patients with type 1 diabetes (T1D) on pump therapy. METHODS: A total of 20 patients randomly performed four 30-min late post-lunch (3 h after lunch) exercise sessions and a rest session: two moderate sessions [50% maximum oxygen consumption (VO2 max)] with 50 or 80% basal rate (BR) reduction during exercise + 2 h and two intense sessions (75% VO2 max) with 80% BR reduction or with their pump stopped. Two additional early post-lunch sessions (90 min after lunch) were analysed to compare hypoglycaemia incidence for BR reduction versus bolus reduction. RESULTS: In all, 100 late post-lunch sessions were analysed. Regardless of exercise type and BR reduction, no more hypoglycaemic events occurred in the period until the next morning than occurred after the rest sessions. In the afternoon, no more hypoglycaemic events occurred with 80% BR reduction/moderate exercise or with pump discontinuation/intense exercise than for the rest session, whereas more hypoglycaemic events occurred with 50% BR reduction/moderate exercise and 80% BR reduction/intense exercise. After early post-lunch exercise (n = 37), a trend towards fewer hypoglycaemic episodes was observed with bolus reduction versus BR reduction (p = 0.07). Mean blood glucose fell by ∼3.3 mmol/l after 30 min of exercise, irrespective of dose reduction, remaining stable until the next morning with no rebound hyperglycaemia. CONCLUSION: In adults with T1D, to limit the hypoglycaemic risk associated with 30 min of exercise 3 h after lunch, without carbohydrate supplements, the best options seem to be to reduce BR by 80% or to stop the pump for moderate or intense exercise, or for moderate exercise 90 min after lunch, to reduce the prandial bolus rather than the BR.

Telemedicine and type 1 diabetes: is technology per se sufficient to improve glycaemic control?

AIM: In the TELEDIAB-1 study, the Diabeo system (a smartphone coupled to a website) improved HbA1c by 0.9% vs controls in patients with chronic, poorly controlled type 1 diabetes. The system provided two main functions: automated advice on the insulin doses required; and remote monitoring by teleconsultation. The question is: how much did each function contribute to the improvement in HbA1c? METHODS: Each patient received a smartphone with an insulin dose advisor (IDA) and with (G3 group) or without (G2 group) the telemonitoring/teleconsultation function. Patients were classified as "high users" if the proportion of "informed" meals using the IDA exceeded 67% (median) and as "low users" if not. Also analyzed was the respective impact of the IDA function and teleconsultations on the final HbA1c levels. RESULTS: Among the high users, the proportion of informed meals remained stable from baseline to the end of the study 6months later (from 78.1±21.5% to 73.8±25.1%; P=0.107), but decreased in the low users (from 36.6±29.4% to 26.7±28.4%; P=0.005). As expected, HbA1c improved in high users from 8.7% [range: 8.3-9.2%] to 8.2% [range: 7.8-8.7%] in patients with (n=26) vs without (n=30) the benefit of telemonitoring/teleconsultation (-0.49±0.60% vs -0.52±0.73%, respectively; P=0.879). However, although HbA1c also improved in low users from 9.0% [8.5-10.1] to 8.5% [7.9-9.6], those receiving support via teleconsultation tended to show greater improvement than the others (-0.93±0.97 vs -0.46±1.05, respectively; P=0.084). CONCLUSION: The Diabeo system improved glycaemic control in both high and low users who avidly used the IDA function, while the greatest improvement was seen in the low users who had the motivational support of teleconsultations.

Efficacy and safety of canagliflozin in patients with type 2 diabetes mellitus inadequately controlled with metformin and sulphonylurea: a randomised trial.

AIMS: Canagliflozin is a sodium glucose co-transporter 2 inhibitor developed for the treatment of type 2 diabetes mellitus (T2DM). This randomised, double-blind, placebo-controlled, Phase 3 study evaluated the efficacy and safety of canagliflozin as an add-on to metformin plus sulphonylurea in patients with T2DM. METHODS: Patients (N = 469) received canagliflozin 100 or 300 mg or placebo once daily during a 26-week core period and a 26-week extension. Prespecified primary end-point was change in HbA1c at 26 weeks. Secondary end-points included change in HbA1c at week 52 as well as proportion of patients achieving HbA1c < 7.0%, change in fasting plasma glucose (FPG) and systolic blood pressure, and per cent change in body weight, high-density lipoprotein cholesterol, and triglycerides (weeks 26 and 52). RESULTS: HbA1c was significantly reduced with canagliflozin 100 and 300 mg vs. placebo at week 26 (-0.85%, -1.06%, and -0.13%; p < 0.001); these reductions were maintained at week 52 (-0.74%, -0.96%, and 0.01%). Both canagliflozin doses reduced FPG and body weight vs. placebo at week 26 (p < 0.001) and week 52. Overall adverse event (AE) rates were similar across groups over 52 weeks, with higher rates of genital mycotic infections and osmotic diuresis-related AEs seen with canagliflozin vs. placebo; these led to few discontinuations. Increased incidence of documented, but not severe, hypoglycaemia episodes was seen with canagliflozin vs. placebo. CONCLUSIONS: Canagliflozin improved glycaemic control, reduced body weight, and was generally well tolerated in T2DM patients on metformin plus sulphonylurea over 52 weeks.