Comprehensive Telehealth Model to Support Diabetes Self-Management

Distinct Roles of Common Genetic Variants and Their Contributions to Diabetes: MODY and Uncontrolled T2DM

Type 2 diabetes mellitus (T2DM) encompasses a range of clinical manifestations, with uncontrolled diabetes leading to progressive or irreversible damage to various organs. Numerous genes associated with monogenic diabetes, exhibiting classical patterns of inheritance (autosomal dominant or recessive), have been identified. Additionally, genes involved in complex diabetes, which interact with environmental factors to trigger the disease, have also been discovered. These genetic findings have raised hopes that genetic testing could enhance diagnostics, disease surveillance, treatment selection, and family counseling. However, the accurate interpretation of genetic data remains a significant challenge, as variants may not always be definitively classified as either benign or pathogenic. Research to date, however, indicates that periodic reevaluation of genetic variants in diabetes has led to more consistent findings, with biases being steadily eliminated. This has improved the interpretation of variants across diverse ethnicities. Clinical studies suggest that genetic risk information may motivate patients to adopt behaviors that promote the prevention or management of T2DM. Given that the clinical features of certain monogenic diabetes types overlap with T2DM, and considering the significant role of genetic variants in diabetes, healthcare providers caring for prediabetic patients should consider genetic testing as part of the diagnostic process. This review summarizes current knowledge of the most common genetic variants associated with T2DM, explores novel therapeutic targets, and discusses recent advancements in the pharmaceutical management of uncontrolled T2DM.

Patient-Reported Outcomes Improve with a Virtual Diabetes Care Model that Includes Continuous Glucose Monitoring

Background: The objective was to examine patient-reported outcomes (PROs) associated with access to a virtual clinic model for diabetes care. Methods: Adults with diabetes (N = 234) received virtual care, including support for continuous glucose monitoring (CGM) over a 6-month study period. Care was led by a Certified Diabetes Care and Education Specialist and focused on optimizing self-management skills and response to glucose values observed on CGM. After 6 months of CGM use and access to diabetes education, participants could opt in to another 6 months of follow-up with access to the virtual care team. Participants completed PRO surveys and had health and glycemic measures collected at baseline, 3, 6, and 12 months. Results: Participants with type 1 diabetes (N = 160) were 44 ± 14 years and had mean baseline HbA1c of 61 mmol/mol (7.7%). Participants with type 2 diabetes (N = 74) were 52 ± 12 years and had mean baseline HbA1c of 66 mmol/mol (8.2%). Compared with baseline levels, at 6 months participants experienced less depression, diabetes distress, and hypoglycemic fears while also experiencing greater satisfaction with glucose monitoring, diabetes technology and specifically with CGM, and confidence for managing hypoglycemic (p < 0.05). For participants with type 1 diabetes, more time in the target range for glucose levels (70-180 mg/dL) was associated with less depression, diabetes distress, and hypoglycemic fears. Conclusions: PROs improved for adults with diabetes utilizing virtual diabetes care, including support for CGM use. Paired with the glycemic improvements observed in this virtual clinic study, there were robust benefits on the quality of life of adults with diabetes. ClinicalTrials.gov Identifier: NCT04765358.

Older Adults Benefit From Virtual Support for Continuous Glucose Monitor Use But Require Longer Visits

BACKGROUND

Older adults may be less comfortable with continuous glucose monitoring (CGM) technology or require additional education to support use. The Virtual Diabetes Specialty Clinic study provided the opportunity to understand glycemic outcomes and support needed for older versus younger adults living with diabetes and using CGM.

METHODS

Prospective, virtual study of adults with type 1 diabetes (T1D, N = 160) or type 2 diabetes (T2D, N = 74) using basal-bolus insulin injections or insulin pump therapy. Remote CGM diabetes education (3 scheduled visits over 1 month) was provided by Certified Diabetes Care and Education Specialists with additional visits as needed. CGM-measured glycemic metrics, HbA1c and visit duration were evaluated by age (<40, 40-64 and ≥65 years).

