Refining Insulin on Board with netIOB for Automated Insulin Delivery.

Automated insulin delivery (AID) systems enhance glucose management by lowering mean glucose level, reducing hyperglycemia, and minimizing hypoglycemia. One feature of most AID systems is that they allow the user to view "insulin on board" (IOB) to help confirm a recent bolus and limit insulin stacking. This metric, along with viewing glucose concentrations from a continuous glucose monitoring system, helps the user understand bolus insulin action and the future "threat" of hypoglycemia. However, the current presentation of IOB in AID systems can be misleading, as it does not reflect true insulin action or automatic, dynamic insulin adjustments. This commentary examines the evolution of IOB from a bolus-specific metric to its contemporary use in AID systems, highlighting its limitations in capturing real-time insulin modulation during varying physiological states.

Point-Counterpoint: The Need for Do-It-Yourself (DIY) Open Source (OS) AID Systems in Type 1 Diabetes Management.

In the last decade, technology developed by people with diabetes and their loved ones has added to the options for diabetes management. One such example is that of automated insulin delivery (AID) algorithms, which were created and shared as open source by people living with type 1 diabetes (T1D) years before commercial systems were first available. Now, numerous options for commercial systems exist in some countries, yet tens of thousands of people with diabetes are still choosing Open-Source AID (OS-AID), previously called "do-it-yourself" (DIY) systems, which are noncommercial versions of these open-source AID systems. In this article, we provide point and counterpoint perspectives regarding (1) safety and efficacy, (2) regulation and support, (3) user choice and flexibility, (4) access and affordability, and (5) patient and provider education, for open source and commercial AID systems. The perspectives reflected here include that of a person living with T1D who uses and has developed OS-AID systems, a physician-researcher based in the United States who conducts clinical trials to support development of commercial AID systems and supports people with diabetes using all types of AID, and an endocrinologist with T1D who uses both systems and treats people with diabetes using all types of AID.

Exocrine Pancreatic Insufficiency Dosing Guidelines for Pancreatic Enzyme Replacement Therapy Vary Widely Across Disease Types.

BACKGROUND: Pancreatic enzyme replacement therapy (PERT) is the standard treatment for exocrine pancreatic insufficiency (EPI). However, many individuals are inadequately treated, with gaps in clinical dosing, guidelines, and tools to aid individual titration. METHODS: A systematic review identified research and guidelines on PERT dosing recommendations across conditions, systematically reviewing and synthesizing total PERT intake, meal/snack guidelines, and changes over time to provide an up-to-date look at the most common doses used in studies and guidelines. RESULTS: This review of 257 articles found wide variability in PERT dosing guidelines within and across conditions. Many patients with EPI are underdosed, with guidelines differing globally and by disease type, and clinician prescribing may also play a role. The most common dosing guidelines focus on starting doses at 40,000-50,000 units of lipase/meal with increases of up to two to three times this amount before pursuing additive therapies. Guidelines and studies typically focus only on fat digestion, and comparison by total daily dose shows underdosing is common. Most PERT studies are on safety and efficacy rather than optimal titration. CONCLUSION: The current guidelines for PERT in EPI demonstrate substantial variability in dosing recommendations, both within and across disease types. This variation highlights the need for further research to optimize PERT dosing and improve patient outcomes. Healthcare providers should consider individualizing PERT dosing based on nutritional status and response to therapy, ensuring regular follow-up with patients for dose titrations with consideration that most guidelines are framed as initial doses rather than upper limits.

Psychosocial Outcomes Among Users and Nonusers of Open-Source Automated Insulin Delivery Systems: Multinational Survey of Adults With Type 1 Diabetes.

