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AimsMicrovascular 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 MethodsUtilizing 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.ResultsThe 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.ConclusionsML 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.
AimsMicrovascular 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 MethodsUtilizing 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.ResultsThe 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.ConclusionsML 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.
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.
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