View clinical trials related to Pre-diabetes.
Filter by:This study will investigate whether changes in the intestinal microbiota generated through a nutritional strategy based on functional foods, modifies postprandial glycemic responses in subjects with prediabetes and obesity, which in turn will generate a personalized dietary intervention through a prediction of postprandial blood glucose levels.
The purpose of this study is to investigate the effect of providing healthy foods and nutrition education on participants' body weight, blood pressure, and average blood sugar level. The healthy food offerings tested in this study will help determine which option is best to improve health outcomes among Cleveland Clinic Akron General patients with chronic conditions. Findings from this study could guide doctors in deciding on appropriate nutrition and dietitian services for Cleveland Clinic patients.
With this study, researchers want to conduct ambulatory studies in which people (healthy, with T2D, or at-risk of T2D) will consume a variety of pre-set and conventional meals in free-living conditions while wearing one or more continuous glucose monitors (CGMs) and, to assess physical activity, a smart watch. With data from these devices, researchers will develop algorithms that can predict the content of a meal.
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.
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.
This clinical study aims to prove that the efficacy of non digestible carbohydrates supplementation (daily dose of 20 grams consumed twice a day for 12 weeks) on the regulation of glucose homeostasis is superior than placebo in prediabetic subjects.
The purpose of this study is to determine the effects of oral abscisic acid (ABA) on glucose metabolism in subjects with defined prediabetes.
This study will test whether a culturally-tailored nutrition and exercise intervention designed for African-American women will lead to sustained improvements in exercise and healthy eating through improvements in self-management mediators: mindfulness, stress management, positive reappraisal, self-regulation, and self-efficacy.
This is a longitudinal study involving use of the January App which collects multiple data streams and employs machine learning techniques to offer personalized lifestyle recommendations and structured food and activity challenges.
To collect data in an observational study from Prediabetes (PD) and Type 2 Diabetes (T2D) patients including time correlated CGM, medication and food intake approximately 80% of the time for each subject that completes the entire active phase.