BACKGROUND: Emerging research suggests that open-source automated insulin delivery (AID) may reduce diabetes burden and improve sleep quality and quality of life (QoL). However, the evidence is mostly qualitative or uses unvalidated, study-specific, single items. Validated person-reported outcome measures (PROMs) have demonstrated the benefits of other diabetes technologies. The relative lack of research investigating open-source AID using PROMs has been considered a missed opportunity. OBJECTIVE: This study aimed to examine the psychosocial outcomes of adults with type 1 diabetes using and not using open-source AID systems using a comprehensive set of validated PROMs in a real-world, multinational, cross-sectional study. METHODS: Adults with type 1 diabetes completed 8 validated measures of general emotional well-being (5-item World Health Organization Well-Being Index), sleep quality (Pittsburgh Sleep Quality Index), diabetes-specific QoL (modified DAWN Impact of Diabetes Profile), diabetes-specific positive well-being (4-item subscale of the 28-item Well-Being Questionnaire), diabetes treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire), diabetes distress (20-item Problem Areas in Diabetes scale), fear of hypoglycemia (short form of the Hypoglycemia Fear Survey II), and a measure of the impact of COVID-19 on QoL. Independent groups 2-tailed t tests and Mann-Whitney U tests compared PROM scores between adults with type 1 diabetes using and not using open-source AID. An analysis of covariance was used to adjust for potentially confounding variables, including all sociodemographic and clinical characteristics that differed by use of open-source AID. RESULTS: In total, 592 participants were eligible (attempting at least 1 questionnaire), including 451 using open-source AID (mean age 43, SD 13 years; n=189, 41.9% women) and 141 nonusers (mean age 40, SD 13 years; n=90, 63.8% women). Adults using open-source AID reported significantly better general emotional well-being and subjective sleep quality, as well as better diabetes-specific QoL, positive well-being, and treatment satisfaction. They also reported significantly less diabetes distress, fear of hypoglycemia, and perceived less impact of the COVID-19 pandemic on their QoL. All were medium-to-large effects (Cohen d=0.5-1.5). The differences between groups remained significant after adjusting for sociodemographic and clinical characteristics. CONCLUSIONS: Adults with type 1 diabetes using open-source AID report significantly better psychosocial outcomes than those not using these systems, after adjusting for sociodemographic and clinical characteristics. Using validated, quantitative measures, this real-world study corroborates the beneficial psychosocial outcomes described previously in qualitative studies or using unvalidated study-specific items.

Glucose Variability Analysis in Two Large-Scale and Real-World Data Sets of Open-Source Automated Insulin Delivery Systems.

BACKGROUND: Open-source automated insulin delivery (OS-AID) systems combine commercially available insulin pumps and continuous glucose monitors with open-source algorithms to automate insulin dosing for people with insulin-requiring diabetes. Two data sets (OPEN and the OpenAPS Data Commons) contain anonymized OS-AID user data. METHODS: We assessed glycemic variability (GV) outcomes in the OPEN data set and characterized it alongside a comparison to the n = 122 version of the OpenAPS Data Commons. Glucose data are analyzed using an unsupervised machine learning algorithm for clustering, and GV metrics are quantified using statistical tests for distribution comparison. Demographic data are also analyzed quantitatively. RESULTS: The n = 75 OPEN data set contains 36 827 days worth of data. Mean TIR is 82.08% (TOR < 70: 3.66%; TOR > 180: 14.3%). LBGI (P < .05) differs by gender whereas HBGI distributions are similar (P > .05). GV metrics (except TOR < 70, LBGI) show a statistically significant difference (P < .05) between data sets. CONCLUSIONS: Both the OPEN and OpenAPS Data Commons data sets show TOR < 70, TIR, and TOR > 180 within recommended goals, adding additional evidence of real-world efficacy of OS-AID. Future research should evaluate in more detail potential data set differences and relationships between individual patterns of user behaviors and GV outcomes.

Survey of Pancreatic Enzyme Replacement Therapy Dosing Experiences in Adults with Exocrine Pancreatic Insufficiency.

