View clinical trials related to Pre-diabetes.
Filter by:This study is a prospective cross-sectional study. The investigators enroll participants form the department of endocrinology and of metabolism, China Medical University Hospital. In total, 400 participants , composed of 100 of type 2 diabetes , 100 of pre-DM and 200 healthy participants. The investigators apply tongue diagnosis system, pulse wave analysis, body constitution questionnaires, and nailfold capillaroscopy to assess the differences of TCM diagnosis. After collecting the tongue coating and analyzing the metabolite signals with a flying mass spectrometer, The investigators collect the data to establish metabolite pattern and biomarkers. This study aims to identify the clinical symptoms of DM with TCM diagnostic tools and investigate the pattern difference and treatment for DM. Furtherly, the investigators add mass spectrometer to analyze the metabolites of tongue coating, the investigators propose to establish a metabolite pattern and biomarker to identify important biological indicators of DM.
Obesity is associated with increased cardiometabolic disease risk due, in part, to heightened chronic inflammation arising from adipose tissue. There are no current targeted therapies to prevent or reverse the chronic inflammation of obesity, and a better understanding of these inflammatory pathways in humans is key to future therapeutic interventions. This project will determine both the anti-inflammatory potential of the SGLT2 inhibitor empagliflozin, and the contribution of adipose inflammation to surrogate measures of cardiovascular disease in a randomized controlled trial of obese patients.
This study seeks to address two questions. Firstly, how might a suite of interventions and data feedback (activity, diet, mood*, continuous blood glucose) through coaching be effective in influencing behaviour change for individuals at-risk of developing type 2 diabetes? Secondly, what elements of coaching might be extracted for automated implementation in a scalable coach-light model?
Participants who were previously Viome costumers who signed informed consent to participate and self reported type 2 diabetes or pre-diabetes were enrolled. They provided stool samples to VIOME and were provided with precision diet and supplement recommendations. The information obtained from this study is used to train a model to predict diabetes and/or risks of developing diabetes.
Patients with prediabetes can significantly reduce their risk of developing type 2 diabetes mellitus (T2DM) by participating in a Diabetes Prevention Program (DPP) or using metformin, but very few patients with prediabetes engage in these strategies. This randomized controlled trial will compare, among adults with prediabetes, the effectiveness of financial incentives, tailored messages based on self-determination theory (SDT) principles, and the combination of financial incentives plus tailored messages based on SDT principles in decreasing hemoglobin A1c (HbA1c) and weight and in increasing participation in a DPP or use of metformin. Our main hypotheses are that the mean decrease in HbA1c will be greater in the arm that receives a combination of financial incentives plus tailored messages than in the arm that receives generic health education messages, and that the mean decrease in HbA1c will be greater in the arm that receives a combination of financial incentives plus tailored messages than in the arm that receives financial incentives alone and the arm that receives tailored messages alone. The study will also identify moderators and mediators of the effectiveness of the interventions and evaluate facilitators of and barriers to scalability, acceptability, and sustainability of the different interventions.
Our goal is to test whether shared decision making for diabetes prevention can help women with a history of gestational diabetes mellitus (GDM) who are at high risk of developing type 2 diabetes (T2DM) increase weight loss and adoption of evidence based strategies to lower their risk of incident diabetes.
Many DM and pre-DM remain undiagnosed. The aim is to develop and validate a risk prediction function to detect DM and pre-DM in Chinese adults aged 18-84 in primary care (PC). The objectives are to: 1. Develop a risk prediction function using non-laboratory parameters to predict DM and pre-DM from the data of the HK Population Health Survey 2014/2015 2. Develop a risk scoring algorithm and determine the cut-off score 3. Validate the risk prediction function and determine its sensitivity in predicting DM and pre-DM in PC Hypothesis to be tested: The prediction function developed from the Population Health Survey (PHS) 2014/2015 is valid and sensitive in PC. Design and subjects: We will develop a risk prediction function for DM and pre-DM using data of 1,857 subjects from the PHS 2014/2015. We will recruit 1014 Chinese adults aged 18-84 from PC clinics to validate the risk prediction function. Each subject will complete an assessment on the relevant risk factors and have a blood test on OGTT and HbA1c on recruitment and at 12 months. Main outcome measures: The area under the Receiver operating characteristic (ROC) curve, sensitivity and specificity of the prediction function. Data analysis and expected results: Machine learning and Logistic regressions will be used to develop the best model. ROC curve will be used to determine the cut-off score. Sensitivity and specificity will be determined by descriptive statistics. A new HK Chinese general population specific risk prediction function will enable early case finding and intervention to prevent DM and DM complications in PC.
Type 2 diabetes (T2DM) is a serious chronic condition and one of the world's fastest growing health problems. The onset of T2DM is gradual, with most individuals progressing through a state of pre-diabetes, which provides an important window of opportunity for the prevention of T2DM and its complications. This project aims to translate the evidence-based diabetes prevention strategies into community setting and utilize mobile health technology to reduce diabetes risks in Hong Kong.
The purpose of the study is to determine the efficacy of semaglutide 1mg (Ozempic®) to aid recently postpartum women with dysglycemia and a history of GDM to regress to normoglycemia; thereby filling a gap in efficacious pharmacologic intervention options for clinicians to support postpartum diabetes recovery and reduce future risk of T2DM in young women.
There are an estimate 7 million people in the United Kingdom living with pre-diabetes. The increasing number of new cases of pre-diabetes presents a global health concern due to funding implications. The progression from pre-diabetes to overt type 2 diabetes is often characterised by a reduction in insulin secretion (or β-cell dysfunction). Whilst inflammation may contribute to β-cell dysfunction, a complete picture is still lacking. The proposed research will help develop a more complete understanding of the molecules that may trigger β-cell failure, a process that often connects pre-diabetes to overt diabetes. The aims of this project are; 1. Run large-scale proteomics and metabolomics analysis in pre-diabetic individuals to determine possible biomarker molecules. 2. Use measures and / or classifications of insulin resistance and diabetes (i.e. β-cell function and Disposition Index) to establish whether particular metabolic and / or proteomic signatures (aim 1) are associated with the development of pre-diabetes. 3. To determine if the possible metabolite or protein profile changes are associated with the progression or regression of pre-diabetes from baseline (0 month) to the end of the National Diabetes Prevention Programme (NDPP) (9 month).