Active clinical trials for "Diabetes Mellitus"

The aim of this study is to investigate if diabetes, obesity and metabolic syndrome affects disease presentation and severity of malaria in adults in a hospital setting in Cameroon.

This is a single site, double-blind, placebo-controlled, crossover trial to quantify the effects of combination adjunctive therapy on glycemic control, ketogenesis during insulinopenia, insulin resistance, and diabetes burden and quality of life.

This study will evalute the effect of a low glycemic load (LGL diet on dysglycemia, insulin requirements, DXA-derived body composition, gastrointestinal symptoms and quality of life measures in adults with cystic fibrosis-related diabetes (CFRD). We will use continuous glucose monitors (CGM) to assess the LGL diet both in a controlled setting (via a meal delivery company) and in free-living conditions.

The purpose of the research is to test the effectiveness of different messaging approaches to nudge members of Vitality and Discovery Health, with risk factors for diabetes (based on data from the Vitality Health Check and Vitality Age assessment), to visit a doctor and test for diabetes. The messages are based on concepts from behavioural economics that aim to make information on screening more salient by using the concept of social proof (person like you) and an authoritative source (a diabetes specialist and the Vitality doctor).

This is an open-label, two-group parallel study, comparing before and after treatment within the same group and between the two groups. This study is designed to collect data on the safety and efficacy of a marketed pharmaceutical product (DIDALA) compared with Metformin. The efficacy data of DIDALA will be directly compared with Metformin 1000mg/day monotherapy in this study.

The choroidal thickness was found to be thinner in diabetic eyes without retinopathy compared to healthy eyes, thus choroidal thickness might be an important parameter for the development of diabetic retinopathy in diabetic eyes without retinopathy. Repeated low-level red-light (RLRL) therapy is an emerging innovative and non-invasive treatment for a variety of eye diseases. Notably, RLRL was found to be effective in thickening choroidal thickness in a 1-year randomized controlled trial, indicating its potential in modulating blood flow in the fundus. This study aims to answer whether RLRL therapy can thicken choroidal thickness in adults with diabetes mellitus or diabetic retinopathy.

Study Background: Diabetes mellitus (DM) is a major non-communicable disease. Diagnosis and self-management of DM is important. Currently, detection of diabetes requires blood tests, which is costly and inconvenient, especially for elderlies.Tongue diagnosis has been used in Chinese medicine as a routine diagnostic method, and it has recently been studied for detection of DM and diabetic retinopathy (DR). We have developed a method for taking tongue images using smartphone, which can reveal more detailed features than conventional clinical tongue inspection. There are many limitations of the preliminary study. Therefore, it is our plan in this study to address these specific limitations with the following objectives. The results of this study will enable us to develop a practical App for diabetes screening and monitoring. Study Objective The aim of the study is to develop an algorithm for diabetes screening, with the following objectives: . To determine the sensitivity and specificity of tongue images taken with smartphone in predicting abnormal HbA1c (≥6.5%); To determine tongue image features responsible for the classification of normal and abnormal levels of HbA1c (≥6.5%); To determine the sensitivity and specificity of tongues image in predicting four different levels of HbA1c: <6% (normal), 6-6.4% (prediabetes), 6.5-8.9% (diabetes) and ≥ 9% (diabetes with high HbA1c); To determine the sensitivity and specificity of combining image analysis results with the results from a TCM symptom questionnaire in predicting the four levels of HbA1c. Hypothesis: Our working hypothesis is that different tongue coating features may be associated with different stages of diabetes, as indicated by different levels of HbA1c；and different combination of symptoms from a TCM point of view may also be associated with different levels of HbA1c. Thus, combining tongue image with TCM symptoms may allow a machine learning model to build an algorithm for HbA1c prediction with reasonable accuracy. Inclusion criteria: The inclusion criteria is adult subjects with HbA1c test results from a laboratory that meets the ISO 15189 standard, such as those laboratories used by the Hospital Authority of Hong Kong. Exclusion criteria: Subjects who are unable to give consent, unable to answer the questionnaire or to cooperate in tongue image collection will be excluded. We will not include subject who are unable to understand written Chinese or English. Study design: This is a cross-sectional design looking at the relationship between tongue image pattern and HbA1c reading. Age, gender, weight, height , duration of diabetes, family history of diabetes and any comorbid disease will be recorded. The level of hemoglobin and blood lipid profile will also be recorded if the information is available. Any acute repertory or digestive illness, as well as smoking habits will also be noted. An electronic questionnaire (using Qualtrics survey software) based on published TCM symptoms of diabetes and the abovementioned information will be used for data collection. Data processing and analysis Tongue segmentation The images containing the tongue and its surrounding area will be processed for segmentation of the tongue area. This segmentation is carried out by a computer algorithm developed in-house by machine learning. Machine learning Two approaches will be used in machine learning. In the first approach, we will first perform image classification of either normal or abnormal HbA1c and generate the probabilities for the classification using convolutional neural networks (CNNs) (Anwar et al., 2016; Ødegaard et al., 2016). Then we will try to classify the images into four different classes according to their HbA1c level: <6% (normal), 6-6.4% (prediabetes), 6.5-8.9% (diabetes) and ≥ 9% (diabetes with high HbA1c). Primary Outcome: Tongue image features: We will extract tongue image features and perform image classification of either normal or abnormal HbA1c and generate the probabilities for the classification using convolutional neural networks (CNNs). Then we will try to classify the images into four different classes according to their HbA1c level: <6% (normal), 6-6.4% (prediabetes), 6.5-8.9% (diabetes) and ≥ 9% (diabetes with high HbA1c). Secondary Outcome: Symptom patterns: Questionnaire data will be combined with image data for regression analysis

The purpose of this study is to evaluate the efficacy and safety of LID104 in the treatment of type 2 diabetes mellitus.

The goal of this observational study has the purpose of collecting biological samples from obese patients undergoing evaluation for weight loss by means of medical or endoscopic therapies; and of post bariatric surgery patients presenting with short- and long-term surgical complications. The aim is to enhance the overall understanding of the mechanisms leading to obesity, weight loss, failure to lose weight, and weight regain following treatment. Additional goals are to determine the efficacy of endoscopic and surgical procedures, to identify potential therapeutic targets and disease biomarkers that predict response to therapy.

The objective of this randomized study was to compare outcomes of imaging-and physiology-guided state-of-the-art Percutaneous coronary intervention (PCI) to coronary artery bypass grafting (CABG) in patients with diabetes and multivessel coronary artery disease(CAD) with left anterior descending(LAD) involvement.