OBJECTIVES: Pancreatic enzyme replacement therapy (PERT) is essential for treating exocrine pancreatic insufficiency (EPI), a condition where the pancreas does not produce adequate enzymes for digestion. This study delves into the real-world experiences of individuals with EPI regarding their PERT usage. METHODS: A study was executed using a tailored survey targeting individuals with EPI. Quantitative data analysis assessed factors such as age, duration of EPI, elastase levels, choice of PERT, perceived effectiveness of titration, and the time taken for effective titration. RESULTS: The study comprised 111 participants, predominantly female (93%) and hailing from North America (79%). Of these, 36.7% had been diagnosed with EPI for 3 or more years. A significant 72% felt they were not consistently consuming adequate enzymes, with only 22% believing their intake was sufficient. There were 44 participants (42%) still in the process of adjusting their enzyme doses. In contrast, 17 participants (16%) took a few weeks, 21 (20%) a few months, 11 (10%) over six months, 10 (9%) more than a year, and 3 (3%) several years for dose adjustment. Regarding enzyme titration advice, 30 participants (29%) received vague guidance, while 22 (21%) found the advice beneficial. CONCLUSIONS: This study underscores the pressing need for enhanced PERT dosing guidance. The insights gleaned spotlight the prevalent undertreatment across the entire EPI demographic, including in those with lesser-studied co-conditions.

A Systematic Review of Exocrine Pancreatic Insufficiency Prevalence and Treatment in Type 1 and Type 2 Diabetes.

Type 1 diabetes and type 2 diabetes have high rates of associated exocrine pancreatic insufficiency (EPI). This review evaluated the current evidence on prevalence and treatment of EPI in type 1 and type 2 diabetes and compared general population prevalence rates of EPI and prevalence of other common gastrointestinal conditions such as celiac disease and gastroparesis based on within-diabetes rates of common gastrointestinal (GI) conditions. Prevalence of EPI in type 1 diabetes ranges from 14% to 77.5% (median 33%), while EPI in type 2 diabetes ranges from 16.8% to 49.2% (median 29%), and where type of diabetes is not specified in studies, ranges from 5.4% to 77%. In studies with control groups of the general population, prevalence of EPI overall in those without diabetes ranged from 4.4% to 18%, median 13%, which is comparable with other estimated general population prevalence rates of EPI (10%-20%). Cumulatively, this suggests there may be significant numbers of people with diabetes with EPI who are undiagnosed. People with diabetes (both type 1 and type 2) who present with gastrointestinal symptoms, such as steatorrhea or changes in stool, bloating, and/or abdominal pain, should be screened for EPI. Both diabetes specialists and gastroenterologists and primary care providers should be aware of the high rates of prevalence of diabetes and EPI and recommend fecal elastase-1 screening for people with diabetes and GI symptoms.

Long-Term Glucose Forecasting for Open-Source Automated Insulin Delivery Systems: A Machine Learning Study with Real-World Variability Analysis.

Glucose forecasting serves as a backbone for several healthcare applications, including real-time insulin dosing in people with diabetes and physical activity optimization. This paper presents a study on the use of machine learning (ML) and deep learning (DL) methods for predicting glucose variability (GV) in individuals with open-source automated insulin delivery systems (AID). A three-stage experimental framework is employed in this work to systematically implement and evaluate ML/DL methods on a large-scale diabetes dataset collected from individuals with open-source AID. The first stage involves data collection, the second stage involves data preparation and exploratory analysis, and the third stage involves developing, fine-tuning, and evaluating ML/DL models. The performance and resource costs of the models are evaluated alongside relative and proportional errors for 17 GV metrics. Evaluation of fine-tuned ML/DL models shows considerable accuracy in glucose forecasting and variability analysis up to 48 h in advance. The average MAE ranges from 2.50 mg/dL for long short-term memory models (LSTM) to 4.94 mg/dL for autoregressive integrated moving average (ARIMA) models, and the RMSE ranges from 3.7 mg/dL for LSTM to 7.67 mg/dL for ARIMA. Model execution time is proportional to the amount of data used for training, with long short-term memory models having the lowest execution time but the highest memory consumption compared to other models. This work successfully incorporates the use of appropriate programming frameworks, concurrency-enhancing tools, and resource and storage cost estimators to encourage the sustainable use of ML/DL in real-world AID systems.

