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

背景：英国目前约有67万人患有痴呆症，预计这一数字在未来20年内将翻一番。尽管进行了多年的研究，我们仍然没有预防或治愈这种毁灭性疾病的治疗方法。我们现在知道，导致痴呆症的损害在一个人出现症状之前很多年就开始了，所以治疗可能不起作用，因为当我们给病人治疗的时候，情况太严重了。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，我们只需要使用全科医生可以获得的那种信息来完成这项工作，以避免对许多健康人进行侵入性和昂贵的测试。项目目的：在这项研究中，我将使用一项名为英国生物库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年内最有可能患上痴呆症。在哪里进行这项研究：这项研究代表了爱丁堡大学几个地点的几组研究人员的合作。我将在该领域专家的支持、指导和培训下进行分析。如何创建预测模型：英国生物库(UKB)是一项非常大的基于人群的队列研究，涉及503,000名中年人。在招募过程中，参与者接受了广泛的评估，并接受了简短的电子思维测试。2014-2015年间，超过11.8万名参与者参加了一项重复的在线记忆测试，这是迄今为止规模最大的重复认知测试研究。UKB的参与者使用英国国民健康保险制度的常规数据集进行了跟踪调查。当患者被他们的全科医生诊断为痴呆症时，无论是在医院里还是在他们死后，这都会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，这样他们就可以了解自己的健康状况。保守预测显示，到2017年年中，队列中可能会有大约4000例痴呆症病例，这将使这项研究成为迄今为止创建痴呆症预测模型的最大规模的研究。它还让我获得了相关的研究经验，并对使用来自队列研究的数据以及来自英格兰、苏格兰和威尔士的医疗保健数据集时涉及的问题有所了解。我将申请访问UKB数据，其中包括在招募、重复在线测试期间和在NHS常规数据集中获得的信息。然后，我将研究哪些简单的特征最能预测谁可能患上痴呆症。这些因素很可能是年龄、吸烟状况、教育水平和家族史。我还将通过观察患有这些疾病之一是否会增加患痴呆症的风险，来研究身体健康问题(如糖尿病、心脏病和中风)可能对一个人的心理健康产生的影响。我还将使用参与者在招募时和后续行动中进行的简短思维测试，看看这些测试的变化是否可以预测谁会在出现明显症状之前患上痴呆症。然后，我将把最具预测性的特征组合到一个模型中。创建模型后，下一个重要的阶段将是测试它。为了做到这一点，我将使用苏格兰一项名为苏格兰一代(GS)的研究的数据。GS在招募和测试参与者的方式上与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的治疗方法的可能性。我的目标是建立一种预测工具，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与测试新疗法的试验。如果有有效的治疗方法，医生也可以使用这一工具来确定谁将受益。

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.