Active clinical trials for "Diabetes Mellitus"

Pseudoexfoliation syndrome (PEX) is characterized by the deposition of a distinctive fibrillar material in the anterior segment of the eye and was first described in 1917 by Lindberg. It is frequently associated with open angle glaucoma, known as pseudoexfoliation glaucoma, which is one of the most common identifiable forms of secondary open angle glaucoma worldwide. Despite extensive research, the exactchemical nature of the fibrillar material is unknown. It is believed to be secreted multifocally in the iris pigment epithelium, the ciliary epithelium, and the peripheral anterior lens epithelium.

Retinopathy may be associated with diastolic dysfunction and/or coronary flow reserve in the heart, and albuminuria in diabetic patients. The objective of this study is to examine the cross-sectional relationships of retinopathy with indices of left ventricular diastolic function, coronary flow reserve and urinary albumin excretion, among diabetic patients.

This study is to build an multi-modal artificial intelligence ophthalmological imaging diagnostic system covering multi-level medical institutions. We are going to evaluate this system in an evidence-based medicine view, taking diabetic retinopathy as an example. And clinical diagnostic criteria will be made based on this multi-modal artificial intelligence imaging diagnostic system. The study is designed as a cross-sectional study involving 1,000 normal individuals, 1,000 diabetes patients without ocular complications, and 1,000 with diabetic ocular complications. Statistical analysis of the diagnostic sensitivity and specificity of the artificial intelligence system will be made, and ROC curve wil be draw.

INNODIA is a global consortium linking 26 academic institutions, 4 industrial partners, a small to medium enterprise (SME), and 2 patient organisations, bringing their knowledge and experience together with one common goal: "To fight type 1 diabetes". (www.innodia.eu). The project, approved in November 2015 and launched in January 2016, runs under the framework of the Innovative Medicines Initiative - Joint Undertaking (https://www.imi.europa.eu/projects-results/project-factsheets/innodia) with a dedicated governance structure ensuring close interaction, communication and adherence to the objectives and deliverables of the consortium. The overall aim of INNODIA is to advance in a decisive way how to predict, stage, evaluate and prevent the onset and progression of type 1 diabetes (T1D). For this, INNODIA has established a comprehensive and interdisciplinary network of clinical and basic scientists, who are leading experts in the field of T1D research in Europe, with complementary expertise from the areas of immunology, Beta-cell biology, biomarker research and T1D therapy, joining forces in a coordinated fashion with industry partners and two foundations, as well as with all major stakeholders in the process, including regulatory bodies and patients with T1D and their families. One of the objectives of INNODIA is to develop a new European clinical research network with standardized protocol based on repeated measures of C-peptide (including home measurements) and comprehensive collection of appropriate biological samples for 'omics', immune, viral and microbiome studies in new onset T1D patients and high-risk auto-antibody positive subjects. A protocol for the harmonization of sample collections in newly diagnosed type 1 diabetic patients and first degree relatives of patients with type 1 diabetes was developed following extensive preliminary work involving partners from across all specialities. Core laboratories with experience in their respective field were set up for analysis of auto-antibodies, fresh immune cells, handling of frozen immune cells, C-peptide measures. A series of standard operating procedures for sample collections and analysis were agreed. Sample tracking between clinical centres and central laboratories was included into a purposely designed electronic case report form (eCRF) into which all clinical and laboratory data collected are captured.

This study seeks to correlate microbiome sequencing data with information provided by patients and their medical records.

The investigators evaluated the satisfaction of these patients with the health service and their self-care. The study included patients diagnosed with DM, respecting the established inclusion and exclusion criteria. Participants were randomized into the control group and the intervention group.

The investigators will evaluate the stool microbiota, serum and fecal bile acid composition, and changes in blood glucose and lipid profile before and after one month of metformin or YH1 treatment.

The prevalence of lower extremity arterial disease (LEAD) in patients with diabetes increases significantly and are characterized with obvious arteriosclerosis that are caused by multiple metabolic disorders. Metabolomics measures the metabolites in biological fluids or tissues that generated under certain conditions via rapidly evolving high-throughput technology. Herein, the investigators designed the study to characterize the serum metabolic profiles of LEAD patients and identify metabolic biomarkers using metabolomics. The serum of volunteers, type 2 diabetes mellitus(T2DM) patients with or without LEAD were collected and analyzed using liquid chromatography-mass spectrometry(LC-MS) coupled with a series of multivariate statistical analyses.

There were 83,85,440 confirmed cases of COVID-19 worldwide with a mortality rate of 5.4% according to the situation report of the World Health Organisation on June 19, 2020. In India there were 3,95,048 confirmed cases of COVID-19 with a mortality rate of 3.3%. In Tamil Nadu there were 54,449 confirmed cases of COVID-19 with a mortality rate of 1.2% according to the report of Ministry of Health and Family Welfare, Government of India on June 19, 2020. COVID-19 infection is a double challenge for people with diabetes. India has a large number of persons with diabetes (more than 77 million). Recent studies have reported rising prevalence both in the urban and rural populations. The incidence of type 2 diabetes (T2D) also shows an increasing trend in the last few decades. Mortality seems to be threefold higher in people with diabetes when COVD-19 coexists when compared with mortality due to COVID-19 without comorbidities. Yang et al noted that among 52 intensive care patients who had COVID 19 infection, 22% had diabetes among the non-survivors. In a study of 173 patients with severe disease, 16.2% had diabetes, and in another study of 140 hospitalized patients, 12% had diabetes. Li et al compared intensive care and non-intensive care patients who had COVID-19. They showed a twofold increase in the incidence of diabetes in intensive care patients. In addition to diabetes; the other common co-morbidities present in COVID 19 patients were hypertension (20%), cardiovascular disease (16%), and lung disease (6%). In this context, patients with diabetes have been listed as people with higher severity for COVID-19 by several health authorities. However, precise data regarding patients with and without diabetes having COVID-19 infection are still lacking. Moreover, the relationship between diabetes and the severity of COVID-19 remains unknown. In this study, we intend to identify the disease severity and mortality in people with and without diabetes admitted for COVID-19 in southern India.

The aim of this study is to compare visual-motor integration, academic achievement and participation in activities of daily living with and without type 1 diabetes.