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: 10270198
• 负责人: NICHOLAS Joseph SCHORK
• 金额: $ 39.26万
• 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/10270198
• 关键词: Address; Age; Age-associated memory impairment; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Assessment tool; Behavior Therapy; Biological; Biological Assay; Biological Markers; Biometry; Cellular Phone; 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; Ensure; Environmental Risk Factor; Exhibits; Future; Gene Expression Profile; Genetic; Genotype-Tissue Expression Project; Goals; Health; Heterogeneity; Human; Impaired cognition; Individual; Internet; Intervention; Intervention Studies; Intervention Trial; Light; Longevity; Maintenance; Measures; Mediation; Mendelian randomization; Meta-Analysis; Metadata; Methodology; Methods; Modeling; Molecular; Molecular Genetics; Molecular Profiling; Monitor; Nature; Network Infrastructure; Nomograms; Nutritional; Pathway interactions; Patients; Performance; Personal Satisfaction; Pharmacology; Physical Function; Physiological; Physiology; Plant Roots; Preventive; Protocols documentation; Psychometrics; Public Domains; Public Health; Research; Research Design; Research Personnel; Risk; Risk Assessment; Sampling; Seeds; Services; Source; Subgroup; Systems Biology; Techniques; Technology; Testing; Theoretical model; Tissues; Treatment Efficacy; Work; age related; base; brain health; care providers; causal model; cognitive change; cognitive function; cognitive testing; cohort; combat; design; drug development; functional decline; genome wide association study; handheld mobile device; healthspan; individual variation; insight; lifestyle factors; member; model building; multimodality; neuroimaging; novel; personalized intervention; predictive modeling; preservation; prevent; prognostic model; prototype; risk prediction model; smartphone Application; social factors; targeted treatment; 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.

总结/摘要：项目4 评估脑健康的精确方法以及与脑功能相关的认知障碍和AD 应对与年龄相关的认知障碍相关的日益沉重的公共卫生和社会负担 （ARCI）和认知能力下降并不容易。需要的是可靠的风险评估、早期发现， 最终，在老年人中维持认知健康的干预策略。发展和 这些战略的实施因数量、复杂性和异质性而变得复杂， 导致ARCI的因素。为了理解这种复杂性，有必要收集和整合 不同的信息来源。这些数据将来自纵向表征的大样本， 通过使用设计用于询问诸如分子，遗传， 生理、生物特征、临床、心理测量、社会、生活方式和环境因素。此外，本发明还提供了一种方法， 用于整合从这些测定中获得的数据并绘制全面但 令人信服的推论是必不可少的。拟议的精密老化网络将进行的研究 (PAN)将涉及收集1,620名老年人面对面的全面数据， 基于一组经验证的在线测试对其进行认知表征的纵向跟踪的个体。 PAN项目4“评估大脑健康和年龄相关认知障碍的精确方法”是 致力于整合这些数据，沿着来自公共领域的补充数据，使用一套 旨在识别有助于维持或脆弱性认知功能的因素的技术， 面对衰老、阿尔茨海默病（AD）和AD相关痴呆（ADRD）。项目4将与 项目2旨在探索认知能力下降的预测模型，项目3旨在确定预防性和 治疗目标数据将作为分子特征分析（MP）核心的一部分生成，认知 评估和神经成像（CAN）核心，以及非侵入性技术（NIT）核心-全部在 为拟议研究项目1 - 3的大型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 在所有项目和核心中进行分析，无缝地汇集到管道中， 建设项目4交付成果，旨在为用于打击ARCI的潜在产品提供种子， 与年龄相关的认知能力下降，包括预测模型，将个体分为更容易接受的亚组 可能的干预策略;建议活动或日常生活的长期预后模型 保持认知健康的策略;对表现出认知变化的个体的监测策略， 可以在移动终端上实现;干预功效以及潜在药物靶点的标记 和治疗分子;以及PAN研究结果实现的新型干预试验设计。项目4将 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，预防或减轻ARCI、AD或ADRD的负担。