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