Accuracy of a CGM Sensor in Pediatric Subjects With Type 1 Diabetes. Comparison of Three Insertion Sites: Arm, Abdomen, and Gluteus

Patients with diabetes, especially pediatric ones, sometimes use continuous glucose monitoring (CGM) sensor in different positions from the approved ones. Here we compare the accuracy of Dexcom\uae G5 CGM sensor in three different sites: abdomen, gluteus (both approved) and arm (off-label)

Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population : study protocol

Purpose Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic management through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate an artificial intelligence (AI) based algorithm to detect glycaemic events using ECG signals collected through non-invasive device. Methods This study will enrol T1D paediatric participants who already use CGM. Participants will wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glycaemic measurements driven through ECG variables are the main outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep learning (DL) AI algorithm, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices. Results Data collection is expected to be completed approximately by June 2023. It is expected that sufficient data will be collected to develop and validate the AI algorithm. Conclusion This is a validation study that will perform additional tests on a larger diabetes sample population to validate the previous pilot results that were based on four healthy adults, providing evidence on the reliability of the AI algorithm in detecting glycaemic events in paediatric diabetic patients in free-living conditions

A self-attention deep neural network regressor for real time blood glucose estimation in paediatric population using physiological signals

With the advent of modern digital technology, the physiological signals (such as electrocardiogram) are being acquired from portable wearable devices which are being used for non-invasive chronic disease management (such as Type 1 Diabetes). The diabetes management requires real-time assessment of blood glucose which is cumbersome for paediatric population due to clinical complexity and invasiveness. Therefore, real-time non-invasive blood glucose estimation is now pivotal for effective diabetes management. In this paper, we propose a Self-Attention Deep Neural Network Regressor for real-time non-invasive blood glucose estimation for paediatric population based on automatically extracted beat morphology. The first stage performs Morphological Extractor based on Self-Attention based Long Short-Term Memory driven by Convolutional Neural Network for highlighting local features based on temporal context. The second stage is based on Morphological Regressor driven by multilayer perceptron with dropout and batch normalization to avoid overfitting. We performed feature selection via logit model followed by Spearman’s correlation among features to avoid feature redundancy. We trained as tested our model on publicly available MIT/BIH-Physionet databases and physiological signals acquired from a T1D paediatric population

Ketoacidosis at diagnosis in childhood-onset diabetes and the risk of retinopathy 20years later

Aims To investigate on the relationship between severity of ketoacidosis, an important risk factor for C-peptide preservation, and long-term microvascular complications in childhood-onset type 1 diabetes mellitus (T1DM). Methods 230 childhood-onset diabetic patients (177 pre-pubertal), aged 7.0 \ub1 3.8 years followed for at least 15 years after their diagnosis, were enrolled. Clinical and laboratory data at diagnosis, and C-peptide levels in a subset of patients, were compared with the severity of retinopathy and nephropathy, after a mean of 19.6 \ub1 3.8 years of disease. Digital retinal photographs were taken in all patients, and centrally graded. Repeated measurements of HbA1c and microalbuminuria for the whole duration of diabetes were collected in over half of the cases. Results Out of 230 patients, those with the lowest age at diagnosis had the most severe DKA and clinical conditions (p < 0.05), and lower C-peptide levels (p < 0.0001) at diagnosis. There was a significant relationship between pH and clinical severity (r = - 0.783, p < 0.0001), and between pH and C-peptide levels (r = 0.278, p < 0.05). The severity of ketoacidosis had no relationship with subsequent lifetime HbA1c values and long-term microvascular complications. In logistic regression analysis, the only variables that independently influenced severity of retinopathy were lifetime HbA1c (B = 0.838, p < 0.001), duration of disease (B = 0.208, p < 0.005) and age at diagnosis (B = 0.116, p < 0.05). Conclusions The degree of metabolic derangement at diagnosis is not associated with retinopathy and nephropathy in childhood-onset T1DM. Age at diagnosis seems to be an important variable to be considered when evaluating the long-term effects of residual beta-cell function

Celiac Disease Negatively Influences Lipid Profiles in Youngest Children With Type 1 Diabetes: Effect of the Gluten-Free Diet

The association between low HDL cholesterol (HDL-C) concentrations and increased cardiovascular risk is well established. Low HDL-C levels were found in subjects with type 1 diabetes (T1D) who presented complications and in untreated subjects with celiac disease (CD). The association between TID and CD might therefore enhance this lipid abnormality and accelerate the atherosclerotic process

Diabetic ketoacidosis at the onset of disease during a national awareness campaign: a 2-year observational study in children aged 0-18 years

After a previous survey on the incidence of diabetic ketoacidosis (DKA) at onset of type 1 diabetes in children in 2013-2014 in Italy, we aimed to verify a possible decline in the incidence of DKA at onset during a national prevention campaign