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 评估大脑健康和与年龄有关的认知障碍和AD的精确方法 打击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伯恩斯 （ARCI）和认知能力下降并不容易。需要的是可靠的风险评估，早期检测， 最终，在老年时期维持认知健康的干预策略。发展和 此类策略的实施使数量，复杂性和异质性质变得复杂 导致ARCI的因素。要了解这种复杂性，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 通过使用旨在审问现象（例如分子，遗传， 物理，生物识别，临床，心理测量，社会，生活方式和环境因素。此外， 合适的方法集成了从这些测定中获得的数据并全面绘制 令人信服的推论至关重要。该研究将在拟议的精确老化网络中进行 （PAN）将涉及收集有关1,620名老年人面对面的全面数据，超过50,000 基于一组经过验证的在线测试的纵向跟踪认知表征的个人。 PAN项目4“评估大脑健康和与年龄有关的认知障碍的精确方法”是 专门用于集成这些数据，以及来自公共领域的完整数据，使用一套 旨在确定有助于认知功能维持或脆弱性的因素的技术 衰老，阿尔茨海默氏病（AD）和与广告相关的痴呆症（ADRD）的面孔。项目4将与 项目2探索认知能力下降的预测模型以及项目3，以识别预防性和 治疗靶标。数据应作为分子谱分析（MP）核心的一部分生成认知 评估和神经影像学（CAN）核心以及非侵入性技术（NIT）核心 - 全部 在拟议的研究的项目1-3中将进行大规模人类队列研究的服务。 项目4将与数据科学（DS）核心成员紧密合作，以确保收集的所有数据和 在所有项目和核心之间进行分析，无缝地漏斗到管道中，这将允许可靠的模型 建筑。项目4的可交付成果，该产品旨在播种潜在的产品，以对抗Arci和 与年龄相关的认知下降，包括预测模型，将个体分类为子组更合理 可能采取介入策略；长期预后模型建议活动或日常生活 维持认知健康的策略；监视表现出认知变化的个人的策略 可以在移动设备上实现；干预效率和潜在药物目标的标记 和治疗分子； PAN研究结果实现了新颖的干预试验设计。项目4 Will 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 优化认知健康范围并预防或减少ARCI，AD或ADRD的燃烧。