Predicting dementia outcomes using simple, non-invasive assessments: a prospective population-based study

使用简单、非侵入性评估预测痴呆症结果：一项基于人群的前瞻性研究

• 批准号: MR/P001823/1
• 负责人: Timothy Wilkinson
• 金额: $ 26.38万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FP001823%2F1
• 关键词: Predicting; dementia; outcomes; using; simple

Background:Around 670,000 people in the UK are currently living with dementia, and this number is expected to double over the next twenty years. Despite many years of research, we still do not have a treatment that prevents or cures this devastating condition. We now understand that the damage causing dementia begins many years before someone develops symptoms, and so it is possible that treatments do not work because the condition is too severe by the time we give them to patients. We therefore need to find a way of identifying people who are currently healthy but are at risk of getting dementia in the future. We ideally need to do this using only the sort of information that is available to GPs, to avoid doing invasive and expensive tests on lots of healthy people. Project aim:In this study I will use data from a large study called UK Biobank, to create a model that uses simple information to predict who is most at risk of developing dementia over a 5-10 year period.Where this research will be performed:This research represents a collaboration of several groups of researchers across several sites at the University of Edinburgh. I will perform the analyses with support, guidance and training from experts in the field.How the predictive model will be created:UK Biobank (UKB) is a very large population-based cohort study of 503,000 middle-aged people. During recruitment participants were extensively evaluated and took brief, electronic thinking tests. In 2014-2015 over 118,000 participants responded to a repeat online memory test, making this the biggest study of repeat cognitive testing ever.The participants in UKB are followed up using routine NHS datasets. When patients are diagnosed with conditions such as dementia either by their GP, in hospital or after they have died, this is recorded in these datasets. The participants in UKB have consented to let UKB access these records so they can learn about their health. Conservative predictions have shown there is likely to be around 4000 dementia cases in the cohort by mid-2017, which would make this by far the largest ever study to create a dementia prediction model. It has also given me relevant research experience and an appreciation of the issues involved when working with data from cohort studies and with healthcare datasets from England, Scotland and Wales.I will apply to access UKB data that includes the information obtained at recruitment, during the repeat online tests and in the routine NHS datasets. I will then investigate which simple characteristics can best predict who is likely to get dementia. These are likely to be things such as age, smoking status, educational level and family history. I will also look at how physical health problems (such as diabetes, heart disease and stroke) might impact on a person's mental health, by seeing whether having one of these conditions increases the risk of getting dementia. I will also use the brief thinking tests that participants took at recruitment and during follow up to see if changes in these can predict who will get dementia before they have obvious symptoms. I will then combine the most predictive characteristics into one model.After creating the model, the next, important stage will be to test it. To do this I will use data from a Scottish study called Generation Scotland (GS). GS has many similarities to UKB in the way participants were recruited and tested. I will also test the model using real-life data from two very large sources of GP data from England and Wales. Why this research matters:We need to change the way we test new dementia treatments to increase the likelihood we find one that works. My goal is to build a prediction tool that can be used to identify people at risk of developing dementia, so they can be invited to participate in trials testing new treatments. If an effective treatment becomes available, doctors could also use this tool to identify who would benefit.

背景：目前英国约有67万人患有痴呆症，预计这一数字在未来20年将翻一番。尽管经过多年的研究，我们仍然没有一种治疗方法可以预防或治愈这种毁灭性的疾病。我们现在明白，导致痴呆症的损害在患者出现症状之前很多年就开始了，所以有可能治疗不起作用，因为在我们给患者治疗时，病情已经太严重了。因此，我们需要找到一种方法来识别目前健康但未来有患痴呆症风险的人。理想情况下，我们只需要利用全科医生可以获得的信息来做这件事，以避免对许多健康的人进行侵入性和昂贵的测试。项目目标：在这项研究中，我将使用一项名为UK Biobank的大型研究的数据来创建一个模型，该模型使用简单的信息来预测谁在5-10年内最有可能患痴呆症。这项研究将在哪里进行：这项研究代表了爱丁堡大学几个地点的几个研究小组的合作。我将在该领域专家的支持、指导和培训下进行分析。如何建立预测模型：英国生物银行（UKB）是一项基于人口的大型队列研究，涉及503,000名中年人。在招聘期间，对参与者进行了广泛的评估，并进行了简短的电子思维测试。2014-2015年，超过11.8万名参与者参与了一项重复的在线记忆测试，这是有史以来规模最大的重复认知测试研究。UKB的参与者使用常规NHS数据集进行随访。当病人被全科医生诊断患有痴呆症时，无论是在医院还是在他们去世后，这都被记录在这些数据集中。UKB的参与者已经同意让UKB访问这些记录，以便他们了解自己的健康状况。保守预测显示，到2017年年中，该队列中可能会有大约4000例痴呆症病例，这将使该研究成为迄今为止创建痴呆症预测模型的最大研究。它也给了我相关的研究经验，并在处理来自队列研究的数据和来自英格兰、苏格兰和威尔士的医疗保健数据集时，对所涉及的问题有了认识。我将申请访问UKB数据，包括在招聘、重复在线测试和常规NHS数据集中获得的信息。然后，我将研究哪些简单的特征可以最好地预测谁可能患痴呆症。这些因素可能包括年龄、吸烟状况、教育程度和家族史等。我也会看看身体健康问题（如糖尿病、心脏病和中风）是如何影响一个人的心理健康的，看看有这些疾病中的一种是否会增加患痴呆症的风险。我还将使用参与者在招募时和随访期间进行的简短思维测试，看看这些测试的变化是否可以在出现明显症状之前预测谁会患上痴呆症。然后，我将把最具预测性的特征合并到一个模型中。在创建模型之后，下一个重要的阶段将是测试它。为此，我将使用一项名为“苏格兰一代”（GS）的苏格兰研究的数据。在招募和测试参与者的方式上，GS与UKB有许多相似之处。我还将使用来自英格兰和威尔士的两个非常大的GP数据来源的真实数据来测试该模型。为什么这项研究很重要：我们需要改变测试新的痴呆症治疗方法的方式，以增加我们找到有效治疗方法的可能性。我的目标是建立一个预测工具，可以用来识别有患痴呆症风险的人，这样他们就可以被邀请参加新疗法的试验。如果有一种有效的治疗方法，医生也可以使用这个工具来确定谁会受益。

项目成果

Accuracy of routinely-collected healthcare data for identifying motor neurone disease cases: A systematic review.

• DOI: 10.1371/journal.pone.0172639
• 发表时间: 2017
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Horrocks S;Wilkinson T;Schnier C;Ly A;Woodfield R;Rannikmäe K;Quinn TJ;Sudlow CL
• 通讯作者: Sudlow CL

The diagnosis, burden and prognosis of dementia: A record-linkage cohort study in England.

• DOI: 10.1371/journal.pone.0199026
• 发表时间: 2018
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Pujades-Rodriguez M;Assi V;Gonzalez-Izquierdo A;Wilkinson T;Schnier C;Sudlow C;Hemingway H;Whiteley WN
• 通讯作者: Whiteley WN