MICA: MRC APBI STratification and Extreme Response Mechanism IN Diabetes - MASTERMIND

研究背景超过4%的人患有2型糖尿病。它是导致疾病和早逝的主要原因，约占NHS支出的10%。良好的血糖控制，适当的生活方式和药物治疗，会让患者感觉更好，减少发生糖尿病并发症的风险。策划者研究涉及学术研究人员和行业之间的合作，以帮助2型糖尿病患者接受最合适的治疗。目前，《2型糖尿病患者治疗指南》列出了大量药物，对哪些患者应该服用哪些药物没有明确的指导。这使得患者和他们的医疗保健专业人员很难知道哪些药物可能最适合他们。我们知道，2型糖尿病患者对不同糖尿病药物的反应如何，以及他们是否对特定药物产生副作用，存在很大差异。我们最初的初步研究帮助我们确定了识别反应亚群的最佳实验类型，并使我们产生了初步结果，现在我们可以在此基础上进行测试、改进和扩展。研究目的本项目的目的是根据特定的临床特征(如体重或血液测试结果)确定对特定药物反应良好或不良的患者亚群，以便更好地针对特定个人进行治疗。研究概述这个新项目旨在建立在最初研究工作的基础上，涉及两个方面：1.我们将进行一项试验，目前服用二甲双胍和磺脲类药物的高血糖患者将随机获得3种不同的糖尿病药片。我们将在研究开始时测量并采集血样，然后在每种药物治疗4个月结束时测量患者的平均血糖控制(HbA1c)。我们还会记录任何副作用，并询问患者他们更喜欢哪种治疗方法。这三种治疗糖尿病的方法有不同的效果。我们将能够测试这是否意味着不同的患者有不同的临床特征(例如，患者是否肥胖，或者他们的肾功能是否较差)，这些特征决定了他们对药物是否有良好的反应。我们将分析可公开获得的大型数据集，这些数据集包含数千名2型糖尿病患者的数据。我们将能够访问来自全科医生实践的匿名数据和来自制药公司运营的药物试验的数据。我们将使用统计分析来确定哪些特征挑选出对不同药物在短期和长期内反应良好的患者，并确定哪些患者有副作用。研究的关键结果这项工作的主要结果是：1.关于可用于确定哪些患者亚群对哪些药物反应最好的标准的信息。这一证据将被用来为2型糖尿病的药物处方提供新的指导方针。2.制定如何确定这些个人亚群的方法，这将有助于今后在这一领域的研究。这些新的科学方法和数据分析技术也可以应用于其他疾病。3.学术团体和制药公司都将获得来自2型糖尿病患者的大量样本以及他们对三种不同糖尿病药物的反应。这将用于未来的研究，在这些研究中，可以分析这些样本，以找到新的血液标志物，以确定一个人对特定药物的反应是好是坏，并将通过深入了解不同患者对不同的2型糖尿病药物反应不同的原因来帮助药物开发。

Context of the researchOver 4% of the population have Type 2 diabetes. It is a major cause of illness and early death accounting for around 10% of the money spent in the NHS. Good control of blood glucose with appropriate life style and medication makes patients feel better and reduces the risks of the development of the complications of diabetes. The MASTERMIND study involves collaboration between academic researchers and industry to help patients with Type 2 diabetes receive the most suitable treatment. At present, the guidelines for treatment of patients with Type 2 diabetes list a large number of drugs without giving clear guidance on which patients should have which drugs. This makes it difficult for patients and their health care professionals to know which drugs are likely to suit them best. We know that patients with Type 2 diabetes vary greatly in how well they respond to different diabetes drugs, and whether they develop side effects to particular medications. Our initial pilot study has helped us to determine the best types of experiment to identify response subgroups and has let us generate preliminary results that we can now test, improve and expand upon.Aim of the researchThe aim of this project is to identify subgroups of patients that respond well or poorly to particular drugs based on particular clinical characteristics such as their weight or blood test results, to enable better targeting of treatment for a particular individual. Outline of the researchThe new project aims to build on the work done in the initial study and involves 2 strands:1. We will carry out a trial where patients who currently have high blood glucose on metformin and sulphonylurea therapy will receive 3 different diabetes tablets in random order. We will take measurements and blood samples at the start of the study, and then measure the patients' average blood glucose control (HbA1c) at the end of 4 months on each of these drugs. We will also record any side effects and ask the patient which treatment they preferred. These 3 diabetes treatments work in different ways. We will be able to test whether this means different patients have different clinical features (e.g. whether a patient is obese, or whether they have poorer kidney function) that determine whether they respond well to the drugs.2. We will analyse large publicly-available datasets that have data on thousands of patients with Type 2 diabetes. We will have access to anonymised data from GP practices and data from drug trials run by pharmaceutical companies. We will use statistical analysis to identify which features pick out patients who respond well to the different drugs in the short and long term and also to identify which patients have side effects.Key outcomes of the researchThe key outcomes of this work are: 1. Information on the criteria that can be used to identify which subgroups of patients respond best to which drugs. This evidence will be used to inform new guidelines for prescribing drugs for Type 2 diabetes. 2. Development of methodology for how to identify these subgroups of individuals that will help future studies in this area. These new scientific methods and data analysis techniques can then also be applied to other diseases. 3. A large bioresource of samples from patients with Type 2 diabetes and how they responded to the three different diabetes drugs will be available to both academic groups and pharmaceutical companies. This will be used in future studies where these samples can be analysed to find new blood markers that identify whether an individual is likely to respond well or poorly to a particular drug, and will help drug development by giving insights into why different patients respond differently to the different Type 2 diabetes drugs.