Journal of Diabetes Science and Technology最新文献

Introduction: Diabetes mellitus (DM) is a chronic disease with high morbidity and mortality, and glycemic control is key to avoiding complications. Technological innovations have led to the development of new tools to help patients with DM manage their condition.

Objective: This consensus assesses the current perspective of physicians on the potential benefits of using smart insulin pens in the glycemic control of patients with type 1 diabetes (DM1) in Spain.

Methods: The Delphi technique was used by 110 physicians who were experts in managing patients with DM1. The questionnaire consisted of 94 questions.

Results: The consensus obtained was 95.74%. The experts recommended using the ambulatory glucose profile report and the different time-in-range (TIR) metrics to assess poor glycemic control. Between 31% and 65% of patients had TIR values less than 70% and were diagnosed based on glycosylated hemoglobin values. They believed that less than 10% of patients needed to remember to administer the basal insulin dose and between 10% and 30% needed to remember the prandial insulin dose.

Conclusions: The perception of physicians in their usual practice leads them to recommend the use of ambulatory glucose profile and time in range for glycemic control. Forgetting to administer insulin is a very common problem and the actual occurrence rate does not correspond with clinicians' perceptions. Technological improvements and the use of smart insulin pens can increase treatment adherence, strengthen the doctor-patient relationship, and help improve patients' education and quality of life.

Diabetes technologies, including continuous glucose monitors, insulin pumps, and automated insulin delivery systems offer the possibility of improving glycemic outcomes, including reduced hemoglobin A1c, increased time in range, and reduced hypoglycemia. Given the rapid expansion in the use of diabetes technology over the past few years, and touted promise of these devices for improving both clinical and psychosocial outcomes, it is critically important to understand issues in technology adoption, equity in access, maintaining long-term usage, opportunities for expanded device benefit, and limitations of the existing evidence base. We provide a brief overview of the status of the literature-with a focus on psychosocial outcomes-and provide recommendations for future work and considerations in clinical applications. Despite the wealth of the existing literature exploring psychosocial outcomes, there is substantial room to expand our current knowledge base to more comprehensively address reasons for differential effects, with increased attention to issues of health equity and data harmonization around patient-reported outcomes.

Background: The GOLD trial demonstrated that continuous glucose monitoring (CGM) in people with type 1 diabetes (T1D) managed with multiple daily insulin injections (MDI) improved not only glucose control but also overall well-being and treatment satisfaction. This analysis investigated which factors contributed to improved well-being and treatment satisfaction with CGM.

Methods: The GOLD trial was a randomized crossover trial comparing CGM versus self-monitored blood glucose (SMBG) over 16 months. Endpoints included well-being measured by the World Health Organization-Five Well-Being Index (WHO-5) and treatment satisfaction by the Diabetes Treatment Satisfaction Questionnaire (DTSQ) as well as glucose metrics. Multivariable R²-decomposition was used to understand which variables contributed most to treatment satisfaction.

Results: A total of 139 participants were included. Multivariable analyses revealed that increased convenience and flexibility contributed to 60% (95% confidence interval [CI] = 50%-69%) of the improvement in treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire change version [DTSQc]) observed with CGM, whereas perceived effects on hypoglycemia and hyperglycemia only contributed to 6% (95% CI = 2%-11%) of improvements. Significant improvements in well-being (WHO-5) by CGM were observed for the following: feeling cheerful (P = .025), calm and relaxed (P = .024), being active (P = .046), and waking up fresh and rested (P = .044). HbA1c reductions and increased time in range (TIR) were associated with increased treatment satisfaction, whereas glycemic variability was not. HbA1c reduction showed also an association with increased well-being and increased TIR with less diabetes-related distress.

Conclusions: While CGM improves glucose control in people with T1D on MDI, increased convenience and flexibility through CGM is of even greater importance for treatment satisfaction and patient well-being. These CGM-mediated effects should be taken into account when considering CGM initiation.

Background: The recent availability of high-quality data from clinical trials, together with machine learning (ML) techniques, presents exciting opportunities for developing prediction models for clinical outcomes.

Methods: As a proof-of-concept, we translated a hypoglycemia risk model derived from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study into the HypoHazardScore, a risk assessment tool applicable to electronic health record (EHR) data. To assess its performance, we conducted a 16-week clinical study at the University of Minnesota where participants (N = 40) with type 2 diabetes mellitus (T2DM) had hypoglycemia assessed prospectively by continuous glucose monitoring (CGM).

