From fingerstick blood glucose monitoring and mechanical insulin pens in the 1970s to modern automated insulin delivery systems, rapidly progressing advances in diabetes technology are transforming management options for people with diabetes, particularly those with type 1 diabetes but also, increasingly, people with type 2 diabetes. However, access to life-changing diabetes technologies is neither uniform nor universally covered, and there is no one size fits all approach. In this position statement, we emphasise to healthcare professionals the importance of supporting individuals with diabetes to access and use the right diabetes technology according to personal needs, capabilities and preferences. In doing so, we highlight the equal importance of avoiding disparities in the provision of diabetes technology by challenging preconceived barriers, which can be overcome with education and determination. We also make a series of suggestions for action to advance the more widespread adoption of diabetes technology while minimising the ‘digital divide’.

The addition of SGLT2i to insulin therapy in type 1 diabetes (T1D) is an emerging treatment strategy. This study evaluates the real-world effects of SGLT2i on glycaemic control and other outcomes in individuals with T1D. In this single-center retrospective study, we included 78 adults with T1D who initiated SGLT2i and were observed for up to 24 months. Data included demographics, laboratory values, diabetic complications, and ongoing therapy. The primary outcome was the change in HbA1c over time. Persistence on therapy and adverse events were also recorded. The mean age was 47.2 years, diabetes duration 24.6 years, baseline HbA1c 8.3

Despite the growing evidence supporting the outpatient use of continuous glucose monitoring (CGM) for improving glycaemic control and reducing hypoglycaemia, there is a need for a detailed understanding of the specific features of CGM devices that best meet individual patient needs. This expert opinion, based on a comprehensive literature review and the personal perspectives of clinicians, aims to provide the healthcare professionals (HCPs) with a comprehensive framework for selecting CGM devices. It evaluates the current state of CGM technology, categorizing features into essential features, major drivers of choice, and additional/useful features. Moreover, the practical model presented outlines a patient’s journey with CGM, emphasising the importance of aligning device features with patient needs. This includes understanding the patient’s lifestyle, clinical conditions, and personal preferences to optimize CGM use and improve diabetes management outcomes.

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

The artificial pancreas or automated insulin delivery (AID) system is an important step forward in the treatment of diabetes. Besides commercial AID systems, open-source automated insulin delivery systems (opensource-AID), known more colloquially as “Do-it-Yourself Artificial Pancreas Systems” (DIY APS) have become available. They couple algorithms developed by patients with diabetes, with commercially available and regulatory-approved insulin pumps and continuous glucose monitoring (CGM) sensors. These closed loop systems, are categorized by the technology and algorithm they incorporate (OpenAPS, Android APS or Loop). Until now only one open-source algorithm has been approved by Food and Drug Administration. Different studies, observational, real time and randomized-controlled have shown that AID open-source systems can improve metabolic control and quality of life in patients with diabetes, while decreasing the burden of diabetes management. Aim of this paper is to summarize data from the literature and to reason about the implications that the arrival of this new technique will have for patients and caregivers.

BACKGROUND:Providing real-time magnitude and direction of glucose rate-of-change (ROC) via trend arrows represents one of the major strengths of continuous glucose monitoring (CGM) sensors in managing type 1 diabetes (T1D). Several literature methods were proposed to adjust the standard formula (SF) used for insulin bolus calculation by accounting for glucose ROC, but each of them provides different suggestions, making it difficult to understand which should be applied in practice. This work aims at performing an extensive in-silico assessment of their performance and safety.METHODS:The methods of Buckingham (BU), Scheiner (SC), Pettus/Edelman (PE), Klonoff/Kerr (KL), Aleppo/Laffel (AL), Ziegler (ZI), and Bruttomesso (BR) were evaluated using the UVa/Padova T1D simulator, in single-meal scenarios, where ROC and glucose at mealtime varied between [-2,+2] mg/dL/min and [80,200] mg/dL, respectively. Efficacy of postprandial glucose control was quantitatively assessed by time in, above and below range (TIR, TAR, and TBR, respectively).RESULTS:For negative ROCs, all methods proved to increase TIR and decrease TAR and TBR vs SF, with KL, PE, and BR being the most effective. For positive ROCs, a general worsening of the performances is present, only BR improved the glycemic control when mealtime glucose was close to hypoglycemia, while SC resulted the safest in the other conditions.CONCLUSIONS:Insulin bolus adjustment methods are effective for negative ROCs, but they generally appear to overdose for positive ROCs, calling for safer strategies in such a scenario. These results can be useful in outlining guidelines to identify which adjustment to apply based on the mealtime condition.