Extended Use of an Open-Source Automated Insulin Delivery System in Children and Adults with Type 1 Diabetes: The 24-Week Continuation Phase Following the CREATE Randomized Controlled Trial.

Aim: To assess long-term efficacy and safety of open-source automated insulin delivery (AID) in children and adults (7-70 years) with type 1 diabetes. Methods: Both arms of a 24-week randomized controlled trial comparing open-source AID (OpenAPS algorithm within a modified version of AndroidAPS, preproduction DANA-i™ insulin pump, Dexcom G6 continuous glucose monitor) with sensor-augmented pump therapy (SAPT), entered a 24-week continuation phase where the SAPT arm (termed SAPT-AID) crossed over to join the open-source AID arm (termed AID-AID). Most participants (69/94) used a preproduction YpsoPump® insulin pump during the continuation phase. Analyses incorporated all 52 weeks of data, and combined between-group and within-subject differences to calculate an overall "treatment effect" of AID versus SAPT. Results: Mean time in range (TIR; 3.9-10 mmol/L [70-180 mg/dL]) was 12.2% higher with AID than SAPT (95% confidence interval [CI] 10.4 to 14.1; P < 0.001). TIR was 56.9% (95% CI 54.2 to 59.6) with SAPT and 69.1% (95% CI 67.1 to 71.1) with AID. The treatment effect did not differ by age (P = 0.39) or insulin pump type (P = 0.37). HbA1c was 5.1 mmol/mol lower [0.5%] with AID (95% CI -6.6 to -3.6; P < 0.001). There were no episodes of diabetic ketoacidosis or severe hypoglycemia with either treatment over the 48 weeks. Six participants (all in SAPT-AID) withdrew: three with hardware issues, two preferred SAPT, and one with infusion-site skin irritation. Conclusion: Further evaluation of the community derived automated insulin delivery (CREATE) trial to 48 weeks confirms that open-source AID is efficacious and safe with different insulin pumps, and demonstrates sustained glycemic improvements without additional safety concerns.

The Need for Diabetes Advance Directives to Support the Care of People With Diabetes.

It is time to adopt an advance directive specific to diabetes management. Research shows that people with diabetes in the hospital are often removed from existing diabetes self-management, resulting in poorer outcomes. Diabetes advance directives, which outline preferred diabetes self-management in scenarios such as hospitalization or outpatient procedures, are key for enabling patients with diabetes to continue successful diabetes management including use of existing diabetes technology. A diabetes advance directive is a new concept for both patients and providers that can improve clinical outcomes and patient-reported outcomes. Given the risk of harm in the absence of such a document, diabetes advance directives can be a useful new tool for patients and providers and to aid in the discussion, care planning, and self-management with diabetes technology.

Barriers to uptake of Open-Source automated insulin delivery Systems: Analysis of socioeconomic factors and perceived challenges of adults with type 1 diabetes from the OPEN survey.

AIMS: Social and technical trends are empowering people with diabetes to co-create or self-develop medical devices and treatments to address their unmet healthcare needs, for example, open-source automated insulin delivery (AID) systems. This study aims to investigate the perceived barriers towards adoption and maintaining of open-source AID systems. METHODS: This is a multinational study based on a cross-sectional, retrospective web-based survey of non-users of open-source AID. Participants (n = 129) with type 1 diabetes from 31 countries were recruited online to elicit their perceived barriers towards building and maintaining of an open-source AID system. RESULTS: Sourcing the necessary components, lack of confidence in one's own technology knowledge and skills, perceived time and energy required to build a system, and fear of losing healthcare provider support appear to be major barriers towards the uptake of open-source AID. CONCLUSIONS: This study identified a range of structural and individual-level barriers to uptake of open-source AID. Some of these individual-level barriers may be overcome over time through the peer support of the DIY online community as well as greater acceptance of open-source innovation among healthcare professionals. The findings have important implications for understanding the possible wider diffusion of open-source diabetes technology solutions in the future.

