Untangling the mechanistic links between heart and brain health in older populations: An AI assisted toolkit for assessing dementia risk

理清老年人心脏和大脑健康之间的机制联系：用于评估痴呆症风险的人工智能辅助工具包

• 批准号: MR/X005437/1
• 负责人: Blossom Stephan
• 金额: $ 14.89万
• 依托单位: University of Nottingham
• 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: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX005437%2F1
• 关键词: Untangling; mechanistic; links; between; heart

Dementia is a leading cause of disability and death globally. Poor heart health has consistently been linked to an increased risk of dementia. Yet not everyone with cardiovascular disease develops dementia and we don't yet have a method to separate people into different risk groups (e.g. high, medium, and low dementia risk groups). Therefore, this project aims to use advanced analytical methods to develop, for the first time, new approaches for identifying those individuals with cardiovascular disease who would be at highest risk of (1) dementia; and (2) dementia-related mortality. Being able to identify individuals at high risk of dementia is important as it could: (1) assist in timely identification; (2) allow for individually tailored treatment to reduce risk, where available; (3) assist with the development of new interventions focused on dementia; and (4) assist patients, their families, and clinicians with future planning.We will analyse data from two large international resources including the Cohort Studies of Memory in an International Consortium (COSMIC) and the Emerging Risk Factors Collaboration (ERFC). These resources comprise baseline and follow-up data from over one million men and women in more than 30 longitudinal cohort studies conducted in over 30 countries. We will also access data from the UK Biobank. This includes extensive information from approximately 500,000 men and women aged 40-69 years at baseline in the UK. Access to such a large quantity of high-quality data will help to reduce bias and ensure that the findings are valid. Data collected in all studies includes questionnaire data, biological samples, and physical measurements. We will assess the likely complex pattern of associations between different risk variables (e.g. age, sex, ethnicity, health, diet, lifestyle and genetics) and dementia risk, in people with cardiovascular disease (e.g. coronary heart disease). We will answer questions like (1) what are the most important factors driving risk of dementia and dementia-related mortality; and (2) how can we incorporate knowledge across different factors to improve prediction of dementia? With no cure available, the current strategy for dementia is to try and prevent, or delay, its onset in people at higher risk; and to diagnose as early as possible to allow access to support. The results are essential to informing treatment and management planning according to dementia risk status. They will also help to target existing treatments more efficiently in those at highest risk of dementia.

痴呆症是全球残疾和死亡的主要原因。心脏健康状况不佳一直与痴呆症风险增加有关。然而，并不是每个患有心血管疾病的人都会患痴呆症，我们还没有一种方法将人们分为不同的风险组（例如高，中，低痴呆风险组）。因此，该项目旨在使用先进的分析方法，首次开发新的方法来识别那些患有心血管疾病的人，他们将面临（1）痴呆症的最高风险;和（2）痴呆症相关的死亡率。能够识别痴呆症高风险个体非常重要，因为它可以：（1）帮助及时识别;（2）允许个体定制治疗以降低风险（如果可用）;（3）协助开发专注于痴呆症的新干预措施;以及（4）帮助患者及其家属，我们将分析来自两个大型国际资源的数据，包括国际联盟中的记忆队列研究（COSMIC）和新兴风险因素协作（ERFC）。这些资源包括在30多个国家进行的30多项纵向队列研究中来自100多万男性和女性的基线和后续数据。我们还将访问英国生物银行的数据。这包括来自英国基线时年龄在40-69岁之间的约500，000名男性和女性的广泛信息。获得如此大量的高质量数据将有助于减少偏见，确保调查结果的有效性。所有研究中收集的数据包括问卷调查数据、生物样本和物理测量。我们将评估心血管疾病（如冠心病）患者中不同风险变量（如年龄、性别、种族、健康、饮食、生活方式和遗传学）与痴呆风险之间可能的复杂关联模式。我们将回答以下问题：（1）导致痴呆症风险和痴呆症相关死亡率的最重要因素是什么;以及（2）我们如何将不同因素的知识结合起来，以改善痴呆症的预测？由于没有治愈方法，目前的痴呆症策略是尝试预防或延迟高风险人群的发病;并尽早诊断，以便获得支持。这些结果对于根据痴呆风险状态制定治疗和管理计划至关重要。它们还将有助于更有效地针对痴呆症高危人群的现有治疗。

项目成果

Cardiovascular disease, associated risk factors, and risk of dementia: An umbrella review of meta-analyses

• DOI: 10.3389/fepid.2023.1095236
• 发表时间: 2023-02
• 期刊: Frontiers in Epidemiology
• 作者: Jacob Brain;Leanne Greene;E. Tang;J. Louise;A. Salter;S. Beach;Deborah Turnbull;M. Siervo;B. C. Stephan;P. Tully