Optimizing generalizability of biomarker studies for Alzheimer’s disease and related dementias with epidemiologic tools

利用流行病学工具优化阿尔茨海默病和相关痴呆症生物标志物研究的普遍性

• 批准号: 10359225
• 负责人: Elizabeth Rose Mayeda
• 金额: $ 58.25万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10359225
• 关键词: Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer’s disease biomarker; Amyloid; Biological; Biological Markers; Brain; Cognitive; Confidence Intervals; Data; Data Coordinating Center; Dementia; Disease; Epidemiology; Future; General Population; Genetic Risk; Genotype; Goals; Health; Impaired cognition; Investments; Life; Life Experience; Magnetic Resonance Imaging; Measures; Memory Loss; Outcome; Participant; Population; Positron-Emission Tomography; Prevalence; Process; Race; Research; Research Design; Research Personnel; Risk Factors; Sampling; Sampling Studies; Structure; Symptoms; Uncertainty; Weight; Work; analytical method; analytical tool; apolipoprotein E-4; base; cardiometabolism; cognitive change; cohort; dementia risk; depressive symptoms; design; healthy aging; improved; interest; magnetic resonance imaging biomarker; mild cognitive impairment; neuroimaging; novel marker; pre-clinical; prevent; recruit; sociodemographic group; sociodemographics; tau Proteins; tool; user-friendly

PROJECT SUMMARY Characterizing the biological mechanisms of Alzheimer's disease and related dementias (ADRD) is crucial for identifying effective strategies to prevent and treat dementia. Careful and substantial investments have enabled characterization of preclinical Alzheimer's disease (AD) using the AT(N) framework based on biomarkers, but the limited diversity of biomarker study samples may limit generalizability of findings from these studies. Unfortunately, early attempts to increase diversity have not solved the problem of non- generalizable study results due to differential selection processes across sociodemographic groups. Thus, increasing diversity of study samples is necessary, but not sufficient, for achieving generalizable study results. Our overall objective is to develop tools and approaches to enhance the generalizability of ADRD biomarker studies. It is increasingly recognized that evaluating generalizability of ADRD study results is important, but researchers are stymied by lack of tools for systematically evaluating generalizability of findings. Standard analytic methods do not take full advantage of currently available data and do not reveal the extent of uncertainty in estimates when generalizing to the entire population. New epidemiologic and statistical tools, including weighting and g-computation (“transport tools”), allow for generalizing results from a study to an external population of interest. We propose to apply transport tools to develop population-level estimates of the predictive accuracy of ADRD AT(N) biomarkers on cognitive outcomes and prevalence of preclinical AD (Aim 1) and effects of risk factors on ADRD AT(N) biomarkers (Aim 2) using data from Alzheimer's Disease Neuroimaging Initiative (ADNI), the French MEMENTO cohort, and Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) and Life After 90 (LA90), two newly available, exceptionally diverse ADRD biomarker samples with harmonized measures. Generalizing from a study sample to a population always increases uncertainty in estimated effects relative to estimates in the sample (e.g. wider confidence intervals and reduced power), but the magnitude of added uncertainty due to generalizing depends on the composition of the sample. Currently, there are no tools to evaluate how the composition of a proposed study will impact statistical power to detect population-representative effects (i.e. draw generalizable inferences from the proposed study). In Aim 3, we will develop a power calculator for population-level estimates based on the sociodemographic composition of the proposed sample, enabling researchers to evaluate generalizability in the study design stage. This project advances critical analytic tools to improve generalizability of existing ADRD biomarker studies, which can be applied to novel biomarkers and help prioritize recruitment goals. Developing transport tools for ADRD biomarker research will help researchers obtain the best possible evidence on how to prevent and effectively treat ADRD in the entire population.

项目概要 描述阿尔茨海默病和相关痴呆 (ADRD) 的生物学机制对于 确定预防和治疗痴呆症的有效策略。谨慎而大量的投资 使用基于 AT(N) 框架的临床前阿尔茨海默氏病 (AD) 特征分析 生物标志物，但生物标志物研究样本的有限多样性可能会限制研究结果的普遍性 这些研究。不幸的是，早期增加多样性的尝试并没有解决非 由于社会人口群体之间的差异选择过程，可推广的研究结果。因此， 为了获得可推广的研究结果，增加研究样本的多样性是必要的，但还不够。 我们的总体目标是开发工具和方法来增强 ADRD 生物标志物的普遍性 研究。人们越来越认识到评估 ADRD 研究结果的普遍性很重要，但是 研究人员因缺乏系统评估研究结果的普遍性的工具而受到阻碍。标准 分析方法没有充分利用当前可用的数据，也没有揭示问题的程度 推广到整个人口时估计的不确定性。新的流行病学和统计工具， 包括加权和 g 计算（“传输工具”），允许将研究结果概括为 外部感兴趣人群。我们建议应用交通工具来对人口水平进行估计 ADRD AT(N) 生物标志物对认知结果和临床前 AD 患病率的预测准确性（目标 1) 以及危险因素对 ADRD AT(N) 生物标志物的影响（目标 2），使用阿尔茨海默氏病的数据 神经影像倡议 (ADNI)、法国 MMENTO 队列和 Kaiser 健康老龄化与多元化生活 Experiences (KHANDLE) 和 Life After 90 (LA90)，两个新推出的、极其多样化的 ADRD 生物标志物 具有统一措施的样本。从研究样本到总体的推广总是会增加 估计效果相对于样本估计的不确定性（例如更宽的置信区间和减少的 功率），但由于泛化而增加的不确定性的大小取决于样本的组成。 目前，没有工具可以评估拟议研究的组成将如何影响统计功效 检测人群代表性效应（即从拟议的研究中得出普遍的推论）。瞄准 3、我们将开发一个基于社会人口统计的人口水平估计的功效计算器 拟议样本的组成，使研究人员能够评估研究设计的普遍性 阶段。该项目改进了关键分析工具，以提高现有 ADRD 生物标志物的通用性 研究，可应用于新型生物标志物并有助于优先考虑招募目标。发展交通 ADRD 生物标志物研究工具将帮助研究人员获得有关如何预防 ADRD 的最佳证据 并有效治疗整个人群的 ADRD。