Do-it-yourself closed-loop systems for people living with type 1 diabetes.

Growing numbers of people with type 1 diabetes are using do-it-yourself closed-loop systems. While these technologies are not approved by regulatory bodies and are not commercially available, users of the technology report improvements in HbA1c and time in range, and reduced burden of diabetes. Healthcare professionals have expressed their concern that legal or regulatory body actions could ensue if they support people who choose to use do-it-yourself closed-loop systems. Diabetes UK's position statements make recommendations that aim to provide guidance for both people with diabetes and healthcare professionals, based on the current professional and legal situation. They respect an individual's right to make their own informed decisions about their diabetes management, and recommend that they should have access to the technology they need for optimal diabetes management. People who wish to use do-it-yourself closed-loop systems should continue to receive support and care from their diabetes team. Healthcare professionals should engage in conversations around do-it-yourself closed-loop systems, if the issue is raised, to allow a balanced discussion of risks and benefits. However, healthcare professionals cannot recommend the use of do-it-yourself closed-loop systems because of a lack of regulatory body approval and robust, published research to support safety or effectiveness. People using this technology should be aware that they do so at their own risk. This position statement recognizes that the development of diabetes technology is a rapidly changing environment, and guidance around do-it-yourself systems is required from professional and regulatory bodies

Efficacy and safety of continuous glucose monitoring and intermittently scanned continuous glucose monitoring in patients with type 2 diabetes: A systematic review and meta-analysis of interventional evidence

BACKGROUND Traditional diabetes self-monitoring of blood glucose (SMBG) involves inconvenient finger pricks. Continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems offer CGM, enhancing type 2 diabetes (T2D) management with convenient, comprehensive data. PURPOSE To assess the benefits and potential harms of CGM and isCGM compared with usual care or SMBG in individuals with T2D. DATA SOURCES We conducted a comprehensive search of MEDLINE, Embase, the Cochrane Library, Web of Science, and bibliographies up to August 2023. STUDY SELECTION We analyzed studies meeting these criteria: Randomized controlled trials (RCT) with comparison of at least two interventions for ≥8 weeks in T2D patients, including CGM in real-time/retrospective mode, short-/long-term CGM, isCGM, and SMBG, reporting glycemic and relevant data. DATA EXTRACTION We used a standardized data collection form, extracting details including author, year, study design, baseline characteristics, intervention, and outcomes. DATA SYNTHESIS We included 26 RCTs (17 CGM and 9 isCGM) involving 2,783 patients with T2D (CGM 632 vs. usual care/SMBG 514 and isCGM 871 vs. usual care/SMBG 766). CGM reduced HbA1c (mean difference 20.19% [95% CI 20.34, 20.04]) and glycemic medication effect score (20.67 [21.20 to 20.13]), reduced user satisfaction (20.54 [20.98, 20.11]), and increased the risk of adverse events (relative risk [RR] 1.22 [95% CI 1.01, 1.47]). isCGM reduced HbA1c by 20.31% (20.46, 20.17), increased user satisfaction (0.44 [0.29, 0.59]), improved CGMmetrics, and increased the risk of adverse events (RR 1.30 [0.05, 1.62]). Neither CGMnor isCGMhad a significant impact on body composition, blood pressure, or lipid levels. LIMITATIONS Limitations include small samples, single-study outcomes, population variations, and uncertainty for younger adults. Additionally, inclusion of <10 studies for most end points restricted comprehensive analysis, and technological advancements over time need to be considered. CONCLUSIONS Both CGM and isCGM demonstrated a reduction in HbA1c levels in individuals with T2D, and unlike CGM, isCGM use was associated with improved user satisfaction. The impact of these devices on body composition, blood pressure, and lipid levels remains unclear, while both CGMand isCGMuse were associated with increased risk of adverse events

A practical approach to the clinical challenges in initiation of basal insulin therapy in people with type 2 diabetes

Initiating insulin therapy with a basal insulin analogue has become a standard of care in the treatment of type 2 diabetes mellitus (T2DM). Despite increasing choices in pharmacological approaches, intensified glucose monitoring and improvements in quality of care, many patients do not achieve the desired level of glycaemic control. Although insulin therapy, when optimized, can help patients reach their glycaemic goals, there are barriers to treatment initiation on both the side of the patient and provider. Providers experience barriers based on their perceptions of patients' capabilities and concerns. They may lack the confidence to solve the practical problems of insulin therapy and avoid decisions they perceive as risky for their patients. In this study, we review recommendations for basal insulin initiation, focussing on glycaemic targets, titration, monitoring, and combination therapy with non-insulin anti-hyperglycaemic medications. We provide practical advice on how to address some of the key problems encountered in everyday clinical practice and give recommendations where there are gaps in knowledge or guidelines. We also discuss common challenges faced by people with T2DM, such as weight gain and hypoglycaemia, and how providers can address and overcome them

