Type 1 diabetes-related distress: Current implications in care

Recent advances in early diagnosis and treatment of T1D with miRNAs

Type 1 diabetes (T1D) is an autoimmune disease characterized by T cell-mediated destruction of pancreatic β-cells and is one of the most common chronic diseases in adults and children. In recent years, the incidence of T1D has been increasing worldwide. Currently, the diagnosis of T1D relies on clinical manifestations and autoantibody detection, with a lack of early predictive biomarkers. MicroRNAs (miRNAs), as crucial post-transcriptional regulatory factors, which are involved in various biological processes, including cell division, proliferation, differentiation, development, and metabolism. Additionally, miRNAs participate in the regulation of inflammatory complications in T1D, and their aberrant expression is closely associated with the disease. The stability of miRNAs makes them potential candidates for early diagnostic biomarkers and therapeutic targets in T1D. This paper discusses the pathogenesis of T1D and the potential applications of miRNAs in early diagnosis and interventional therapy. It provides references for advancing precision diagnosis and personalized treatment strategies through more profound miRNA research in the future.

The Relationship Between Quality of Life, Diabetes Distress, and Metabolic Control in Hungarian Type 1 Diabetic Patients: A Cross-Sectional Study

Background: Diabetes-related distress (DD) significantly impacts self-management and quality of life (QoL) in individuals with type 1 diabetes (T1D). While previous research has established a strong link between DD and glycemic control in type 2 diabetes, the relationship remains less consistent in T1D. Additionally, continuous glucose monitoring (CGM) has been shown to improve glycemic outcomes, yet its effects on self-management and QoL are still debated. This study aimed to examine the relationship between DD, self-management efficacy (SME), and QoL in T1D, incorporating both physiological and behavioral indicators. Furthermore, differences between CGM-users and non-users were investigated. Methods: A cross-sectional study including 108 T1D patients was conducted. Participants completed several validated self-report measures, including the Diabetes Distress Scale (DDS), Diabetes Self-Management Questionnaire (DSMQ), and Audit of Diabetes-Dependent Quality of Life (ADDQoL-19). HbA1c levels and CGM usage were retrieved from medical records. Structural equation modeling (SEM) was used to examine the relationships between DD, self-management, and QoL. Results: Distress level (DDS) had a significant negative effect on SME (β = −0.47, p < 0.001), suggesting that higher distress levels are associated with lower self-management. In contrast, SME showed no significant impact on quality of life (β = 0.03, p = 0.779). However, the relationship between quality of life and distress was significant and negative (β = −0.37, p < 0.001), meaning that higher distress levels are linked to a lower quality of life. No significant differences in DD, SME, HbA1c, or QoL were found among CGM users and non-users. Conclusions: DD significantly impacts self-management and QoL in individuals with T1D. Therefore, incorporating PROs on DD and on behavioral aspects of self-management alongside HbA1c levels in clinical care is essential for optimizing treatment plans and improving physical health outcomes. While CGM technology facilitates glucose regulation, it does not inherently improve QoL, which is more closely linked to distress.

Clinical phenotyping of people living with type 1 diabetes according to their levels of diabetes-related distress: results from the SFDT1 cohort

INTRODUCTION

Type 1 diabetes is burdensome, requiring complex daily management and making people more prone to emotional distress. To better detect diabetes-related distress (DD) and identify at-risk patients, we aimed to provide an in-depth characterization of DD in people with type 1 diabetes.

RESEARCH DESIGN AND METHODS

We included adults with type 1 diabetes from the Suivi en France des personnes avec un Diabète de Type 1 cohort who filled in the Problem Areas in Diabetes questionnaire (PAID ≥40 indicates high DD). Age and sex-adjusted multivariable logistic regression models analyzed individual characteristics, clinical indicators, diabetes-related complications and psychological factors. We further analyzed DD according to six data-driven subdimensions: emotional distress, fear of complications, social distress, eating distress, management distress, and diabetes burnout.

RESULTS

In total, 1220 participants (50.6% female, age 42 years (SD 13.9), diabetes duration 24.7 years (13.6)) had a total mean PAID score of 39.6 (21.7) and 592 (48.5%) reported high DD. Leading subdimensions of DD included fear of complications (50.1 (24.4)) and diabetes burnout (45.9 (24.5)). Females, younger age, social vulnerability, smoking, and the presence of retinopathy were positively associated with high DD (p<0.05). We observed similar DD levels across HbA1c levels and treatment modalities, including automated insulin delivery and continuous glucose monitoring use. Several psychological factors, such as anxiety/depression, poor sleep quality, and treatment burden, were strongly associated with DD (p<0.001).

CONCLUSIONS

We provide a holistic clinical phenotyping approach that enables the identification of determinants and prevalence of DD, overall and according to key DD subdimensions, in a large and diverse population. Our results underscore the importance of developing DD-targeted prevention and intervention strategies focused specifically on high-risk groups and the most impactful distress subdimensions to reduce the impact of type 1 diabetes burden.

TRIAL REGISTRATION NUMBER

NCT04657783.

Development and external validation of an algorithm for self-identification of risk for microvascular complications in patients with type 1 diabetes

AIMS

Microvascular complications, such as diabetic retinopathy (DR), diabetic nephropathy (DN) and diabetic peripheral neuropathy (DPN), are common and serious outcomes of inadequately managed type 1 diabetes (T1D). Timely detection and intervention in these complications are crucial for improving patient outcomes. This study aimed to develop and externally validate machine learning (ML) models for self-identification of microvascular complication risks in T1D population.

