EFFECTS OF VULNERABILITY AND RESILIENCY ON BRAIN HEALTH DURING THE MID-TO-LATE-LIFE TRANSITION

项目2摘要 有效改善阿尔茨海默氏痴呆（AD）或AD相关痴呆（ADRD）的疾病治疗 目前还没有，这强调了迫切需要确定大脑健康保护因素，以减少风险， 认知能力下降、AD和ADRD。影响可改变风险因素影响的关键时间是 从中年过渡到晚年（50至80岁）。研究还表明，炎症在 神经变性和破坏大脑炎症可能会降低痴呆症的风险。因此，确定 这些脆弱性/弹性因素对认知健康和拐点的相对贡献， 具体的减少风险干预措施可能最为有效。人类连接组计划（Human Connectome Project，HCP） 表明了一种强的正-负模式的人口协变， 和行为。这个项目提供了一个前所未有的机会，在一个长期的过程中纵向研究这些因素。 当生活方式因素开始启动大脑健康下降轨迹时，即为关键拐点。阐明 在这些关系的基础上，本项目将致力于实现以下以中年到晚年过渡为重点的具体目标： (1)确定可改变的脆弱性和复原力因素在中年到晚年过渡中的影响 （运动、睡眠、饮食、压力（P1）;健康指标（体重指数（BMI）、血压（BP）、胰岛素） 阻力（HbA 1c，胆固醇），结构连接（用扩散张量成像（DTI）测量）; 功能连接（静息状态功能连接（rsFC）;任务fMRI）和认知（2）确定 AD风险因素和标志物与可改变的生活方式因素和健康标志物对AD的独特贡献 大脑和认知变化的轨迹跨越中晚期的生活过渡使用横截面和纵向 （3）确定AABC中连接大脑和认知结果的最可能的结构模型 AD生物标志物、基线认知、年龄、AD风险和生活方式的纵向数据。4)与其它 项目和中心作为一个整体来研究大脑和认知老化的终身非线性轨迹， 抗AD能力、风险和恢复能力。为了实现这些目标，我们将对764名患者进行纵向随访 受试者处于中晚期过渡期（50至80岁），包括评估生活方式和恢复力的措施 (e.g.,活动记录、睡眠、饮食），并与核心和其他项目协调，以纳入其他措施， 与这些目标相关的变量。随着证据的积累，AD风险的缓解可以延迟或预防 因此，有必要对痴呆症风险最高的人群进行识别、告知和干预。启动时 早期，即使是相对适度的风险降低也可能转化为疾病发病率的显著下降。这个项目 提供了一个机会，利用一个经过充分研究的正常老化的中年和老年人队列，其中许多人 十年的纵向数据。

ABSTRACT FOR PROJECT 2 An effective disease-modifying treatment for Alzheimer’s dementia (AD) or AD related dementias (ADRD) is not available, underscoring the pressing need to identify brain health protective factors that reduce risk for cognitive decline, AD, and ADRD. A critical time that influences the impact of modifiable risk factors is the transition from midlife to late life (50 to 80 years). Research also has demonstrated the role of inflammation in neurodegeneration, and disrupting brain inflammation may reduce dementia risk. It is thus critical to determine relative contributions of these vulnerability/resiliency factors to cognitive health and inflection points when specific risk reduction interventions may be most effective. Findings from the Human Connectome Project (HCP) indicate a strong positive-negative mode of population covariation that links brain connectivity, demographics and behavior. This project offers an unprecedented opportunity to examine such factors longitudinally during a critical inflection point when lifestyle factors begin to initiate a downward brain health trajectory. To elucidate these relationships, this project will address the following specific aims focused on the mid to-late-life transition: (1) Determine the effects of modifiable vulnerability and resilience factors in the mid-to late-life transition (exercise, sleep, diet, stress (with P1); measures of health (Body Mass Index (BMI), blood pressure (BP), insulin resistance (HbA1c), cholesterol), on structural connectivity (measured with Diffusion Tensor Imaging (DTI); functional connectivity(resting state functional connectivity (rsFC); task fMRI) and cognition (2) Determine the unique contributions of AD risk factors and markers vs. modifiable lifestyle factors and health markers on the trajectory of brain and cognitive change across the mid-late life transition using cross-sectional and longitudinal analysis; (3) Identify the most likely structural model that connects brain and cognitive outcomes in the AABC longitudinal data with AD biomarkers, baseline cognitive, age, AD risk and lifestyle. 4) Integration with other projects and the Center as a whole to examine lifetime, non-linear trajectories of brain and cognitive aging, resistance to AD, risk, and resilience. To accomplish these aims, we will perform longitudinal follow-up of 764 subjects in the mid- to late-life transition (ages 50 to 80) and include measures assessing lifestyle and resilience (e.g., actigraphy, sleep, diet) and coordinate with the cores and other projects to include other measures and variables relevant to these aims. With the accumulation of evidence that mitigation of AD risk can delay or prevent the disease, there is a need to identify, inform and intervene in people at greatest dementia risk. When started early, even relatively modest risk reduction may translate to significant declines in disease incidence. This project offers the opportunity to leverage a well-studied cohort of normal aging middle-aged and older adults, many with a decade of longitudinal data.