View clinical trials related to Type1diabetes.
Filter by:This is a single center, single arm non-randomized study of 48-hour CGMIS wear duration incorporating two meal-challenge tests to explore the performance of a single insertion combined glucose-sensing insulin delivery cannula.
In this study, 14 subjects with type 1 diabetes are studied in a randomized crossover study in which the subjects cycle at a fixed intensity at 60% of their maximum oxygen capacity (VO2 max) for 1 hour. Thirty minutes before each cycling test, participants consume a 200 ml beverage, consisting of either: 1) dextrose (20 g), 2) galactose (20 g), 3) lactose (20 g) or 4) water (sweetened). If blood sugar drops below 3.9 mmol/l, glucose infusion is given and blood sugar is kept just above 5 mmol/L. The trial days take place at least 4 days apart.
Type 1 diabetes mellitus (DM) is an autoimmune disease characterized by absolute insulin deficiency, defined as insulin-dependent diabetes mellitus and develops due to autoimmune damage of beta cells in the pancreas. Approximately 425 million people worldwide are diabetic patients, 5% to 10% of whom are T1DM. In the majority of type 1 DM prevention studies, the main aim is to stop beta cell destruction. Primary prevention studies aim to prevent or alter exposure to environmental stimuli before autoimmunity is developed. Secondary prevention studies address interventions in the autoimmune processes that cause betacell destruction. Tertiary prevention studies include interventions to stop or reverse beta-cell destruction after clinical diagnosis of type 1 DM. Despite all technological advances, type1DM has not shown a permanent improvement in metabolic control over the last 5 years. Breast milk provides protection against Type 1 DM through the prevention of diabetogenic infections, delaying exposure to diet antigens including cow's milk, and the ability to produce healthy intestinal microbiota. Xiao et al. (2018) published in Nature, investigated the effect of human milk oligosaccharides on non-obese diabetic rats, where it was found that it improved intestinal flora and insulitis scores and brought the blood glucose level closer to the optimum level. This study is expected to fill the gap in the literature about dose-dependent efficacy and placebo of breastmilk oligosaccharides in diabetic humans. This project will investigate 1) the possible contribution of 2-FL oligosaccharides to the disease's metabolic control 2) their effects on beta-cell preservation in the pancreas 3)their effects on intestinal microbiota 4) whether there is a doseresponse relationship as compared to placebo among type 1 diabetic children. This project is designed as a double-blind randomized placebo-controlled experiment lasting for 36 months. The proposed research population consists of 111 children aged 4-16-year-old who were diagnosed with Type 1 DM at the Department of Pediatric Endocrinology of Ege University. The sample size was calculated as 111 with an effect size of 0.33, an error of 0.05 and a power of 80% using the F-test group (for a numerical variable such as blood glucose) for 3 groups. It is planned that the two intervention arms consist of 37 volunteers and the placebo group of 37 volunteers. In the research, 1.5 g/day of human milk oligosaccharides will be supplemented in the first intervention group and 3 g/day for the second intervention group. The placebo group will receive maltodextrin as a placebo at a dose with no effect on metabolic control. Patients included in the study will be provided human milk oligosaccharides for 3 months and will be under follow-up for 12 months. All variables, mainly C-peptide, HbA1c, immunoglobulins, lymphocytes and faecal analysis will be examined. The project aims to ameliorate the microbiota profile, optimize C-peptide levels, reduce exogenous insulin dependence through the provision of 2-FL from human milk oligosaccharides and develop a more applicable, acceptable and an innovative method in the metabolic control of the disease. It is believed that the psychosocial and economic burdens of the disease will be reduced by increasing the metabolic control of the disease.
The aim of the China Diabetes Type 1 Study (CD1S) is to conduct a nationwide type 1 diabetes (T1D) registry study in patients with T1D and in pediatric adolescent patients with diabetes who had an age of onset <= 20 years. CD1S compromises a retrospective study enrolling inpatients hospitalized from Jan 1st, 2016 to Dec 31, 2021, and a prospective study beginning from the year 2022.