Trends in approaches to assist freeze-drying of food: A cohort study on innovations

Freeze drying has been a very successful technology in the food and pharma industries, particularly owing to the benefits it offers in terms of product quality. It is most recommended for high-value foods and those that contain heat-sensitive ingredients. Over the years, to meet industry requirements, several variants of the freeze-drying technology have been developed, even to meet specific drying applications. The process of freeze-drying is highly energy-intensive and time-consuming. Nevertheless, innovative techniques are available with the potential to produce products at reduced drying times and lower cost, while being environment-friendly and maintaining freeze-dried quality. This review summarizes advances in the application of infrared, microwave, ultrasound, pulsed electric field, and other techniques as pretreatments and/or to assist conventional freeze-drying processes. Freeze drying combined with other techniques can provide more benefits in terms of energy, time, and cost savings. In this review, comparative studies have been presented to describe these aspects. Such techniques to assist the freeze-drying process can be linked well with sustainable food processing strategies, particularly considering a significant reduction in energy requirements

Insulin Pumps and Hybrid Close Loop Systems Within Hospital: A Scoping Review and Practical Guidance From the Joint British Diabetes Societies for Inpatient Care

This article is the second of a two-part series providing a scoping review and summary of the Joint British Diabetes Societies for Inpatient Care (JBDS-IP) guidelines on the use of diabetes technology in people with diabetes admitted to hospital. The first part reviewed the use of continuous glucose monitoring (CGM) in hospital. In this article, we focus on the use of continuous subcutaneous insulin infusion (CSII; insulin pumps) and hybrid closed-loop systems in hospital. JBDS-IP advocates enabling people who can self-manage and are willing and capable of using CSII to continue doing so as they would do out of hospital. CSII should be discontinued if the individual is critically ill or hemodynamically unstable. For individuals on hybrid closed-loop systems, the system should be discontinued from auto-mode, and may be used individually (as CGM only or CSII only, if criteria are met). Continuing in closed-loop mode may only be done so under specialist guidance from the Diabetes Team, where the diabetes teams are comfortable and knowledgeable about the specific devices used. Health care organizations need to have clear local policies and guidance to support individuals using these wearable technologies, and ensure the relevant workforce is capable and skilled enough to ensure their safe use within the hospital setting

Using Technology to Improve Diabetes Care in Hospital: The Challenge and the Opportunity

The past 10 years have seen a revolution in technology improving the lives of people with diabetes. This has implications for diabetes care in hospitalized inpatients. These technological developments have the potential to significantly improve the care of people with diabetes in hospital. Combining point of care glucose monitoring, electronic prescribing, electronic observations with electronic referral, and electronic health records allow teams to daily oversee the whole hospital population. To make the most of these tools as well as developing the use of pumps and glucose sensors in hospital, the diabetes team needs to work in new ways. To date, very little work has described how these should be combined. We describe how this technology can be combined to improve diabetes care in hospital.</p

Continuous Glucose Monitoring Within Hospital: A Scoping Review and Summary of Guidelines From the Joint British Diabetes Societies for Inpatient Care

Increasing numbers of people, particularly with type 1 diabetes (T1D), are using wearable technologies. That is, continuous subcutaneous insulin infusion (CSII) pumps, continuous glucose monitoring (CGM) systems, and hybrid closed-loop systems, which combine both these elements. Given over a quarter of all people admitted to hospital have diabetes, there is a need for clinical guidelines for when people using them are admitted to hospital. The Joint British Diabetes Societies for Inpatient Care (JBDS-IP) provide a scoping review and summary of guidelines on the use of diabetes technology in people with diabetes admitted to hospital. JBDS-IP advocates enabling people who can self-manage and use their own diabetes technology to continue doing so as they would do out of hospital. Whilst people with diabetes are recommended to achieve a target of 70% time within range (3.9-10.0 mmol/L [70-180 mg/dL]), this can be very difficult to achieve whilst unwell. We therefore recommend targeting hypoglycemia prevention as a priority, keeping time below 3.9 mmol/L (70 mg/dL) at < 1%, being aware of looming hypoglycemia if glucose is between 4.0 and 5.9 mmol/L (72-106 mg/dL), and consider intervening, particularly if there is a downward CGM trend arrow. Health care organizations need clear local policies and guidance to support individuals using diabetes technologies, and ensure the relevant workforce is capable and skilled enough to ensure their safe use within the hospital setting. The current set of guidelines is divided into two parts. Part 1, which follows below, outlines the guidance for use of CGM in hospital. The second part outlines guidance for use of CSII and hybrid closed-loop in hospital.</p

Personality traits of alexithymia and perfectionism in impaired awareness of hypoglycemia in adults with type 1 diabetes – An exploratory study

