Project 4: Precision Methods for Assessing Brain Health and Age-related Cognitive Impairment

项目 4：评估大脑健康和年龄相关认知障碍的精确方法

• 批准号: 10689327
• 负责人: NICHOLAS Joseph SCHORK
• 金额: $ 38.8万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10689327
• 关键词: Address; Age-associated memory impairment; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Assessment tool; Behavior Therapy; Biological Assay; Biological Markers; Biometry; Cellular Phone; Classification; Clinical; Clinical Trials; Clinical Trials Design; Cognitive; Cognitive aging; Cohort Studies; Collection; Data; Data Analyses; Data Science Core; Data Set; Decision Trees; Development; Drug Targeting; Early Diagnosis; Environmental Risk Factor; Exhibits; Future; Gene Expression Profile; Genetic; Genotype-Tissue Expression Project; Goals; Health; Heterogeneity; Human; Impaired cognition; Individual; Informatics; Internet; Intervention; Intervention Studies; Intervention Trial; Light; Longevity; Maintenance; Measures; Mediation; Mendelian randomization; Meta-Analysis; Metadata; Methodology; Methods; Modeling; Molecular; Molecular Profiling; Monitor; Nature; Network Infrastructure; Nomograms; Nutritional; Pathway interactions; Patients; Performance; Personal Satisfaction; Physical Function; Physiological; Physiology; Preventive; Protocols documentation; Psychometrics; Public Domains; Public Health; Research; Research Design; Research Personnel; Risk; Risk Assessment; Sampling; Services; Sorting; Source; Subgroup; Systems Biology; Techniques; Technology; Testing; Theoretical model; Therapeutic; Tissues; Treatment Efficacy; Work; age related; brain health; care providers; causal model; cognitive change; cognitive function; cognitive testing; cohort; combat; data integration; design; drug development; functional decline; genome wide association study; handheld mobile device; healthspan; human old age (65+); improved; individual variation; insight; lifestyle factors; member; model building; multimodality; neuroimaging; novel; personalized intervention; pharmacologic; predictive modeling; preservation; prevent; prognostic model; prototype; risk prediction model; smartphone application; social factors; therapeutic target; therapy development; trial design; web-based intervention

SUMMARY/ABSTRACT: Project 4 Precision Methods for Assessing Brain Health and Age-related Cognitive Impairment and AD Combatting the growing public health and societal burdens associated with age-related cognitive impairment (ARCI) and cognitive decline will not be easy. What is needed are reliable risk assessment, early detection, and ultimately, intervention strategies for maintaining cognitive health at older ages. The development and implementation of such strategies are complicated by the number, complexity, and heterogeneous nature of the factors that contribute to ARCI. To understand this complexity, it will be necessary to collect and integrate different sources of information. These data will come from large samples of longitudinally characterized individuals through the use of assays designed to interrogate phenomena such as molecular, genetic, physiological, biometric, clinical, psychometric, social, lifestyle, and environmental factors. In addition, appropriate methods for integrating the data obtained from these assays and drawing comprehensive yet compelling inferences are essential. The research to be pursued in the proposed Precision Aging Network (PAN) will involve collecting comprehensive data on 1,620 older individuals face-to-face and over 50,000 individuals tracked longitudinally who are cognitively characterized based on a set of validated online tests. PAN Project 4 “Precision methods for assessing brain health and age-related cognitive impairment” is devoted to integrating these data, along with complementary data from the public domain, using a suite of techniques designed to identify factors contributing to the maintenance or vulnerability of cognitive function in the face of aging, Alzheimer’s disease (AD), and AD-related dementias (ADRD). Project 4 will work with Project 2 to explore predictive models of cognitive decline, as well as Project 3 to identify preventive and therapeutic targets. Data are to be generated as part of the Molecular Profiling (MP) Core, Cognitive Assessment and Neuroimaging (CAN) Core, and the Non-Invasive Technology (NIT) Core – all in the service of the large-scale human cohort studies to be pursued in Projects 1 - 3 of the proposed research. Project 4 will work closely with members of the Data Science (DS) Core to ensure that all data collected and analyzed across all Projects and Cores funnel seamlessly into a pipeline that will allow for reliable model building. Project 4 deliverables, which are meant to seed potential products to be used to combat ARCI and age-related cognitive decline, include prediction models to classify individuals into subgroups more amenable to possible interventional strategies; long-term prognostic models that suggest activities or daily living strategies to maintain cognitive health; monitoring strategies for individuals exhibiting cognitive changes that could be implemented on a mobile device; markers for intervention efficacy as well as potential drug targets and therapeutic molecules; and novel intervention trial designs enabled by PAN study findings. Project 4 will enable the PAN goals to develop individualized interventions to maintain health, well-being, and function to optimize cognitive healthspan and prevent or reduce the burden of ARCI, AD, or ADRD.

摘要/摘要：项目 4 评估大脑健康以及与年龄相关的认知障碍和 AD 的精确方法 应对与年龄相关的认知障碍相关的日益增长的公共卫生和社会负担 （ARCI）和认知能力下降并不容易，我们需要的是可靠的风险评估、早期发现、 最终，维持老年人认知健康的干预策略。 此类策略的实施因策略的数量、复杂性和异构性而变得复杂 为了理解这种复杂性，有必要收集和整合影响 ARCI 的因素。 这些数据将来自于纵向特征的大样本。 通过使用旨在询问分子、遗传、 此外，生理、生物特征、临床、心理、社会、生活方式和环境因素。 整合从这些测定中获得的数据并绘制全面的数据的适当方法 令人信服的推论至关重要。 (PAN) 将涉及面对面收集 1,620 名老年人和超过 50,000 名老年人的综合数据 根据一组经过验证的在线测试对个人进行纵向跟踪，并对其进行认知特征描述。 PAN 项目 4“评估大脑健康和年龄相关认知障碍的精确方法” 致力于使用一套集成这些数据以及来自公共领域的补充数据 旨在识别有助于维持或脆弱认知功能的因素的技术 项目 4 将与衰老、阿尔茨海默氏病 (AD) 和 AD 相关痴呆症 (ADRD) 合作。 项目 2 探索认知能力下降的预测模型，项目 3 确定预防和预防措施 数据将作为分子分析 (MP) 核心认知的一部分生成。 评估和神经影像 (CAN) 核心以及非侵入性技术 (NIT) 核心 – 全部包含在 为拟议研究的项目 1 - 3 中进行的大规模人类队列研究提供服务。 项目 4 将与数据科学 (DS) 核心成员密切合作，以确保收集的所有数据和 对所有项目和核心漏斗进行分析，无缝地纳入管道，从而实现可靠的模型 项目 4 可交付成果，旨在培育用于对抗 ARCI 和的潜在产品。 与年龄相关的认知能力下降，包括将个体分为更容易接受的亚组的预测模型 可能的干预策略；建议活动或日常生活的长期预后模型； 维持认知健康的策略；对表现出以下变化的个体的监测策略 可以在移动设备上实施干预效果标记以及潜在药物靶点； 和治疗分子；以及由 PAN 研究结果支持的新颖干预试验设计。 使 PAN 目标能够制定个性化干预措施，以维持健康、福祉和功能 优化认知健康寿命并预防或减轻 ARCI、AD 或 ADRD 的负担。