The overall goal of this study is to develop and test a novel method involving ultrasound imaging, in order to detect the development of type 1 diabetes. In this study the investigators will first establish a standard operating procedure for measuring pancreas blood flow speed and volume in the pancreas of human subjects. The investigators will then determine 1) whether these pancreas blood flow factors differ between healthy subjects and those who have recently developed type1 diabetes; and 2) how variable measurements are in healthy subjects and subjects that recently developed type1 diabetes, both between subjects and over time. To address these aims the investigators will perform pancreas ultrasound measurements in each subject using an approved injectable 'bubble' contrast agent that allows measurement of pancreas blood flow. The investigators will compare ultrasound measurement with characteristics of the subject's type 1 diabetes, including genetic factors, glucose levels and other circulating factors, as well as other factors that may influence blood flow in the pancreas independent of type1 diabetes. The successful conclusion of this study will indicate whether measuring pancreas blood flow speed/volume will be helpful in monitoring whether type1 diabetes will emerge and thus will allow a large scale study to answer this question.
This RCT aims to improve T1D care in East African children and young adults by testing the hypothesis that enabling patients to continuously monitor glucose levels with flash CGM technology will improve glucose time-in-range (glucose level 70-180 mg/dl). A second primary endpoint is to perform a cost analysis on flash glucose monitoring compared to 3x/day SMBG, to determine whether this technology is cost-effective in the setting of a less-resourced nation. After a 2 week assessment with blinded CGM when a potential subject's ability to wear CGM is confirmed, subjects will be enrolled for 12 months in randomized, open label study, with a primary endpoint measurement at 6 months. All subjects will receive monthly diabetes self-management education. For the first six months, months 1-6: - Half of patients (n=90) will be randomized to an unblinded FreeStyle Libre 2 CGM.They and their care providers will be able to continuously see their CGM glucose levels to assist in insulin adjustment. - Half of patients (n=90) will be given sufficient test strips for 3x daily SMBG while wearing blinded CGM (control group). Neither they nor their care providers will be able to see their CGM glucose levels (the blinded CGM is simply for outcome measurement, not an intervention). As per usual clinical practice, only the SMBG glucose levels will be available to assist in insulin adjustment. - The change between baseline to 6 months in CGM-derived glucose percent time-in- range will be compared between groups (first primary study endpoint). For the second six months, months 7-12: - The control group will switch to unblinded CGM months 7-12 (their data months 7-12 months will be compared to their data months 1-6 as part of the primary endpoint assessment). - The patients who wore the unblinded CGM months 1-6 will continue for another 6 months to assess the impact of wearing the CGM for 12 continuous months (a secondary endpoint). Once the clinical portion of the study is complete, study investigators who are health economists from the Uganda Ministry of Health will perform a costs analysis (second primary endpoint).
Pilot study to evaluate effects of an experimental additional remote digital care (TELEDUC-DIAB) to adolescents with poorly controlled type 1 diabetes using digital monitoring platform and educative app
The Diabetes registry for biomarker research Graz is a prospective cohort-study including subjects with type 1 and type 2 diabetes, rare types of diabetes, obesity and dyslipidemia, aiming to collect data, blood and urine samples of all subjects on an annual basis.
Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control and incorrect Insulin administration. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic control 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 a deep learning algorithm to detect glycaemic events using electrocardiogram (ECG) signals collected through non-invasive device. This observational single-arm study will enrol participants with T1D aged less than 18 years old who already use CGM device. Participants will wear an additional non-invasive wearable device, for recording physiological data (e.g. ECG, breathing waveform, 3-axis acceleration) for three days. ECG variables (e.g. heart rate variability features), respiratory rate, physical activity, posture and glycaemic measurements driven through ECG variables and other physiological signals (e.g. the frequency of hypo or hyperglycaemic events, the time spent in hypo- or hyperglycaemia and the time in range) are the main outcomes. A quality-of-life questionnaire will be administered to collect secondary outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep-learning artificial intelligence (AI) algorithm developed during the pilot study, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices. This study is a validation study that will carry out additional tests on a larger diabetes sample population, to validate the previous promising pilot results that were based on four healthy adult subjects. Therefore, this study will provide evidence on the reliability of the deep-learning artificial intelligence algorithms investigators developed, in detecting glycaemic events in paediatric diabetic patients in free-living conditions. Additionally, this study aims to develop the generalized AI model for the automated glycaemic events detection on real-time ECG.
In clinical practice, women living with type 1 diabetes frequently report that insulin requirements change across the menstrual cycle. Consequently, glycemic fluctuations are observed. This phenomenon could be explained by a decrease in insulin sensitivity during the second half of the menstrual cycle (luteal phase). Overall, despite an important proportion of women reporting glycemic and/or insulin variations across the menstrual cycle, studies to date have involved small sample sizes, and have had inconsistent results. The objective of this study will be to study glycemic fluctuations across the menstrual cycle using CGM data, alongside insulin data, in a large sample of women.