Risk factors for diabetic kidney disease in adults with longstanding type 1 diabetes: results from the Canadian Study of Longevity in Diabetes

Development and external validation of a machine learning model to predict diabetic nephropathy in T1DM patients in the real-world

AIMS

Studies on machine learning (ML) for the prediction of diabetic nephropathy (DN) in type 1 diabetes mellitus (T1DM) patients are rare. This study focused on the development and external validation of an explainable ML model to predict the risk of DN among individuals with T1DM.

METHODS

This was a retrospective, multicenter study conducted across 19 hospitals in Gansu Province, China (No: 2022-473). In total, 1368 patients were eligible for analysis among 1633 collected T1DM patients from January 2016 to December 2023. Recursive feature elimination using random forest and fivefold cross-validation was conducted to identify key features. Among the 12 initial ML algorithms, the optimal ML model was developed and validated externally in a distinct population, and its predictive outcomes were explained via the SHapley additive exPlanations method, which offered personalized decision insights.

RESULTS

Among the 1368 T1DM patients, 324 had DN. The extreme gradient boosting (XGBoost) model, which achieved optimal performance with an AUC of 83% (95% confidence interval [CI]: 76‒89), was selected to predict the risk of DN among T1DM patients. The DN predictive model included variables such as T1DM duration, postprandial glucose (PPG), systolic blood pressure (SBP), glycated hemoglobin (HbA1c), serum creatinine (Scr) and low-density lipoprotein cholesterol (LDL-C). External validation confirmed the reliability of the model, with an AUC of 76% (95% CI: 70‒82).

CONCLUSIONS

The ML prediction tool has potential for advancing early and precise identification of the risk of DN among T1DM patients. Although successful external validation indicated that the developed model can provide a promising strategy for clinical adoption and help improve patient outcomes through timely and accurate risk assessment, additional prospective data and further validation in diverse populations are necessary.

The Role of Selenium Nanoparticles in Addressing Diabetic Complications: A Comprehensive Study

Diabetes, as an emerging epidemic, has put forward a significant spotlight on the evolving population worldwide grounded upon the remarkable affliction of healthcare along with economical conflict. Various studies suggested that, in modern society, lack of maintenance of a healthy life style leads to the occurrence of diabetes as insulin resistant, later having a damaging effect on the pancreatic β-cells, suggesting various complications. Furthermore, diabetes management is controversial owing to different opinions based on the prevention of complications. For this purpose, nanostructured materials (NSM) like selenium nanoparticles (SeNPs) have proved their efficiency in the therapeutic management of such serious diseases. This review offers an in- -depth idea regarding the pathophysiology, diagnosis and various conventional therapeutics of type 1 and type 2 diabetes, shedding light on Diabetic Nephropathy (DN), a case study of type 1 diabetes. Moreover, this review provides an exhaustive study by highlighting the economic and healthcare burdens associated with diabetes along with the controversies associated with conventional therapeutic management and the promising role of NSM like selenium nanoparticles (SeNPs), as a novel weapon for encountering such fatal diseases.

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

Background

Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter—glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters.

Objective

The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management.

Methods

A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts.

Results

On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices.

Conclusions

Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.

Partners in Crime: Beta-Cells and Autoimmune Responses Complicit in Type 1 Diabetes Pathogenesis

Type 1 diabetes (T1D) is an autoimmune disease characterized by autoreactive T cell-mediated destruction of insulin-producing pancreatic beta-cells. Loss of beta-cells leads to insulin insufficiency and hyperglycemia, with patients eventually requiring lifelong insulin therapy to maintain normal glycemic control. Since T1D has been historically defined as a disease of immune system dysregulation, there has been little focus on the state and response of beta-cells and how they may also contribute to their own demise. Major hurdles to identifying a cure for T1D include a limited understanding of disease etiology and how functional and transcriptional beta-cell heterogeneity may be involved in disease progression. Recent studies indicate that the beta-cell response is not simply a passive aspect of T1D pathogenesis, but rather an interplay between the beta-cell and the immune system actively contributing to disease. Here, we comprehensively review the current literature describing beta-cell vulnerability, heterogeneity, and contributions to pathophysiology of T1D, how these responses are influenced by autoimmunity, and describe pathways that can potentially be exploited to delay T1D.

Physical Activity, Dietary Patterns, and Glycemic Management in Active Individuals with Type 1 Diabetes: An Online Survey

Individuals with type 1 diabetes (T1D) are able to balance their blood glucose levels while engaging in a wide variety of physical activities and sports. However, insulin use forces them to contend with many daily training and performance challenges involved with fine-tuning medication dosing, physical activity levels, and dietary patterns to optimize their participation and performance. The aim of this study was to ascertain which variables related to the diabetes management of physically active individuals with T1D have the greatest impact on overall blood glucose levels (reported as A1C) in a real-world setting. A total of 220 individuals with T1D completed an online survey to self-report information about their glycemic management, physical activity patterns, carbohydrate and dietary intake, use of diabetes technologies, and other variables that impact diabetes management and health. In analyzing many variables affecting glycemic management, the primary significant finding was that A1C values in lower, recommended ranges (<7%) were significantly predicted by a very-low carbohydrate intake dietary pattern, whereas the use of continuous glucose monitoring (CGM) devices had the greatest predictive ability when A1C was above recommended (≥7%). Various aspects of physical activity participation (including type, weekly time, frequency, and intensity) were not significantly associated with A1C for participants in this survey. In conclusion, when individuals with T1D are already physically active, dietary changes and more frequent monitoring of glucose may be most capable of further enhancing glycemic management.

Comparison of Clinical and Social Characteristics of Canadian Youth Living With Type 1 and Type 2 Diabetes

OBJECTIVES

Our aim in this study was to describe the clinical and social characteristics of 2 Canadian cohorts of adolescents with diabetes.

METHODS

Participants from the Improving renal Complications in Adolescents with type 2 diabetes through REsearch (iCARE) study (n=322) and the Early Determinants of Cardio-Renal Disease in Youth With Type 1 Diabetes (n=199) study were compared.

RESULTS

Adolescents were 10 to 18 years of age (mean ± standard deviation: 14.8±2.4 years). The T2DM cohort had a shorter duration of diabetes. Both groups had glycated hemoglobin levels above target. The type 2 diabetes (T2D) cohort was comprised of predominantly Indigenous youth. The type 1 diabetes (T1D) cohort was 58.3% European/Caucasian, with a high proportion (41.7%) of visible minority groups (Afro-Caribbean, Asian/Pacific Islander, Hispanic). The prevalence of obesity, hypertension, left ventricular hypertrophy, albuminuria and hyperfiltration was higher in the T2D cohort. The T1D cohort was more socially and economically advantaged in all 4 dimensions of health inequality.

CONCLUSIONS

There are significant differences in clinical and social characteristics of adolescents with T2D and T1D in Canada. Both have inadequate glycemic control with evidence of onset and progression of diabetes-related complications.