Results: The HypoHazardScore combines 16 risk factors commonly found within the EHR. The HypoHazardScore successfully predicted (area under the curve [AUC] = 0.723) whether participants experienced least one CGM-assessed hypoglycemic event (glucose <54 mg/dL for ≥15 minutes from two CGMs) while significantly correlating with frequency of CGM-assessed hypoglycemic events (r = 0.38) and percent time experiencing CGM-assessed hypoglycemia (r = 0.39). Compared to participants with a low HypoHazardScore (N = 19, score <4, median score of 4), participants with a high HypoHazardScore (N = 21, score ≥4) had more frequent CGM-assessed hypoglycemic events (high: 1.6 ± 2.2 events/week; low: 0.3 ± 0.5 events/week) and experienced more CGM-assessed hypoglycemia (high: 1.4% ± 2.0%; low: 0.2% ± 0.4% time) during the 16-week follow-up.

Conclusions: We demonstrated that a hypoglycemia risk model can be successfully adapted from the ACCORD data to the EHR, with validation by a prospective study using CGM-assessed hypoglycemia. The HypoHazardScore represents a significant advancement toward implementing an EHR-based decision support system that can help reduce hypoglycemia in patients with T2DM.

Introduction: Each year, people with diabetes and their insurers or governments spend billions of dollars on blood glucose monitors and their associated components. These monitors have evolved substantially since their introduction in the 1970s, and manufacturers frequently protect original medical devices and their modifications by applying for and obtaining patent protection.

Research design and methods: We tracked the product iterations of five widely used blood glucose monitors-manufactured by LifeScan, Dexcom, Abbott, Roche, and Trividia-from information published by the U.S. Food and Drug Administration (FDA), and extracted relevant U.S. patents.

Results: We found 384 products made by the five manufacturers of interest, including 130 devices cleared through the 510(k) pathway, 251 approved via the premarket approval (PMA) pathway or via PMA supplements, and three for which de novo requests were granted. We identified 8095 patents potentially relevant to these devices, 2469 (31%) of which were likely to have expired by July 2021.

Conclusions: Manufacturers of blood glucose monitoring systems frequently modified their devices and obtained patent protection related to these device modifications. The therapeutic value of these new modifications should be critically evaluated and balanced against their additional cost. Older glucose monitoring devices that were marketed in decades past are now in the public domain and no longer protected by patents. Newer devices will join them as their patents expire. Increased demand from people with diabetes and the health care system for older, off-patent devices would provide an incentive for the medical device industry to make these devices more widely available, enabling good care at lower cost when such devices are substantially equivalent in effectiveness and safety. In turn, availability and awareness of older, off-patent devices could help stimulate such demand.

Background: One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH.

Methods: We present the design and development of binary classifiers to predict NH (blood glucose levels occurring below 70 mg/dL). Using data collected from a 6-month study of 37 adult participants with T1D under free-living conditions, we extract daytime features from continuous glucose monitor (CGM) sensors, administered insulin, meal, and physical activity information. We use these features to train and test the performance of two ML algorithms: Random Forests (RF) and Support Vector Machines (SVMs). We further evaluate our model in an external population of 20 adults with T1D using MDI insulin therapy and wearing CGM and flash glucose monitoring sensors for two periods of eight weeks each.

Results: At population-level, SVM outperforms RF algorithm with a receiver operating characteristic-area under curve (ROC-AUC) of 79.36% (95% CI: 76.86%, 81.86%). The proposed SVM model generalizes well in an unseen population (ROC-AUC = 77.06%), as well as between the two different glucose sensors (ROC-AUC = 77.74%).

Conclusions: Our model shows state-of-the-art performance, generalizability, and robustness in sensor devices from different manufacturers. We believe it is a potential viable approach to inform people with T1D about their risk of NH before it occurs.

The use of artificial intelligence (AI) in diabetes management is emerging as a promising solution to improve the monitoring and personalization of therapies. However, the integration of such technologies in the clinical setting poses significant challenges related to safety, security, and compliance with sensitive patient data, as well as the potential direct consequences on patient health. This article provides guidance for developers and researchers on identifying and addressing these safety, security, and compliance challenges in AI systems for diabetes management. We emphasize the role of explainable AI (xAI) systems as the foundational strategy for ensuring security and compliance, fostering user trust, and informed clinical decision-making which is paramount in diabetes care solutions. The article examines both the technical and regulatory dimensions essential for developing explainable applications in this field. Technically, we demonstrate how understanding the lifecycle phases of AI systems aids in constructing xAI frameworks while addressing security concerns and implementing risk mitigation strategies at each stage. In addition, from a regulatory perspective, we analyze key Governance, Risk, and Compliance (GRC) standards established by entities, such as the Food and Drug Administration (FDA), providing specific guidelines to ensure safety, efficacy, and ethical integrity in AI-enabled diabetes care applications. By addressing these interconnected aspects, this article aims to deliver actionable insights and methodologies for developing trustworthy AI-enabled diabetes care solutions while ensuring safety, efficacy, and compliance with ethical standards to enhance patient engagement and improve clinical outcomes.

