The predicative values of vascular and metabolic disorders for risk of incident mild cognitive impairment and dementia

血管和代谢紊乱对发生轻度认知障碍和痴呆风险的预测价值

• 批准号: 10661996
• 负责人: Longjian Liu
• 金额: $ 18.96万
• 依托单位: DREXEL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10661996
• 关键词: Address; Adult; Affect; Age; Aging; Algorithms; Alleles; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Amyloid beta-Protein; Apolipoprotein E; Biological Markers; Blood; Blood Vessels; Brain; Chronic; Cognitive; Cohort Studies; Data; Dementia; Development; Diabetes Mellitus; Diagnosis; Dimensions; Disease; Dyslipidemias; Early Diagnosis; Elderly; Encephalitis; Functional disorder; Genes; Genetic; Hyperglycemia; Impairment; Individual; Inflammation; Inflammatory; Ionizing radiation; Journals; Judgment; Late Onset Alzheimer Disease; Literature; Longevity; Machine Learning; Magnetic Resonance Imaging; Measures; Memory; Metabolic; Metabolic Diseases; Metabolic syndrome; Methods; Modeling; Modification; Neurosciences; Outcome; Outcome Study; Participant; Pathology; Patients; Population; Population Study; Predictive Factor; Predictive Value; Predictive Value of Tests; Prevention; Prevention Measures; Primary Health Care; Proteins; Public Health; Publishing; Questionnaires; Reporting; Research; Research Design; Risk; Risk Factors; Sample Size; Sampling Studies; Secondary to; Short-Term Memory; Signal Transduction; Special Equipment; Testing; Thinking; Time; Vascular Diseases; Woman; Women' s Health; aged; aging population; cohort; dementia risk; disorder risk; experience; fluorodeoxyglucose positron emission tomography; genetic risk factor; hazard; health care settings; high risk; human old age (65+); indexing; innovation; insight; meetings; men; mental state; middle age; mild cognitive impairment; neuroimaging; normal aging; novel; older women; predictive modeling; risk prediction; risk prediction model; sex; tau Proteins; tool; vascular risk factor; volunteer

Project Summary/Abstract Dementia is a condition affecting thinking, judgment, memory and other cognitive domains to the degree that interferes with performing everyday activities. Alzheimer’s disease (AD) is the most common type of dementia. In 2021, an estimated 6.2 million adults in the US aged ≥65 have dementia, and the number is projected to be nearly 14 million by 2060. Women a have significantly higher risk of AD than men. About two thirds of patients with dementia are women. Mild cognitive impairment (MCI), representing the early stage of the disease, refers to a state in which the patient experiences a decline in short-term memory, or other cognitive domain, but with no significant impairment in everyday functioning. Though dementia mostly affects older adults, it is not a part of normal aging. Given that taking prevention at the early stage of MCI delays the development of the disease. It is of tremendous important to detect MCI during the early pre- symptomatic stage. Several studies observed that vascular, metabolic disorders, and inflammation are associated with risk of MCI, AD and AD related dementia (AD/ADRD). However, research gaps remain: (1) inconsistent findings were observed from the previous studies. (2) Large-scale population-based studies for AD and dementia risk are limited. (3) Although it is known that the risk of MCI and dementia are associated with changes in risk exposures, few studies tested the association between time-varying exposures and risk of outcomes. In the application, we aim at filling these gaps by using a rigorous study design to test the predictive values of vascular and metabolic disorders, inflammation, as well as genetic factors for the risk of incident MCI and dementia, and then develop a novel cumulative (combined) prediction index. We have 2 specific aims. Aim 1: To examine the association of vascular, metabolic and inflammatory biomarkers with risk of incident MCI and dementia in older women. Hypothesis: vascular, metabolic and inflammatory biomarkers, with time-varying measures significantly predict the risk of incident MCI and dementia in women aged 65-79, and these associations are modified by APOE gene (ε4 versus the other alleles). Aim 2: To develop a machine learning (ML)-enabled algorithm to predict individuals who are at high risk of incident MCI and dementia. Hypothesis: A novel and advanced risk prediction model (e.g., a multi-dimensional risk model using ML) that integrates predictive values of multiple risk factors and key covariates, will enhance the degree of the prediction for the risk of incident MCI and dementia. The proposed study addresses a significant public health challenge facing an aging population. The proposed study is innovative, characterized by (1) focusing on sex-specific study in older women; (2) addressing time-varying risk factors that may have significant predictive effects on the study outcomes. (3) We will test whether there are potential modification effects of APOE on the association between the study of exposures and outcomes. The scientifically promising aspects of the study are the fact that it is able to test the natural development of MCI and dementia using data from an unique large-scale population-based study, the Women’s Health Initiative Memory Study. Findings of the study will be disseminated through scientific meetings and research articles published in high impact journals, and add new insights into the prevention and control of MCI and dementia.

项目概要/摘要 痴呆症是一种在一定程度上影响思维、判断、记忆和其他认知领域的疾病 干扰日常活动的阿尔茨海默病 (AD) 是最常见的类型。 2021 年，美国估计有 620 万 65 岁以上的成年人患有痴呆症，这一数字为 预计到 2060 年，这一数字将接近 1400 万。女性患 AD 的风险明显高于男性。 三分之二的痴呆症患者是女性，属于早期认知障碍。 疾病的阶段，是指患者经历短期记忆衰退的状态，或 其他认知领域，但日常功能没有明显损害，尽管大多数情况下是痴呆。 考虑到在 MCI 的早期阶段采取预防措施，它不属于正常衰老的一部分。 延缓疾病的发展，在早期检测 MCI 非常重要。 一些研究观察到血管、代谢紊乱和炎症是有症状的阶段。 与 MCI、AD 和 AD 相关痴呆 (AD/ADRD) 的风险相关，但研究仍存在差距：(1) (2) 大规模人群研究 AD 和痴呆风险有限 (3) 尽管已知 MCI 和痴呆风险相关。 随着风险暴露的变化，很少有研究测试随时间变化的暴露与风险之间的关联 在应用中，我们的目标是通过使用严格的研究设计来测试结果来填补这些空白。 血管和代谢紊乱、炎症以及遗传因素对风险的预测价值 事件 MCI 和痴呆症，然后开发一个新的累积（组合）预测指数 我们有 2 个。 具体目标 1：检查血管、代谢和炎症生物标志物的关联。 老年女性发生 MCI 和痴呆的风险 假设：血管、代谢和痴呆。 炎症生物标志物，随时间变化的测量可显着预测 MCI 事件的风险， 65-79 岁女性患有痴呆症，这些关联被 APOE 基因改变（ε4 与其他基因相比） 目标 2：开发一种支持机器学习 (ML) 的算法来预测个体 发生 MCI 和痴呆症的高风险 假设：一种新颖且先进的风险预测模型（例如， 使用 ML 的多维风险模型），集成了多个风险因素和关键的预测值 协变量将增强对 MCI 和痴呆事件风险的预测程度。 拟议的研究解决了人口老龄化面临的重大公共卫生挑战。 拟议的研究具有创新性，其特点是（1）侧重于老年妇女的性别特异性研究；（2） (3) 解决可能对研究结果产生显着预测影响的随时间变化的风险因素。 我们将测试 APOE 对研究之间的关联是否存在潜在的修改作用 该研究的科学前景在于它能够 使用来自独特的大规模人群的数据来测试 MCI 和痴呆症的自然发展 研究，女性健康倡议记忆研究的研究结果将通过传播。 科学会议和在高影响力期刊上发表的研究文章，并增加新的见解 预防和控制 MCI 和痴呆症。