MATERIALS AND METHODS

Utilizing data from the Chinese Type 1 Diabetes Comprehensive Care Pathway program, 911 T1D patients and 15 patient self-reported variables were included. Combined with XGBoost algorithm and cross-validation, self-identification models were constructed with 5 variables selected by feature importance ranking. For external validation, an online survey was conducted within a nationwide T1D online community (N = 157). The area under the receiver-operating-characteristic curve (AUROC) was adopted as the main metric to evaluate the model performance. The SHapley Additive exPlanation was utilized for model interpretation.

RESULTS

The prevalence rates of microvascular complications in the development set and external validation set were as follows: DR 7.0% and 12.7% (p = 0.013), DN 5.9% and 3.2% (p = 0.162) and DPN 10.5% and 20.4% (p < 0.001). The models demonstrated the AUROC values of 0.889 for DR, 0.844 for DN and 0.839 for DPN during internal validation. For external validation, the AUROC values achieved 0.762 for DR, 0.718 for DN and 0.721 for DPN.

CONCLUSIONS

ML models, based on self-reported data, have the potential to serve as a self-identification tool, empowering T1D patients to understand their risks outside of hospital settings and encourage early engagement with healthcare services.

Pre-Type 1 Diabetes in Adolescents and Teens: Screening, Nutritional Interventions, Beta-Cell Preservation, and Psychosocial Impacts

Type 1 Diabetes (T1D) is a progressive autoimmune disease often identified in childhood or adolescence, with early stages detectable through pre-diabetic markers such as autoantibodies and subclinical beta-cell dysfunction. The identification of the pre-T1D stage is critical for preventing complications, such as diabetic ketoacidosis, and for enabling timely interventions that may alter disease progression. This review examines the multifaceted approach to managing T1D risk in adolescents and teens, emphasizing early detection, nutritional interventions, beta-cell preservation strategies, and psychosocial support. Screening for T1D-associated autoantibodies offers predictive insight into disease risk, particularly when combined with education and family resources that promote lifestyle adjustments. Although nutritional interventions alone are not capable of preventing T1D, certain lifestyle interventions, such as weight management and specific nutritional choices, have shown the potential to preserve insulin sensitivity, reduce inflammation, and mitigate metabolic strain. Pharmacological strategies, including immune-modulating drugs like teplizumab, alongside emerging regenerative and cell-based therapies, offer the potential to delay disease onset by protecting beta-cell function. The social and psychological impacts of a T1D risk diagnosis are also significant, affecting adolescents' quality of life, family dynamics, and mental health. Supportive interventions, including counseling, cognitive-behavioral therapy (CBT), and group support, are recommended for managing the emotional burden of pre-diabetes. Future directions call for integrating universal or targeted screening programs within schools or primary care, advancing research into nutrition and psychosocial support, and promoting policies that enhance access to preventive resources. Advocacy for the insurance coverage of screening, nutritional counseling, and mental health services is also crucial to support families in managing T1D risk. By addressing these areas, healthcare systems can promote early intervention, improve beta-cell preservation, and support the overall well-being of adolescents at risk of T1D.

Current Challenges in Pancreas and Islet Transplantation: A Scoping Review

Type 1 diabetes mellitus is an autoimmune condition characterized by the destruction of pancreatic β-cells, necessitating insulin therapy to prevent life-threatening complications such as diabetic ketoacidosis. Despite advancements in glucose monitoring and pharmacological treatments, managing this disease remains challenging, often leading to long-term complications and psychological burdens, including diabetes distress. Advanced treatment options, such as whole-pancreas transplantation and islet transplantation, aim to restore insulin production and improve glucose control in selected patients with diabetes. The risk of transplant rejection necessitates immunosuppressive therapy, which increases susceptibility to infections and other adverse effects. Additionally, surgical complications, including infection and bleeding, are significant concerns, particularly for whole-pancreas transplantation. Recently, stem cell-derived therapies for type 1 diabetes have emerged as a promising alternative, offering potential solutions to overcome the limitations of formerly established transplantation methods. The purpose of this scoping review was to: (1) summarize the current evidence on achieved insulin independence following various transplantation methods of insulin-producing cells in patients with type 1 diabetes; (2) compare insulin independence rates among whole-pancreas transplantation, islet cell transplantation, and stem cell transplantation; and (3) identify limitations, challenges and potential future directions associated with these techniques. We systematically searched three databases (PubMed, Scopus, and Web of Science) from inception to November 2024, focusing on English-language, peer-reviewed clinical studies. The search terms used were 'transplantation' AND 'type 1 diabetes' AND 'insulin independence'. Studies were included if they reported on achieved insulin independence, involved more than 10 patients with type 1 diabetes, and had a mean follow-up period of at least one year. Reviewers screened citations and extracted data on transplant type, study population size, follow-up duration, and insulin independence rates. We identified 1380 papers, and after removing duplicates, 705 papers remained for title and abstract screening. A total of 139 English-language papers were retrieved for full-text review, of which 48 studies were included in this review. The findings of this scoping review indicate a growing body of literature on transplantation therapy for type 1 diabetes. However, significant limitations and challenges, like insufficient rates of achieved insulin independence, risks related to immunosuppression, malignant diseases, and ethical issues remain with each of the established techniques, highlighting the need for innovative approaches such as stem cell-derived islet transplantation to promote β-cell regeneration and protection.