Objective: Severe hypoglycemia complicates insulin therapy for type 1 diabetes, with impaired awareness of hypoglycemia (IAH) being a major risk factor. We explored associations between the personality traits, alexithymia and perfectionism, and cognitive barriers to hypoglycemia avoidance described in IAH, and evaluated their prevalence in people with and without IAH. Methods: Cross-sectional exploratory study. Ninety adults with type 1 diabetes, 54 hypoglycemia aware and 36 with IAH, completed validated questionnaires exploring alexithymia (Total Alexithymia Scale [TAS-20]) and perfectionism (Frost Multidimensional Perfectionism Scale [FMPS]); and cognitive barriers related to hypoglycemia avoidance (Attitudes to Awareness Questionnaire [A2A]. Results: Alexithymia and perfectionism scores correlated positively with cognitive barriers associated with IAH. Specifically, alexthymia scores correlated with the ‘Hyperglycaemia Avoidance Prioritised’ factor (r = 0.265; p =.02, n = 77) and the ‘Asymptomatic Hypoglycemia Normalised’ factor (r = 0.252–0.255; p =.03, n = 77). Perfectionism scores correlated with the ‘Hyperglycaemia Avoidance Prioritised’ factor (r = 0.525; p <.001, n = 66). Overall, IAH participants were significantly more likely to score at the high end for alexithymia (17.6% vs. 1.9%, p =.008, n = 87) and at the extreme ends (high and low) for perfectionism (69.0% vs. 40.0%, χ2 (1) = 6.24, p =.01, n = 77). Conclusion: These novel data showing associations between alexithymia and perfectionism scores and maladaptive health beliefs in IAH suggest the intriguing possibility that personality traits may contribute to the risk of IAH, perhaps through their influence on incentives to avoid hypoglycemia. If confirmed, measuring such traits may help tailor early adjunctive psychological intervention to reduce hypoglycemia burden for people with IAH

Risk of hypoglycemia in type 1 diabetes management: An in-silico sensitivity analysis to assess and rank the quantitative impact of different behavioral factors

Background and Objective: In type 1 diabetes (T1D), a quantitative evaluation of the impact on hypoglycemia of suboptimal therapeutic decision (e.g. incorrect estimation of the ingested carbohydrates, inaccurate insulin timing, etc) is unavailable. Clinical trials to measure sensitivity to patient actions would be expensive, exposed to confounding factors and risky for the participants. In this work, a T1D patient decision simulator (T1D-PDS), realistically reproducing blood glucose dynamics in a large virtual population, is used to perform extensive in-silico trials and the so-derived data employed to implement a sensitivity analysis that ranks different behavioral factors based on their impact on a clinically meaningful parameter, the time below range (TBR). Methods: Eleven behavioral factors impacting on hypoglycemia are considered. The T1D-PDS was used to perform multiple 2-week simulations involving 100 adults, by testing about 3500 different perturbations for nominal behavior. A local linear approximation of the function linking the TBR and the factors were computed to derive sensitivity indices (SIs), quantifying the impact of each factor on TBR variations. Results: The obtained ranking quantifies importance of factors w.r.t. the others. Factors apparently related to hypoglycemia were correctly placed on the top of the ranking, including systematic (SI=2.05%) and random (SI=1.35%) carb-counting error, hypotreatment dose (SI=-1.21%), insulin bolus time w.r.t. mealtime (SI=1.09%). Conclusions: The obtained SIs allowed to rank behavioral factors based on their impact on TBR. The behavioral factors identified as most influential can be prioritized in patient training

A Mathematical Formula to Determine the Minimum Continuous Glucose Monitoring Duration to Assess Time-in-ranges: Sensitivity Analysis Over the Parameters

In diabetes management, the fraction of time spent with glucose concentration within the physiological range of [70-180] mg/dL, namely time in range (TIR) is often computed by clinicians to assess glycemic control using a continuous glucose monitoring sensor. However, a sufficiently long monitoring period is required to reliably estimate this index. A mathematical equation derived by our group provides the minimum trial duration granting a desired uncertainty around the estimated TIR. The equation involves two parameters, pr and α, related to the population under analysis, which should be set based on the clinician's experience. In this work, we evaluated the sensitivity of the formula to the parameters.Considering two independent datasets, we predicted the uncertainty of TIR estimate for a population, using the parameters of the formula estimated for a different population. We also stressed the robustness of the formula by testing wider ranges of parameters, thus assessing the impact of large errors in the parameters' estimates.Plausible errors on the α estimate impact very slightly on the prediction (relative discrepancy < 5%), thus we suggest using a fixed value for α independently on the population being analyzed. Instead, pr should be adjusted to the TIR expected in the population, considering that errors around 20% result in a relative discrepancy of ~10%.In conclusion, the proposed formula is sufficiently robust to parameters setting and can be used by investigators to determine a suitable duration of the study