Background and aims: Intensive glycemic control minimizes the risk of micro-and macrovascular complications in patients with type 1 diabetes (T1D). We report glycemic control in Italian participants (age groups: 26-44, 45-64, and >= 65 years) of the global SAGE study. Methods and results: The primary endpoint was proportion of participants who achieved an HbA1c <7% in predefined age groups. In the 523 patients with T1D, mean age was 44.6 years and mean body mass index (BMI) was 25 kg/m2. Mean HbA1c was 7.5% and 29.4% had HbA1c <7.0%, with the highest percentage in those 26-45 years (31.7%) and the lowest in those >= 65 years (20%). Altogether, 22.9% of patients achieved their physician-established individualized HbA1c target. Most patients had >= 1 symptomatic hypoglycemic episode in the previous 3 months (<70 mg/dL 82.5%; <54 mg/dL 61%). Severe hypo-and hyperglycemia were experienced by 16.3% and 12% of patients, of which 7.1 and 9.5%, respectively, required hospitalization/emergency visits. More patients achieved HbA1c <7% with CSII (30%) than with multiple daily insulin injec-tions (27.9%). In multivariate analysis, BMI (OR 0.94, 95% CI 0.89-0.99, p = 0.032) and adherence to diet (OR 0.36, 95% CI 0.18-0.70, p = 0.0028) were significantly associated with HbA1c <7.0%. Conclusions: Glycemic control can be considered good in the Italian SAGE cohort, especially in younger patients, who more frequently use pumps/continuous glucose monitoring. Greater pa-tient education and use of technology may further support this achievement. Patients should be encouraged to maintain a low BMI and adhere to their diet. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Management of diabetes in children younger than 6 years of age may be difficult owing to their unpredictable eating and activity patterns, low insulin requirements, and high glycemic variability. However, tight glycemic control is necessary because unmet recommended target glucose levels may have lifelong consequences.1,2 Metabolic control can be improved with the use of a closed-loop system (also termed an "artificial pancreas"), which combines an insulin pump, a continuous glucose monitoring sensor, and a control algorithm that adjusts the amount of insulin administered in real time according to input from the sensor. Currently, all commercially available closed-loop systems are hybrid, . . .

Whether glucose sensor alarms improve metabolic control and are accepted by individuals with diabetes is unclear. Here, we investigated whether switching from a standard flash glucose monitoring system (FGM1) to a system equipped with hypo- and hyperglycemia alarms (FGM2) improves glycemic control and psychological outcomes in adults with type 1 diabetes (T1D). Subjects with T1D and > 4% of time in hypoglycemia or > 40% of time in hyperglycemia were studied while wearing FGM1 (4 weeks) and after switching to FGM2 for 8 weeks. The primary endpoint was the change in time in range (TIR 70–180 mg/dl [3.9–10.0 mmol/L]) after 4 weeks of FGM2 use. Time below range (TBR), time above range (TAR), mean glucose, coefficient of variation (CV), sensor scans, treatment satisfaction, and hypoglycemia fear were secondary outcomes. We included 38 subjects aged 33.7 ± 12.6 year. During 4 weeks of FGM2 use, TIR increased from 52.8 to 57.0% (p = 0.001), TBR decreased from 6.2 to 3.4% (p < 0.0001) as did time < 54 mg/dl (from 1.4 to 0.3%, p < 0.0001) and CV (from 39.6% to 36.1%, p < 0.0001). These changes were confirmed after 8 weeks of FGM2 use. Treatment satisfaction improved and fear of hypoglycemia decreased. Subjects who had > 4% of time in hypoglycemia at baseline showed the greatest improvements in glucose control and treatment satisfaction. Switching from FGM1 to FGM2 improved TIR and treatment satisfaction and reduced fear of hypoglycemia. Participants who benefited most from switching from FGM1 to FGM2 were those prone to hypoglycemia.