RESULTS

Median CGM use was ≥95% in all age groups. From baseline to 6 months, time 70 to 180 mg/dL improved from 45% ± 22 to 57% ± 16%; 50 ± 25 to 65 ± 18%; and 60 ± 28 to 69% ± 18% in the <40, 40-64, and ≥65-year groups, respectively (<40 vs 40-64 years P = 0.006). Corresponding values for HbA1c were 8.0% ± 1.6 to 7.3% ± 1.0%; 7.9 ± 1.6 to 7.0 ± 1.0%; and 7.4 ± 1.4 to 7.1% ± 0.9% (all P > 0.05). Visit duration was 41 min longer for ages ≥65 versus <40 years (P = 0.001).

CONCLUSIONS

Adults with diabetes experience glycemic benefit after remote CGM use training, but training time for those >65 years is longer compared with younger adults. Addressing individual training-related needs, including needs that may vary by age, should be considered.

The Type of Patient Training Does Not Impact Outcomes in the First 90 Days of Automated Insulin Delivery Use

Background: Youth starting Omnipod 5 (OP5) can onboard with a diabetes educator or self-start with support from online, industry-provided educational modules. We compared glycemic control and pump interaction by training type among youth initiating OP5. Methods: This retrospective review included 297 youth with type 1 diabetes (T1D) aged <22 years initiating OP5. We analyzed baseline continuous glucose monitor (CGM) data and pump and CGM data from the first 90 days of OP5 use. Multilevel mixed-effects regression assessed for changes in time in range (TIR) from baseline to 90 days by training type. Results: Of youth initiating OP5, 42.4% trained with a diabetes educator and 57.6% self-started. At baseline, self-starters had a longer T1D duration (5.0 (2.6,7.9) vs. 2.5 (1.3, 5.5) years, P = 0.001), more time <54 mg/dL (0.3% (0.1,1) vs. 0.15% (0,1), P = 0.01), and a higher coefficient of variation (40.2% (37, 44.4) vs. 38.7% (34.4, 42.4), P = 0.004). After 90 days of OP5 use, groups did not differ in time in automated mode or boluses per day. In a longitudinal model, after adjusting for baseline TIR and T1D duration, 90-day TIR was 10.5%-points higher (CI: 9.2-11.8, P < 0.0001), positively associated with baseline TIR (β = 0.82, CI: 0.78-0.85, P < 0.0001), and 1.1%-points greater among self-starters (CI: 0.06-2.2; P = 0.04). Conclusions: After 90 days of OP5 use, glycemic control and pump interactions were minimally different between youth who self-started and those who trained with a diabetes educator. For youth at a tertiary care center previously using an Omnipod system, online educational modules offered by industry provide sufficient training for use.

A Systematic Review of Telehealth Applications in Endocrinology

Introduction

The prevalence of telehealth has witnessed a significant increase in various medical domains, especially in endocrinology. Telehealth brings about considerable advantages for both patients and health care professionals. However, despite these positive aspects, the growing prominence of telehealth is accompanied by certain challenges. This systematic review aims to assess the role of telehealth in endocrinology, including its applications, effectiveness, challenges, and implications for patient care.

Methods

This study involved a thorough search using comprehensive techniques across databases such as PubMed/Medline, Embase, and Scopus. The studies were selected for a tailored adaptation of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to enhance the clarity of our systematic review's reporting.

Results

This systematic review explores global telemedicine applications in endocrinology. Addressing various endocrine conditions, interventions utilize technology tools such as smartphones and applications, offering multifaceted utility from education and data gathering to screening and treatment. Notably, these interventions demonstrate adaptability during the COVID-19 pandemic. Positive outcomes include enhanced patient education, disease self-management, reduced complications, and improved glycemic control. However, drawbacks include the need for technical proficiency, perceived lower care quality, and potential privacy risks. These nuanced findings contribute to the discourse on telemedicine efficacy and limitations.

Conclusion

In conclusion, telehealth holds significant potential in transforming endocrine care. While there are challenges to its implementation, the benefits it offers underscore its value as a health care delivery model.

What Is the Tech Missing? Nutrition Reporting in Type 1 Diabetes

INTRODUCTION

Type 1 Diabetes (T1D) presents self-management challenges, requiring an additional 180 daily decisions to regulate blood glucose (BG) levels. Despite the potential, T1D-focused applications have a 43% attrition rate. This work delves into the willingness of people living with T1D (PwT1D) to use technology.

