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Alzheimer's Disease Risks and Projections Using Multi-State Models with Biomarkers

使用具有生物标志物的多状态模型进行阿尔茨海默病风险和预测

基本信息

批准号：
9447097
负责人：
RONALD S BROOKMEYER
金额：
$ 23.11万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2021-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9447097
关键词：
Address Age Alzheimer&apos s Disease Alzheimer&apos s disease risk Amyloid beta-Protein Anatomy Biological Markers Cerebrospinal Fluid Cessation of life Clinical Dementia Diagnosis Disease Functional disorder Future Goals Individual Intervention Joints Knowledge Magnetic Resonance Imaging Methods Modeling Monte Carlo Method National Institute on Aging Natural History Nerve Degeneration Pathogenesis Persons Population Population Projection Positron-Emission Tomography Process Public Health Research Risk Risk Estimate Statistical Methods Synapses Testing Update Work abeta deposition amyloid imaging base clinical application clinical practice clinical risk fluorodeoxyglucose positron emission tomography innovation lifetime risk mathematical model neuron loss personalized risk prediction pre-clinical public health relevance screening systematic review tau Proteins

项目摘要

Project Summary As the world's population ages, the numbers of persons with Alzheimer's disease (AD) will significantly increase. Knowledge about the pathogenesis of preclinical AD and biomarkers has grown enormously in the last few years. However, little is known about how to utilize biomarker screening tests in clinical settings to obtain individualized risk predictions, to obtain demographic projections of numbers of persons in preclinical states, and to evaluate the public health impact of potential interventions that delay transitions between preclinical states. Our proposed work will fill a critical void by utilizing multistate modeling to address emerging questions in clinical practice and public health that are raised by the expanding knowledge of AD biomarkers. Aim 1 is to perform a systematic review in order to develop a framework for a multi-state model for progression through the pre-clinical period of Alzheimer's disease that incorporates biomarkers and competing risks of death, and to calibrate the model's transition rates. Aim 2 is to calculate absolute risks of developing clinical AD during one's lifetime based on biomarker screenings that account for competing risks of death and ages at screens. Aim 3 is to develop U.S. population projections of numbers of persons in preclinical and clinical states, and to evaluate the potential impact of future interventions that may decrease preclinical transition rates. Interventions may act at various stages of the natural history of preclinical disease, and our models will be used to evaluate their potential effects on population projections. Our approach extends our previous calculation methods used to project clinical AD to now incorporate biomarker preclinical disease states. Our methods include systematic review, mathematical modeling, and Monte Carlo simulations. The proposed research is significant and innovative because it will synthesize new biomarker evidence about the preclinical period of Alzheimer's disease into a unifying multi-state model that will have application for clinical practice and public health. The research will provide a platform for incorporating new information about biomarkers to enable ongoing updating and refinement of individualized estimates of risks of clinical AD, population projections, and the impact of potential interventions on those projections.

项目摘要随着世界人口的老龄化，阿尔茨海默病（AD）患者的人数将增加，显著增加。关于临床前AD的发病机制和生物标志物的知识在过去的几年里有了巨大的增长。然而，如何利用生物标志物知之甚少在临床环境中进行筛选测试，以获得个性化的风险预测，处于临床前状态的人数的人口统计学预测，并评估公众延迟临床前状态之间过渡的潜在干预措施对健康的影响。我们拟议的工作将填补一个关键的空白，利用多态建模，以解决新兴的在临床实践和公共卫生的问题，提出了不断扩大的知识， AD生物标志物。目的1是进行系统的评价，以便为阿尔茨海默病临床前阶段进展的多状态模型，结合生物标志物和死亡的竞争风险，并校准模型的过渡 rates.目的2是计算一个人一生中发生临床AD的绝对风险，生物标志物筛查，解释了筛查时死亡和年龄的竞争风险。目标3 是制定美国人口预测的人数在临床前和临床国家，并评估未来干预措施的潜在影响，转换率。干预措施可能会在临床前自然史的各个阶段发挥作用疾病，我们的模型将用于评估其对人口的潜在影响预测。我们的方法扩展了我们以前用于预测临床AD的计算方法纳入临床前疾病状态的生物标志物。我们的方法包括系统化回顾，数学建模和蒙特卡罗模拟。拟议的研究是重要和创新，因为它将综合新的生物标志物证据，将阿尔茨海默病的临床前阶段转化为统一的多状态模型，用于临床实践和公共卫生。该研究将提供一个平台，整合关于生物标志物的新信息，以使生物标志物的持续更新和完善成为可能。临床AD风险的个体化估计、人口预测以及对这些预测的潜在干预。