Background: Recent advances in diabetes care and technology, such as real-time continuous glucose monitoring, can help people live more freely, with more flexibility and fewer constraints, thereby enhancing quality of life (QOL). To date, there has been no validated means for measuring this key psychological dimension. We developed the Diabetes Constraints Scale (DCS) to assess perceived constraints pertaining to diabetes self-management.

Methods: Six items were developed from qualitative interviews (20 adults with type 2 diabetes [T2D], 8 adults with type 1 diabetes [T1D]). Items were included in one study with T2D adults (N = 458) and one with T1D adults (N = 574). Scale reliability was analyzed for each study using exploratory factor analyses. Associations between DCS and key psychosocial and glycemic variables were assessed.

Results: In both studies, factor analyses revealed a single factor, with adequate internal reliability (Cronbach's alpha >.80). Both studies demonstrated significant associations in the expected direction between DCS and overall well-being, diabetes-specific QOL, and diabetes distress (all P < .001). In both studies, DCS was positively linked with the number of missed insulin boluses and the frequency of severe hypoglycemic episodes (T1D both P < .001; T2D both P < .005) and-in the T1D group only-with HbA_1c (P < .001).

Conclusions: The DCS is a reliable and valid method to determine the degree to which adults with diabetes feel constrained or limited by the disease. It may serve as a useful tool for assessing how new interventions can help individuals feel freer in the face of the demands of diabetes.

Introduction: Two phase 3 randomized controlled studies (ADJUNCT ONE (Clinicaltrials.gov: NCT01836523), ADJUNCT TWO (Clinicaltrials.gov: NCT02098395)) evaluated liraglutide (1.8, 1.2 or 0.6 mg) vs placebo in participants with type 1 diabetes (T1D) as an adjunct to insulin therapy. This paper aims to improve our understanding of the potential mechanisms leading to premature discontinuation of this treatment regimen.

Methods: Post hoc comparisons were conducted on baseline characteristics and adverse event (AE) rates of participants completing and not completing the ADJUNCT studies due to AEs/lack of tolerance using summary tables and variance analysis.

Results: Non-completers (liraglutide and placebo combined) had lower baseline body mass index (BMI) (ADJUNCT ONE: 27.8 kg/m² vs 29.8 kg/m², P < .0001; ADJUNCT TWO: 26.3 kg/m² vs 29.2 kg/m², P < .0001), longer duration of T1D (25.8 years vs 21.0 years, P < .0001; 24.1 years vs 21.0 years, P = .04), lower daily insulin doses by continuous infusion (46.4 U vs 57.3 U, P = .01; 40.9 U vs 57.4 U, P = .12) or multiple injections (58.4 U vs 68.5 U, P = .006; 56.0 U vs 65.8 U, P =.03) and a higher proportion of participants with undetectable C-peptide (91.5% vs 81.3%; 87.0% vs 84.9%) compared to completers. When analyzed by treatment group, only duration of T1D and C-peptide differed between completers and non-completers among liraglutide (and not placebo) participants. The AE rates were higher for non-completers.

Conclusion: Individuals with longer-standing T1D and low levels of C-peptide at baseline were more likely to discontinue adjunctive liraglutide treatment due to AEs/lack of tolerance than individuals with residual insulin production. Lower BMI predicted a greater likelihood of non-completion for the included participants, regardless of treatment. These new findings may be relevant for clinical practice.

Background: Youth with type 1 diabetes (T1D) and public insurance have lower diabetes technology use. This pilot study assessed the feasibility of a program to support continuous glucose monitor (CGM) use with remote patient monitoring (RPM) to improve glycemia for youth with established T1D and public insurance.

Methods: From August 2020 to June 2023, we provided CGM with RPM support via patient portal messaging for youth with established T1D on public insurance with challenges obtaining consistent CGM supplies. We prospectively collected hemoglobin A_1c (HbA_1c), standard CGM metrics, and diabetes technology use over 12 months.

Results: The cohort included 91 youths with median age at enrollment 14.7 years, duration of diabetes 4.4 years, 33% non-English speakers, and 44% Hispanic. Continuous glucose monitor data were consistently available (≥70%) in 23% of the participants. For the 64% of participants with paired HbA_1c values at enrollment and study end, the median HbA_1c decreased from 9.8% to 9.0% (P < .001). Insulin pump users increased from 31 to 48 and automated insulin delivery users increased from 11 to 38.

Conclusions: We established a program to support CGM use in youth with T1D and barriers to consistent CGM supplies, offering lessons for other clinics to address disparities with team-based, algorithm-enabled, remote T1D care. This real-world pilot and feasibility study noted challenges with low levels of protocol adherence and obtaining complete data in this cohort. Future iterations of the program should explore RPM communication methods that better align with this population's preferences to increase participant engagement.