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

项目摘要 随着世界人口老龄化，阿尔茨海默病(AD)患者的数量将 显著增加。对临床前AD的发病机制和生物标记物的了解 在过去的几年里得到了巨大的增长。然而，人们对如何利用生物标记物知之甚少。 在临床环境中筛选测试，以获得个性化的风险预测，以获得 对处于临床前状态的人数进行人口预测，并对公众进行评估 推迟临床前状态之间过渡的潜在干预对健康的影响。我们的 拟议的工作将通过利用多状态建模来解决新出现的问题来填补一个关键的空白 临床实践和公共卫生中的问题，由不断扩大的知识提出 AD生物标志物。目标1是进行系统的审查，以便制定一个框架 阿尔茨海默病临床前期进展的多状态模型 结合了生物标志物和相互竞争的死亡风险，并校准了模型的转变 费率。目标2是计算一个人一生中发生临床AD的绝对风险，基于 在生物标记物筛查上，这些筛查解释了相互竞争的死亡风险和筛查年龄。目标3 是开发临床前和临床阶段的美国人口预测 评估未来干预措施的潜在影响，这些干预措施可能会减少临床前 转换率。干预措施可能在临床前自然历史的不同阶段起作用。 我们的模型将被用来评估它们对人口的潜在影响 投射。我们的方法扩展了我们以前用于预测临床AD的计算方法 现在纳入生物标记物临床前疾病状态。我们的方法包括系统化 回顾、数学建模和蒙特卡洛模拟。拟议的研究是 具有重大意义和创新性，因为它将合成关于 将阿尔茨海默病的临床前阶段转化为统一的多状态模型 应用于临床实践和公共卫生。这项研究将提供一个平台， 纳入有关生物标记物的新信息，以实现持续更新和改进 临床AD风险的个体化估计、人口预测以及以下因素的影响 对这些预测的潜在干预。

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.