Aims Post-prandial hyperglycemia remains an unmet need in the management of type 1 diabetes (T1D). In randomized trials, faster insulin aspart (FIA) showed modest but significant reductions of glycemic spikes after meals. Whether such benefit is evident in routine clinical practice is unclear. Methods We analyzed data of patients with T1D at the time they switched from a prior bolus insulin to FIA and at the first available follow-up. The primary endpoint was the change in the time spent in hyperglycemia > 250 mg/dl during daytime from flash glucose monitoring (FGM). Secondary outcomes included the change in HbA1c, body weight, insulin dose and other FGM metrics. Results We included 117 patients with T1D on multiple daily injections who switched to FIA, 57 of whom had data from FGM. Patients were 41-year-old, 51.3% men, with 19.3 years diabetes duration and a baseline HbA1c of 7.7% (60 mmol/mol). Mean observation time was 4.3 months. After switching to FIA, HbA1c declined by 0.1% (1 mmol/mol) only in patients with baseline HbA1c > 7.0% (53 mmol/mol). Time spent in hyperglycemia > 250 mg/dl during daytime was significantly reduced from 14.8 to 11.9% ( p = 0.006). Time in range improved from 48.3 to 51.0% ( p = 0.028). Results were consistent across various patient characteristics. Conclusions Under routine care, patients with T1D who switched to FIA experienced a reduction in the time spent in hyperglycemia > 250 mg/dl during daytime and an increase in time in range. These improvements may be due to better control of post-prandial hyperglycemia, as observed in trials.

A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialised for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organisations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organisation has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonisation of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organisations for confirmation.

Aims: Automated insulin delivery improves glucose control. Aim of this study was to compare in real life the effects on glucose control and patient reported outcomes of an advanced hybrid closed loop system (Control-IQ), versus a simpler system with predictive low glucose suspend function (Basal-IQ). Methods: Thirty-one type 1 diabetic subjects were studied during Basal-IQ and after switching to Control-IQ. Variables analyzed were time spent in range (70-180 mg/dL), in tight range (70-140 mg/dL), above range (>180 mg/dL), below range (<70 mg/dL), mean glucose, coefficient of variation and glycated hemoglobin. Questionnaires were administered regarding therapy satisfaction (Diabetes Treatment Satisfaction Questionnaire in status/change form), fear of hypoglycemia (Hypoglycemia Fear Survey), quality of sleep (Pittsburgh Sleep Quality Index). Results: After 12 weeks of Control-IQ, time in range increased from 62.7 to 74.0%, p < 0.0001, time in tight range increased from 37.1 to 44.6 %, p < 0.001, time above range decreased from 35.6 to 24.4% p < 0.0001. Improvements were observed in mean glucose and glucose variability. Glycated hemoglobin decreased from 7.0% (53 mmol/mol) to 6.6% (49 mmol/mol), p < 0.0001. Subjects using Control-IQ manifested greater satisfaction and less fear of hypoglycemia. Conclusion: Compared to Basal-IQ, Control-IQ improves glucose control and therapy satisfaction.

Aims To conduct a pooled analysis to assess the performance of intermittently scanned continuous glucose monitoring (isCGM) in association with the rate of change in sensor glucose in a cohort of children, adolescents, and adults with type 1 diabetes. Material and Methods In this pooled analysis, isCGM system accuracy was assessed depending on the rate of change in sensor glucose. Clinical studies that have been investigating isCGM accuracy against blood glucose, accompanied with collection time points were included in this analysis. isCGM performance was assessed by means of median absolute relative difference (MedARD), Parkes error grid (PEG) and Bland-Altman plot analyses. Results Twelve studies comprising 311 participants were included, with a total of 15 837 paired measurements. The overall MedARD (interquartile range) was 12.7% (5.9-23.5) and MedARD differed significantly based on the rate of change in glucose (P < 0.001). An absolute difference of -22 mg/dL (-1.2 mmol/L) (95% limits of agreement [LoA] 60 mg/dL (3.3 mmol/L), -103 mg/dL (-5.7 mmol/L)) was found when glucose was rapidly increasing (isCGM glucose minus reference blood glucose), while a -32 mg/dL (1.8 mmol/L) (95% LoA 116 mg/dL (6.4 mmol/L), -51 mg/dL (-2.8 mmol/L)) absolute difference was observed in periods of rapidly decreasing glucose. Conclusions The performance of isCGM was good when compared to reference blood glucose measurements. The rate of change in glucose for both increasing and decreasing glucose levels diminished isCGM performance, showing lower accuracy during high rates of glucose change.

Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) differ for triggers, mode of start, associated symptoms, evolution, and biochemical traits. Therefore, serious attempts are underway to partition them into subgroups useful for a personalized medicine approach to the disease. Here, we investigated clinical and biochemical traits in 40 ME/CFS patients and 40 sex- and age-matched healthy controls. Particularly, we analyzed serum levels of some cytokines, Fatty Acid Binding Protein 2 (FAPB-2), tryptophan, and some of its metabolites via serotonin and kynurenine. ME/CFS patients were heterogeneous for genetic background, trigger, start mode, symptoms, and evolution. ME/CFS patients had higher levels of IL-17A (p = 0.018), FABP-2 (p = 0.002), and 3-hydroxykynurenine (p = 0.037) and lower levels of kynurenine (p = 0.012) and serotonin (p = 0.045) than controls. Changes in kynurenine and 3-hydroxykynurenine were associated with increased kynurenic acid/kynurenine and 3-hydroxykynurenine/kynurenine ratios, indirect measures of kynurenine aminotransferases and kynurenine 3-monooxygenase enzymatic activities, respectively. No correlation was found among cytokines, FABP-2, and tryptophan metabolites, suggesting that inflammation, anomalies of the intestinal barrier, and changes of tryptophan metabolism may be independently associated with the pathogenesis of the disease. Interestingly, patients with the start of the disease after infection showed lower levels of kynurenine (p = 0.034) than those not starting after an infection. Changes in tryptophan metabolites and increased IL-17A levels in ME/CFS could both be compatible with anomalies in the sphere of energy metabolism. Overall, clinical traits together with serum biomarkers related to inflammation, intestine function, and tryptophan metabolism deserve to be further considered for the development of personalized medicine strategies for ME/CFS.

Background Starting March 2020 the Italian Government imposed a lockdown to limit the spread of SARS-CoV-2. During lockdown outpatient visits were limited and telemedicine (TM) was encouraged. Methods We retrospectively analyzed data from continuous or flash glucose monitoring systems shared through different cloud systems during the lockdown by subjects with type 1 diabetes and compared data obtained 4 weeks before and 4 weeks after structured telephonic visit. Variables considered were mean glucose, time spent in target (70–180 mg/dl), hypoglycemia (<70 mg/dl) and hyperglycemia (>180 mg/dl), coefficient of variation, and length of sensor use. Results During the 4 weeks following the telephonic visit there was an improvement of glycemic control, with a significant reduction of mean glucose values (161.1 before vs 156.3 mg/dl after, p = 0.001), an increase of the time spent in target (63.6 vs 66.3, p = 0.0009) and a reduction of time spent in hyperglycemia (33.4 vs 30.5, p = 0.002). No changes were observed regarding glucose variability, time spent in hypoglycemia, and length of sensor use. Similar results were observed in subjects treated with multiple daily injections or continuous subcutaneous insulin infusion. Conclusions A structured telephonic visit appears to be an effective way to replace or integrate routine visits in particular conditions.

Background and aims: Continuous glucose monitoring improves glycemic control in diabetes. This study compared the accuracy of the Dexcom G5 Mobile (Dexcom, San Diego, CA) transcutaneous sensor (DG5) and the first version of Eversense (Senseonics,Inc., German-town, MD) implantable sensor (EVS). Methods and results: Subjects with type 1 diabetes (TID) and using EVS wore simultaneously DG5 for seven days. At day 3, patients were admitted to a clinical research center (CRC) to receive breakfast with delayed and increased insulin bolus to induce glucose excursions. At CRC, venous glucose was monitored every 15 min (or 5 min during hypoglycemia) for 6 h by YSI 2300 STAT PLUS (TM) glucose and lactate analyzer. At home patients were requested to perform 4 fingerstick glucose measurements per day. Eleven patients (9 males, age 47.4 +/- 11.3 years, MISD) were enrolled. During home-stay the median [25th-75th percentile] absolute relative difference (ARD) over all CGM-fingerstick matched-pairs was 11.64% [5.38-20.65]% for the DG5 and 10.75% [5.15-19.74]% for the EVS (p-value = 0.58). At CRC, considering all the CGM-YSI matched-pairs, the DG5 showed overall smaller median ARD than EVS, 7.91% [4.14-14.30]% vs 11.4% [5.04-18.54]% (p-value<0.001). Considering accuracy during blood glucose swings, DG5 performed better than EVS when glucose rate-of-change was -0.5 to -1.5 mg/dL/min, with median ARD of 7.34% [3.71-12.76]% vs 13.59% [4.53-20.78]% (p-value<0.001), and for rate-of-change < -1.5 mg/di/min, with median ARD of 5.23% [2.09-15.29]% vs 12.73% [4.14-20.82]% (p-value = 0.02). Conclusions: DG5 was more accurate than EVS at CRC, especially when glucose decreased. No differences were found at home. (C) 2020 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

We present a new mobile platform to be used in clinical trials aimed at both collecting data and assessing new technologies and treatments for diabetes care. The main components of the platform are a mobile app, that automatically collects data from continuous glucose monitoring sensors and activity trackers, and also allows users to manually log daily events; a cloud database for safe data storage; a web interface, which allows clinicians to monitor patients’ status in real-time. The platform is modular and highly customizable for a multitude of purposes in clinical research. Preliminary tests performed for daily-life data gathering by both clinicians and users are extremely encouraging.