Developing a decision support tool to enable precision treatment of type 2 diabetes

2型糖尿病在全世界都很常见。糖尿病患者可能出现严重的健康问题，包括失明、肾衰竭、截肢、心脏病发作和中风。如果防止血糖过高，这些问题是可以预防的。大多数2型糖尿病患者需要药物来降低血糖：在英国有超过300万人需要这些药物。虽然很清楚首先使用的药物应该是一种叫做二甲双胍的片剂，但还不清楚在此之后应该使用哪种药物。市面上有许多药物都能在相似程度上平均降低血糖，但目前尚不清楚哪种药物最适合个别患者，因为不同的患者对不同药物的反应可能不同。我们的研究小组，在MRC的资助下，分析了成千上万的糖尿病患者对降血糖药物的反应，包括他们的家庭医生记录和临床试验。我们已经证明了一些简单的特征，比如病人的性别，他们的超重程度，以及血液测试的结果，都与一种特定治疗方法降低血糖的效果有关。这意味着我们现在可以从简单的常规收集的信息中计算出哪种药物最有可能降低血糖。这项令人兴奋的工作意味着现在有可能为每个病人选择合适的药物来最好地降低血糖。该项目的目的是建立在这些信息的基础上，开发一个计算机程序，称为决策支持工具，以找出哪种治疗方法对2型糖尿病患者最好。我们已经开发了一种简单的工具，它结合了不同的常规可用信息，以准确预测哪种降糖药物对降低人的血糖最有效。然而，这并没有对所有糖尿病药物和不同种族的人进行测试，在选择治疗时还有许多其他方面需要考虑，例如是否有可能产生副作用，或者一个人是否有其他影响治疗选择的疾病。在本研究项目的第一部分，我们将解决这些差距。我们将利用来自医疗记录和临床试验的100多万2型糖尿病患者的信息：1)扩展该工具，使其适用于所有糖尿病药物，并适用于所有种族的人群；2)扩展该工具，以预测人们是否可能因副作用而需要迅速停止治疗。3)调整该工具，以便它为患有影响药物可以或应该服用的疾病的人推荐某些药物。例如，某些药物已被证明对心脏病患者效果更好。在项目的第二部分，我们将测试该工具是否可以通过添加将来可能更容易获得的额外信息来改进，例如一个人的遗传信息，或者不常规测试的血液测试。我们将开发一种流程，以便未来的变化——例如预测反应的新特征或新药——可以快速做出，使该工具保持最新状态。然后，我们将与糖尿病患者、医生和护士合作，确定展示结果的最佳方式，使结果易于理解，并有助于讨论应该尝试哪种治疗方法。我们将与计算机程序员合作，将这个工具制作成在线计算器或应用程序。这项研究非常重要，因为它将提供一种方法，帮助糖尿病患者接受他们最有可能受益的治疗，并避免可能给他们带来副作用的治疗。这可能对糖尿病患者和NHS有重要的好处，因为它可以减少高血糖的并发症，减少使用效果不佳或导致令人不快的副作用的药物。

Type 2 diabetes is very common throughout the world. Severe health problems can occur for people living with diabetes including blindness, kidney failure, amputations, heart attacks and strokes. These problems can be prevented if the blood glucose (sugar) is prevented from going too high. Most people with type 2 diabetes need medication to lower blood glucose: over 3 million people in the UK need these medicines. Although it is clear the first medicine to be used should be a tablet called Metformin, it is not clear what type of medicine should be used after this. Many medicines are available which on average lower blood glucose to a similar extent, but it is unclear which is the best to use for individual patients, who may respond differently to different medicines.Our research group, in work funded by the MRC, has analysed the response to blood glucose lowering medicines in many thousands of people with diabetes both from their family doctor records and clinical trials. We have shown that simple features like a patient's sex, how overweight they are, and the results of blood tests are linked to how well a specific type of treatment lowers the blood glucose. This means we can now work out from simple, routinely collected, information which type of medicine is likely to lower blood glucose the most. This exciting work means that it will now be possible to choose the right type of medicine to best lower the glucose for each patient. The aim of this project is to build on this information to develop a computer program, called a decision support tool, to work out which treatments would be best for a person with type 2 diabetes. We have already developed a simple tool that combines different routinely available information to accurately predict which glucose lowering medicines will be most effective in lowering a person's blood glucose. However this has not been tested for all diabetes medicines and for people of different ethnicities, and there are many other aspects to consider when choosing treatment, for example whether side effects are likely, or whether a person has other conditions that affect treatment choice. In the first part of this research project we will address these gaps. We will use information from over a million people with type 2 diabetes from healthcare records and clinical trials to: 1) Expand the tool so that it works for all diabetes medicines, and in people of all ethnicities 2) Expand the tool to predict whether people are likely to need to stop treatments quickly due to side effects. 3) Adapt the tool so that it recommends certain medicines in people with medical conditions that affect what medicine can or should be taken. For example, certain medicines have been shown to be better in people who have heart disease. In the second part of the project we will test whether the tool can be improved by adding in additional information likely to be more available in the future, for example a person's genetic information, or blood tests that are not routinely tested. We will develop a process so that future changes - for example new features that predict response, or new medicines - can be made rapidly, keeping the tool up to date. We will then work with people with diabetes, doctors, and nurses to determine the best way to present the results, so that they are easy to understand and helpful for informing discussions on which treatments to try. We will work with a computer programmer to make this tool into an online calculator or app. This research is really important as it will provide a way to help people with diabetes receive the treatment they are most likely to benefit from, and avoid treatments that are likely to give them side effects. This could have important benefits for people with diabetes and the NHS, by reducing the complications of high blood glucose, and reducing the use of medicines which do not work well or cause unpleasant side effects.