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

开发决策支持工具以实现 2 型糖尿病的精准治疗

• 批准号: MR/W003988/1
• 负责人: Andrew Hattersley
• 金额: $ 125.86万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FW003988%2F1
• 关键词: Developing; decision; support; tool; enable

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.

2 型糖尿病在全世界都很常见。糖尿病患者可能会出现严重的健康问题，包括失明、肾衰竭、截肢、心脏病和中风。如果防止血糖（糖）过高，这些问题就可以预防。大多数 2 型糖尿病患者需要药物来降低血糖：英国有超过 300 万人需要这些药物。尽管明确首先使用的药物应该是二甲双胍片剂，但尚不清楚此后应使用哪种类型的药物。许多药物平均可以将血糖降低到类似的程度，但尚不清楚哪种药物最适合个体患者，因为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，根据家庭医生记录和临床试验分析了数千名糖尿病患者对降血糖药物的反应。我们已经证明，患者的性别、超重程度以及血液检查结果等简单特征与特定类型的治疗降低血糖的效果有关。这意味着我们现在可以从简单的、常规收集的信息中找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可以为每位患者选择合适的药物来最好地降低血糖。 该项目的目的是基于这些信息开发一个称为决策支持工具的计算机程序，以找出最适合 2 型糖尿病患者的治疗方法。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，以准确预测哪些降糖药物对降低人体血糖最有效。然而，这一点尚未针对所有糖尿病药物和不同种族的人进行过测试，并且在选择治疗方法时还需要考虑许多其他方面，例如是否可能出现副作用，或者一个人是否患有影响治疗选择的其他疾病。在本研究项目的第一部分中，我们将解决这些差距。我们将利用医疗记录和临床试验中来自超过 100 万 2 型糖尿病患者的信息来：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 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，以便向患有影响可以或应该服用药物的疾病的人推荐某些药物。例如，某些药物已被证明对患有心脏病的人效果更好。在该项目的第二部分中，我们将测试是否可以通过添加未来可能更容易获得的附加信息来改进该工具，例如一个人的遗传信息或不定期测试的血液测试。我们将开发一个流程，以便可以快速做出未来的变化，例如预测反应的新功能或新药物，并使工具保持最新状态。然后，我们将与糖尿病患者、医生和护士合作，确定呈现结果的最佳方式，以便结果易于理解，并有助于为尝试哪种治疗方法的讨论提供信息。我们将与计算机程序员合作，将该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来帮助糖尿病患者接受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。这可以减少高血糖并发症，并减少使用效果不佳或引起不良副作用的药物，从而为糖尿病患者和 NHS 带来重要好处。

项目成果

Patient preference for second- and third-line therapies in type 2 diabetes: a prespecified secondary endpoint of the TriMaster study.

• DOI: 10.1038/s41591-022-02121-6
• 发表时间: 2023-03
• 期刊: Nature medicine
• 影响因子: 82.9
• 作者: Shields BM;Angwin CD;Shepherd MH;Britten N;Jones AG;Sattar N;Holman R;Pearson ER;Hattersley AT
• 通讯作者: Hattersley AT

Patient stratification for determining optimal second-line and third-line therapy for type 2 diabetes: the TriMaster study.

• DOI: 10.1038/s41591-022-02120-7
• 发表时间: 2023-03
• 期刊: Nature medicine
• 影响因子: 82.9
• 作者: Shields BM;Dennis JM;Angwin CD;Warren F;Henley WE;Farmer AJ;Sattar N;Holman RR;Jones AG;Pearson ER;Hattersley AT;TriMaster Study group
• 通讯作者: TriMaster Study group

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine.

• DOI: 10.1186/s12911-023-02207-2
• 发表时间: 2023-06-16
• 期刊: BMC MEDICAL INFORMATICS AND DECISION MAKING
• 影响因子: 3.5
• 作者: Venkatasubramaniam, Ashwini;Mateen, Bilal A.;Shields, Beverley M.;Hattersley, Andrew T.;Jones, Angus G.;Vollmer, Sebastian J.;Dennis, John M.
• 通讯作者: Dennis, John M.

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: An application for type 2 diabetes precision medicine

比较因果森林和基于回归的方法来评估治疗效果异质性：2 型糖尿病精准医疗的应用

• DOI: 10.1101/2022.11.07.22282023
• 发表时间: 2022
• 作者: Venkatasubramaniam A

TriMaster: randomised double-blind crossover trial of a DPP4-inhibitor, SGLT2-inhibitor and thiazolidinedione to evaluate differential glycaemic response to therapy based on obesity and renal function

TriMaster：DPP4 抑制剂、SGLT2 抑制剂和噻唑烷二酮的随机双盲交叉试验，用于评估基于肥胖和肾功能的治疗的差异血糖反应

• DOI: 10.21203/rs.3.rs-2132634/v1
• 发表时间: 2022
• 作者: Hattersley A