Active clinical trials for "Diabetes Mellitus, Type 1"

The purpose of this single center observational study is to determine the effect of continuous remote continuous glucose monitor (CGM) reporting coupled with a telemedicine intervention (Tele-CGM program) on levels of HbA1C in adults with poorly controlled type 1 or type 2 diabetes. The investigators will follow 200 English and Spanish speaking adults (125 type 2 and 75 type 1 patients) who have an HbA1C >8% over 12 months. The primary analysis will follow the intention-to-treat principle; participants will all be offered the intervention. The primary trial outcome of HbA1c/Glucose Management Indicator (GMI) at 6 and 12 months will be compared from baseline using a linear regression model. The primary independent variable will be the HbA1C from baseline to 6 and 12 months. Patients will serve as their own control. Results will be summarized as group-specific mean, standard deviation (SD) HbA1c, along with a mean treatment difference and 95% confidence interval. Model assumptions including normality and homoscedasticity of residuals will be evaluated; normalizing transformations or rank-based non-parametric procedures will be used as needed. The complete evaluation of HbA1c, including baseline, 6-month and 12 month measurements will be analyzed with mixed effects linear regression, specifying a random participant-level intercept and an unstructured covariance matrix, to accommodate the repeatedly measured data. The secondary trial outcomes of time in range (TIR; CGM glucose levels 70-180) and questionnaires will be compared from baseline to 6 and 12 months using linear regression procedures as detailed above.

The aim of this study is to investigate the efficacy，safety， and tolerability of the recombinant human insulin patch ZJSRM2021 in healthy subjects, type 1diabetes mellitus and type 2diabetes mellitus patients

Treatment of Type 1 diabetes requires daily insulin therapy given by either multiple daily injections or by continuous infusion via a pump. The insulin dose is calculated taking into consideration blood glucose levels, food intake and activity levels, aiming to avoid high and low readings. The Tandem t:slim X2™ insulin pump has recently become available in NHS Scotland and can link with the Dexcom G6 continuous glucose monitoring (CGM) system. The Tandem Control IQ hybrid closed loop system is made up of the Tandem insulin pump communicating wirelessly with the Dexcom G6 CGM. The algorithm within the pump then automatically controls the background (basal) insulin infusion rate. Manually delivered bolus insulin is still required for meals. This system is designed to finely tune insulin delivery and increase the amount of time that glucose is in the target range, reducing hypoglycaemia and hyperglycaemia. Insulin pumps are funded by NHS Scotland, but the Dexcom G6 continuous glucose monitoring system is not, so this hybrid closed loop system is not currently routinely available in NHS Scotland. The purpose of this study is to gain experience of this system and investigate how effective and acceptable the hybrid closed loop system is for people in Scotland. Participants will be provided with the Dexcom G6 CGM device to link to their current pump to create the hybrid closed loop system for the duration of the study. 30 people with Type 1 diabetes who have been using the Tandem pump for at least one month will be invited to participate. The study will include a screening visit, a 30 day run-in period, 52 week treatment period and 12 week follow-up period. Diabetes control will be measured using HbA1c and downloaded information from the hybrid closed loop system. Participants will also complete questionnaires and a reflective diary.

The goal of the study is to examine the impact of an educational video on the rate of breastfeeding in mothers who had gestational diabetes mellitus, and the rate of type-II diabetes mellitus diagnosed postpartum.

This study designed to assess changes in control of Type 1 Diabetes in pubertal adolescents over a two year period. There are two arms/substudies, the first being a longitudinal randomized controlled trial and the second an observational study.

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.

The hypothesis is that humoral and cellular islet-specific responses are an early risk factor for recurrence of autoimmunity and hyperglycemia in simultaneous pancreas-kidney (SPK) recipients independent of alloimmunity. This study will test the hypothesis and will assess their individual and combined predictive value.

The investigators aim to estimate the prevalence of functional ovarian hyperandrogenism [idiopathic hyperandrogenism, idiopatic hirsutism, and polycystic ovary syndrome (PCOS)] in adult patients with type 1 diabetes (T1DM) in an observational cross-sectional study. Study population is comprised of premenopausal adult women with a diagnosis of T1DM, consecutively recruited from a Diabetes outpatient clinic at a tertiary hospital in Spain, Europe.

The purpose of this study is to measure the levels of certain substances (biomarkers) in the body that may indicate the triggers of Type 1 Diabetes, to find a better way to diagnose the disease, as well as to follow its progression.

The main purpose of this study is to evaluate the response of glucagon to insulin-induced low blood sugar after administration of the study drug LY3532226 in participants with type 1 diabetes mellitus (T1DM). The study will also evaluate if LY3532226 helped participants to recover from insulin-induced low blood sugar condition compared to placebo. The study will last approximately 16 weeks excluding screening period.