Open-Source Automated Insulin Delivery in Type 1 Diabetes.

BACKGROUND: Open-source automated insulin delivery (AID) systems are used by many patients with type 1 diabetes. Data are needed on the efficacy and safety of an open-source AID system. METHODS: In this multicenter, open-label, randomized, controlled trial, we assigned patients with type 1 diabetes in a 1:1 ratio to use an open-source AID system or a sensor-augmented insulin pump (control). The patients included both children (defined as 7 to 15 years of age) and adults (defined as 16 to 70 years of age). The AID system was a modified version of AndroidAPS 2.8 (with a standard OpenAPS 0.7.0 algorithm) paired with a preproduction DANA-i insulin pump and Dexcom G6 CGM, which has an Android smartphone application as the user interface. The primary outcome was the percentage of time in the target glucose range of 70 to 180 mg per deciliter (3.9 to 10.0 mmol per liter) between days 155 and 168 (the final 2 weeks of the trial). RESULTS: A total of 97 patients (48 children and 49 adults) underwent randomization (44 to open-source AID and 53 to the control group). At 24 weeks, the mean (±SD) time in the target range increased from 61.2±12.3% to 71.2±12.1% in the AID group and decreased from 57.7±14.3% to 54.5±16.0% in the control group (adjusted difference, 14 percentage points; 95% confidence interval, 9.2 to 18.8; P<0.001), with no treatment effect according to age (P = 0.56). Patients in the AID group spent 3 hours 21 minutes more in the target range per day than those in the control group. No severe hypoglycemia or diabetic ketoacidosis occurred in either group. Two patients in the AID group withdrew from the trial owing to connectivity issues. CONCLUSIONS: In children and adults with type 1 diabetes, the use of an open-source AID system resulted in a significantly higher percentage of time in the target glucose range than the use of a sensor-augmented insulin pump at 24 weeks. (Supported by the Health Research Council of New Zealand; Australian New Zealand Clinical Trials Registry number, ACTRN12620000034932.).

Practical Guidance on Open Source and Commercial Automated Insulin Delivery Systems: A Guide for Healthcare Professionals Supporting People with Insulin-Requiring Diabetes.

As increasing numbers of people with insulin-managed diabetes use automated insulin delivery (AID) systems or seek such technologies, healthcare providers are faced with a steep learning curve. Healthcare providers need to understand how to support these technologies to help inform shared decision making, discussing available options, implementing them in the clinical setting, and guiding users in special situations. At the same time, there is a growing diversity of commercial and open source automated insulin delivery systems that are evolving at a rapid pace. This practical guide seeks to provide a conversational framework for healthcare providers to first understand and then jointly assess AID system options with users and caregivers. Using this framework will help HCPs in learning how to evaluate potential new commercial or open source AID systems, while also providing a guide for conversations to help HCPs to assess the readiness and understanding of users for AID systems. The choice of an AID system is not as simple as whether the system is open source or commercially developed, and indeed there are multiple criteria to assess when choosing an AID system. Most importantly, the choices and preferences of the person living with diabetes should be at the center of any decision around the ideal automated insulin delivery system or any other diabetes technology. This framework highlights issues with AID use that may lead to burnout or perceived failures or may otherwise cause users to abandon the use of AID. It discusses the troubleshooting of basic AID system operation and discusses more advanced topics regarding how to maximize the time spent on AID systems, including how to optimize settings and behaviors for the best possible outcomes with AID technology for people with insulin-requiring diabetes. This practical approach article demonstrates how healthcare providers will benefit from assessing and better understanding all available AID system options to enable them to best support each individual.

