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

痴呆症是全球致残和死亡的主要原因。心脏健康状况不佳一直与患痴呆症的风险增加有关。然而，并不是每个患有心血管疾病的人都会患上痴呆症，我们还没有一种方法将人们划分为不同的风险群体（例如，高、中、低痴呆症风险群体）。因此，该项目旨在利用先进的分析方法，首次开发新的方法来识别那些心血管疾病患者，他们将处于痴呆症的最高风险；(2)与痴呆症相关的死亡率。能够识别痴呆症高危人群非常重要，因为它可以：(1)帮助及时识别；(2)在可能的情况下允许个体化治疗以降低风险；(3)协助开发针对痴呆症的新干预措施；(4)协助患者、家属和临床医生对未来进行规划。我们将分析来自两个大型国际资源的数据，包括国际联盟记忆队列研究（COSMIC）和新兴风险因素合作（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随访数据。我们还将访问英国生物银行的数据。这包括来自英国大约50万名年龄在40-69岁的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并确保研究结果的有效性。所有研究收集的数据包括问卷调查数据、生物样本和物理测量。我们将评估不同风险变量（如年龄、性别、种族、健康、饮食、生活方式和遗传）与心血管疾病（如冠心病）患者痴呆风险之间可能存在的复杂关联模式。我们将回答以下问题：(1)导致痴呆症和痴呆症相关死亡风险的最重要因素是什么；(2)我们如何结合不同因素的知识来提高对痴呆症的预测？由于目前尚无治愈方法，目前针对痴呆症的策略是试图预防或延缓高风险人群的发病；并尽早诊断，以便获得支持。这些结果对于根据痴呆风险状况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风险最高的人群进行现有治疗。

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.