Application of latent and integrative approaches to understand the spectrum of cognitive aging

摘要 建议的研究项目以及我的指导和职业发展计划将使我成为一名 成功的认知老化和认知流行病学独立调查者，专注于纵向研究 认知功能的统计建模，使用协调综合分析协调数据集 方法、流行病学和密集的认知评估。非规范性认知能力下降是一个主要原因 在我们老龄化社会中的问题；在许多痴呆症中，据估计仅阿尔茨海默病(AD)就会影响 年龄在75岁到84岁之间的人占17%，每年的医疗保健费用为226亿美元 预算中不考虑其他痴呆症，其中一些痴呆症可能是可逆的，病因混合。 阿尔茨海默病和相关痴呆症状前阶段的异质性 导致临床治疗和预防试验的效果有限。为此，拟议的 研究项目和培训计划将调查老年人认知轨迹的个体差异； 它还将表征血管风险因素、身体功能和情绪状态的个体差异， 使用结构方程建模、潜变量建模和贝叶斯方法中的技术。这个 培训计划包括授课式课程作业、体验式学习和公认的结构化教程 不同专业领域的导师。这项研究计划利用了美国国立卫生研究院资助的一个庞大而富有的项目 爱因斯坦老龄化研究(EAS)的纵向数据集，以解析认知能力下降的异质性 并找出与不同轨迹相关的因素。通过使用潜在的 轨迹模型适用于来自EAS参与者的数据，以划分异质性并识别候选人 有资格接受早期干预的我们将能够确定可能使特定患者受益的干预类型 配置文件。我们建议使用使用血管危险因素(包括糖尿病、心血管疾病和 高血压)、身体功能(步速、握力、肺功能和虚弱)，以及 情绪状态(压力、抑郁和皮质醇)，因为这些状态是可以控制或逆转的，而且因为 它们还与认知障碍有关。通过对个体进行分类，我们将能够更好地预测 谁将从特定的治疗中受益。通过协调我们的结果并将我们的发现复制到其他 美国国立卫生研究院资助网络中的数据集，老龄化纵向研究的综合分析 (IALSA)，我们的结果将符合新的NIH关于严密性和重复性的指南；我们的结果也将 增加了可推广性。因此，我们的项目将对行为学和药理学产生影响。 干预措施。这些结果将影响轻度认知障碍(MCI)，症状前阿尔茨海默氏症 疾病和痴呆症研究，特别是在进一步向前分类和早期发现方面，以及在 为高危人群设计有针对性的干预计划。 关键词：认知流行病学；个体差异；异质性；MCI；阿尔茨海默病；血管 痴呆；纵向；综合分析；协调分析

ABSTRACT The proposed research project and my mentoring and career development plan will enable me to become a successful independent investigator in cognitive aging and cognitive epidemiology with a focus on longitudinal statistical modeling of cognitive function, harmonizing datasets using coordinated integrative analytic approaches, epidemiology, and intensive cognitive assessment. Non normative cognitive decline is a major issue in our aging society; amongst the many dementias, Alzheimer’s disease (AD) alone is estimated to affect 17% of individuals between the ages of 75 and 84, and accounts for $226 billion for our annual health-care budget without accounting for other dementias, some of which are potentially reversible, with mixed etiology. The heterogeneity among the presymptomatic stages of Alzheimer’s disease and related dementias contributes to the limited effectiveness of clinical treatment and prevention trials. To this end, the proposed research project and training plan will investigate individual differences in cognitive trajectories in older adults; it will also characterize individual differences in vascular risk factors, physical function, and emotional states, using techniques from structural equation modeling, latent variable modeling, and Bayesian approaches. The training plan includes didactic course work, experiential learning, and structured tutorials with well-recognized mentors in various areas of their expertise. The research plan takes advantage of a large and rich NIH-funded longitudinal dataset, the Einstein Aging Study (EAS) to parse heterogeneity in cognitive decline across individuals and to identify factors that are associated with different trajectories. Through the use of latent trajectory models fit to data from EAS participants to partition heterogeneity and to identify candidates who are eligible for early intervention we will be able to establish the type of intervention that may benefit specific profiles. We propose to use prediction models that employ vascular risk factors (including diabetes, CVD, and hypertension), physical function (gait speed, grip strength, lung function, and frailty), and measures on emotional states (stress, depression, and cortisol) because these can be controlled or reversed, and because they are also associated with cognitive impairment. By classifying individuals, we will be better able to predict who will benefit from specific treatments. By harmonizing our results and replicating our findings in other datasets that are part the NIH-funded network, the Integrative Analysis of Longitudinal Studies on Aging (IALSA), our results will comply with the new NIH guidelines on Rigor and Reproducibility; our results will also have increased generalizability. Our project will thus have implications for both behavioral and pharmacological interventions. These outcomes will impact mild cognitive impairment (MCI), presymptomatic Alzheimer’s disease, and dementia research, especially in furthering forward classification and early detection, and in designing targeted intervention programs for individuals at high risk. Key words: cognitive epidemiology; individual differences; heterogeneity; MCI; Alzheimer’s disease; vascular dementia; longitudinal; integrative analysis; coordinated analysis