Automated insulin delivery (AID) systems are a useful tool for people with insulin-requiring diabetes. AID systems include an insulin pump, continuous glucose monitor (CGM), and an algorithm embedded within the pump or a separate mobile device that can determine and automatically adjust insulin delivery in response to glucose levels. There are now a number of AID systems available, some which are made and distributed by commercial manufacturers and some that are available open source. Both open source and commercially developed automated insulin delivery systems have been proven to be safe and effective. Open source and commercially developed automated insulin delivery systems have also been proven to improve the quality of life of people with insulin-requiring diabetes. The choice of an AID system is not merely whether the system is open source or commercially developed. There are multiple criteria to assess when choosing an AID system: pump, CGM, smartphone connectivity and algorithm capabilities, flexibility of the system overall, and interoperability with connected platforms for real-time data access. Most importantly, the choices and preferences of the person living with diabetes should be at the center of any decision around the ideal automated insulin delivery system or any other diabetes technology. Healthcare providers will benefit from assessing and better understanding all available AID system options to enable them to best support each individual. This practical guide seeks to provide a conversational framework for healthcare providers to first understand and then jointly assess AID system options with users and caregivers.

Glycemic Variability Assessment in Newly Treated Exocrine Pancreatic Insufficiency With Type 1 Diabetes.

BACKGROUND: Thirty-nine percent of people with type 1 diabetes may have lowered pancreatic elastase levels, correlated with exocrine pancreatic insufficiency (EPI or PEI). EPI is treated with oral supplementation of pancreatic enzymes. Little is known about the glycemic impact of pancreatic enzyme replacement therapy (PERT) in people with diabetes. This article demonstrates a method of assessing glycemic variability (GV), glycemic outcomes, and other changes in an individual with type 1 diabetes using open-source automated insulin delivery (AID). METHOD: Macronutrient, PERT intake, and EPI-related symptoms were self-tracked; diabetes data were collected automatically via an open-source AID system. Diabetes data were uploaded via Nightscout to Open Humans and downloaded for analysis alongside self-tracked data (food, PERT). Glycemic outcomes, macronutrients, PERT dosing, and a variety of GV metrics following meals were evaluated for one month before and one month after PERT commencement. Breakfast was assessed independently across both time periods. RESULTS: In an n = 1 individual using an open-source AID, time in range was already above goal and improved further after PERT commencement. Glucose rate of change and excursions >180 mg/dL were reduced; mean high blood glucose index was reduced overall and more so specifically at breakfast following PERT commencement. CONCLUSIONS: GV can aid in assessing response to new-onset medications, as was demonstrated in this article for n = 1 individual with type 1 diabetes (using an open-source AID) after commencing PERT for newly identified EPI. GV may be useful for evaluating the efficacy of new-onset medications for people with insulin-requiring diabetes.

Glioblastoma, IDH-wildtype: A New Association with IgM Paraproteinaemic Neuropathy?

It is well recognized that B-cell clonal disorders such as Waldenstrom's macroglobulinaemia may affect the central nervous system by direct infiltration of malignant B cells (Bing-Neel syndrome). However, there is no recognition in the current literature of a clear link between paraproteinaemia and primary brain tumours such as glioma. We present 3 cases of classical IgM paraproteinaemic neuropathy who developed glioblastoma in the course of their illness following treatment with chemoimmunotherapy (CIT). Due to the progressive symptomatic nature of their neuropathy, all 3 patients were treated with CIT. The patients presented with glioblastoma, IDH-wildtype at 9 months, 5 years, and 6 years following treatment completion. None of the patients had unequivocal evidence of known predisposing factors for glioblastoma. Both disorders are exceedingly rare and the chance of random association is less than one in a million. Potential common pathogenic mechanisms include the influence of paraproteins and circulating lymphoplasmacytic cells on blood-brain permeability and CNS immune micro-environment as well as raised circulating angiogenic cytokines such as vascular endothelial growth factor. In cases with anti-myelin-associated glycoprotein (MAG) antibodies, surface MAG on glial cells may act as a target releasing cells from growth inhibition. We suggest that all glioblastoma cases be screened at diagnosis for serum paraproteins and that such cases be reported to central registries to establish the frequency of the association more accurately.