METHOD

An online questionnaire investigated the current practices for carbohydrate estimation, nutritional tracking, and attitudes towards technology engagement, along with hypothetical scenarios and preferences regarding technology use.

RESULTS

Thirty-nine responses were collected from PwT1D (n = 33) and caregivers (n = 6). Nutrition reporting preferences varied, with 50% favoring 'type and scroll' while 30% preferred meal photographing. Concerning the timing of reporting, 33% reported before meals, 55% after, and 12% at a later time. Improved Time in Range (TIR) was a strong motivator for app use, with 78% expressing readiness to adjust insulin doses based on app suggestions for optimizing TIR. Meal descriptions varied; a single word was used in 42% of cases, 23% used a simple description (i.e., "Sunday dinner"), 30% included portion sizes, and 8% provided full recipes.

CONCLUSION

PwT1D shows interest in using technology to reduce the diabetes burden when it leads to an improved TIR. For such technology to be ecologically valid, it needs to strike a balance between requiring minimal user input and providing significant data, such as meal tags, to ensure accurate blood glucose management without overwhelming users with reporting tasks.

Towards a Remote Patient Monitoring Platform for Comprehensive Risk Evaluations for People with Diabetic Foot Ulcers

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stays and amputation. We suggest a remote monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, characteristics of the wound, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0-10); categorized as low, medium, and high risk for unfavorable outcomes; and visually represented using color-coded radar plots. The initial results of the case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

The role of real-time continuous glucose monitoring in diabetes management and how it should link to integrated personalized diabetes management

Diabetes is a complex metabolic condition that demands tailored, individualized approaches for effective management. Real-time continuous glucose monitoring (rtCGM) systems have improved in terms of design, usability and accuracy over the years and play a pivotal role in the delivery of integrated personalized diabetes management (iPDM). iPDM is a comprehensive multidisciplinary approach that combines individualized care strategies utilizing technologies and interventions and encourages the active involvement of the person with diabetes in the care provided. The use of stand-alone rtCGM and its integration with other diabetes technologies, such as hybrid automated insulin delivery, have enabled improved glycaemic and quality of life outcomes for people with diabetes. As the uptake of rtCGM and associated technologies is increasing and becoming the standard of care for people with diabetes, it is important that efforts are focused on associated goals such as reducing health inequalities in terms of access, aligning structured education with rtCGM usage, choosing the right technology based on needs and preferences, and minimizing burden while aiming for optimal glucose outcomes. Utilizing rtCGM in other settings than outpatients and in diabetes cohorts beyond type 1 and type 2 diabetes needs further exploration. This review aims to provide an overview of the role of rtCGM and how best to link rtCGM to iPDM, highlighting its role in enhancing personalized treatment strategies.

Removing Barriers, Bridging the Gap, and the Changing Role of the Health Care Professional with Automated Insulin Delivery Systems

As all people with type 1 diabetes (T1D) and some with type 2 diabetes (T2D) require insulin, there is a need to develop management methods that not only achieve glycemic targets but also reduce the burden of living with diabetes. After insulin pumps and continuous glucose monitors, the next step in the evolution of diabetes technology is automated insulin delivery (AID) systems, which have transformed intensive insulin management over the past decade, as these systems address the shortcomings of previous management options. However, AID use remains fairly limited, and access represents a major barrier to use for many people with diabetes, despite these systems being standard of care. Therefore, the future of AID will necessitate addressing barriers related to social determinants of health, finances, and an expansion of the number and type of health care professionals (HCPs) prescribing AID systems. These crucial steps will be essential to ensure that everyone with intensively managed diabetes can use AID systems. The impact of implementing these changes will create a shift in the future of diabetes care that will result in achievement of more targeted glycemia and psychosocial outcomes for all people with diabetes and an expansion of the role of all HCPs in AID-related diabetes care. Even more importantly, by addressing social determinants of health and clinical inertia related to AID, the field can address disparities in outcomes across countries, race, gender, socioeconomic status, and insurance status. Furthermore, the increased use of AID system will provide more time during appointments for a shift in the discussion away from fine tuning insulin dosing and toward a focus on more topics related to behavior and conversations about general health. This will include psychosocial outcomes, and quality of life. In addition, these changes can hopefully allow for time to discuss more general issues, such as cardiovascular health, obesity prevention, diabetes-related complications, and other health-related concerns.