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

项目摘要 描述阿尔茨海默病和相关痴呆症（ADRD）的生物学机制对于 确定预防和治疗痴呆症的有效策略。谨慎和大量的投资， 使用基于以下的AT（N）框架实现临床前阿尔茨海默病（AD）的表征： 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来自 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选择过程不同，因此研究结果具有普遍性。因此，在本发明中， 增加研究样本的多样性是必要的，但不足以取得普遍的研究结果。 我们的总体目标是开发工具和方法，以提高ADRD生物标志物的普遍性 问题研究越来越多的人认识到评估ADRD研究结果的普遍性是重要的，但 研究人员由于缺乏系统地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现有数据，也没有揭示 在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”），允许将研究结果推广到 感兴趣的外部人群。我们建议应用交通工具，以制定人口水平的估计， 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（Aim 1)以及风险因素对ADRD AT（N）生物标志物的影响（目标2），使用阿尔茨海默病的数据 神经影像学倡议（ADNI），法国MEMENTO队列和凯撒健康老龄化和多样化生活 经验（KHANDLE）和90后生活（LA90），两种新的、异常多样的ADRD生物标志物 具有协调措施的样品。从研究样本到总体的推广总是增加 估计效应相对于样本估计值的不确定性（例如，置信区间较宽， 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，没有工具来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（即从拟议研究中得出可推广的推论）。在Aim中 3.我们将根据社会人口统计数据，开发一个人口水平估计的幂计算器， 建议样本的组成，使研究人员能够评估研究设计的普遍性 阶段该项目推进了关键的分析工具，以提高现有ADRD生物标志物的普遍性 这些研究可以应用于新的生物标志物，并有助于优先考虑招募目标。发展运输 ADRD生物标志物研究的工具将帮助研究人员获得关于如何预防的最佳证据。 并在整个人群中有效治疗ADRD。

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.