Personalized predictions of biomarker progression in Alzheimer's disease

阿尔茨海默病生物标志物进展的个性化预测

基本信息

批准号：
9975370
负责人：
Johannes Weickenmeier
金额：
$ 42.41万
依托单位：
STEVENS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9975370
关键词：
Affect Alzheimer disease detection Alzheimer&apos s Disease Alzheimer&apos s disease brain Alzheimer&apos s disease diagnosis Alzheimer&apos s disease model Alzheimer&apos s disease pathology Alzheimer&apos s disease patient Alzheimer&apos s disease related dementia Alzheimer’s disease biomarker American Amyloid Amyloid beta-Protein Amyotrophic Lateral Sclerosis Anatomy Axon Behavioral Biological Markers Biological Process Biomedical Engineering Brain Cessation of life Characteristics Clinical Clinical Data Computer Models Coupling Data Dementia Deposition Development Diagnostic Diffusion Disease Disease Management Disease Progression Early Diagnosis Elderly Elements Equation Exposure to Functional disorder Goals Graph Health Image Immunoassay Impaired cognition Individual Intervention Magnetic Resonance Imaging Maps Measures Medical Medical Imaging Memory Loss Mission Modeling Monitor Nerve Degeneration Neurodegenerative Disorders Neurofibrillary Tangles Older Population Organ Outcome Parkinson Disease Patients Pattern Pharmacology Phase Physics Physiological Positron-Emission Tomography Prions Proliferating Proteins Public Health Radioactive Tracers Reaction Research Resources Role Scanning Senile Plaques Shapes Social Impacts Spatial Distribution Speed Spinal Spinal Puncture Structure Symptoms Technology Testing Thinness Time United States National Institutes of Health Ventricular age related base biochemical model brain shape brain volume cerebral atrophy chronic traumatic encephalopathy clinical practice computer framework connectome diagnostic biomarker economic impact effective therapy extracellular follow-up graph theory improved longitudinal positron emission tomography neglect neuroimaging neuron loss neurotoxic novel novel marker personalized medicine personalized predictions predictive modeling prion-like progression marker simulation tau Proteins tau aggregation tool tractography treatment strategy ward

项目摘要

PROJECT SUMMARY Alzheimer's disease (AD) is the most common late life dementia and affects approximately 6 million Americans, therefore creating a huge social and economic impact. More importantly, the persistent demographic shift to- wards an older population will cause the number of AD patients to double within the next 20 years. Clinically, AD is deﬁned by progressively worsening memory loss, cognitive decline, behavioral changes, and ultimately death. Pathophysiologically, AD is characterized by the gradual accumulation of toxic protein deposits that spread through the brain and eventually result in wide-spread neuron cell death and cerebral atrophy (CA). However, de- spite signiﬁcant advances in our understanding of pathophysiology in AD and related dementias (ADRD), neither a deﬁnitive, antemortem diagnostic tool nor a pharmacological cure exist today. The early detection of AD and ADRD has proven particularly challenging because the biological processes most often precede the onset of clinical symptoms by up to two decades and, therefore, progress unnoticed during a time at which intervention is considered to be most effective. Thus far, ﬁve biomarkers have been developed to visualize established AD hallmark features: toxic deposits of β-amyloid and tau proteins and neurodegeneration associated with cortical thinning and brain volume loss. These biomarkers are invasive and resource intensive measures, however, and involve the exposure to radioactive tracers in amyloid and tau PET or a lumbar puncture for CSF immunoassays. In this project we propose a novel mechanobiological disease model for AD which predicts the prion-like protein progression and subsequent structural changes on the organ-level in space and time. Our com- putational approach utilizes medical images and physics-based modeling to provide subject-speciﬁc simulations of these common features in AD with the goal to minimize exposure to invasive measures. We hypothesize that our model is a reliable biomarker to enable earlier diagnosis of dementia type and monitoring of disease pro- gression which would allow for the development of more effective and personalized treatment strategies. To test our hypothesis, we will use longitudinal biomarker data (amyloid/tau PET and MRI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and validate our model in ten subjects. For each subject, we will reconstruct their brain in a computer model, use their initial PET scans to calibrate our AD progression model, and compare our subsequent numerical predictions of biomarker progression against their follow up scans. This approach aims at integrating existing technologies that visualize temporal and spatial patterns of individual biomarkers into a noninvasive disease model for AD and ADRD. By capturing the fundamental mechanisms of AD and ADRD, we can, for the ﬁrst time, systematically study organ-level features of individual dementias. As such, this study is particularly relevant to public health because early diagnosis of dementia type and a reliable tool to track disease progression will have a big clinical impact on disease management and minimize frequent expo- sure to invasive biomarkers. Demonstrating the role of mechanobiology in AD pathology will inherently advance our basic understanding of other neurodegenerative diseases, such as Parkinson's disease, amyotrophic lateral sclerosis (ALS), and chronic traumatic encephalopathy (CTE).

项目概要阿尔茨海默病 (AD) 是最常见的晚年痴呆症，影响大约 600 万美国人，因此，产生了巨大的社会和经济影响，更重要的是，持续的人口转变—— 临床上，人口老龄化将导致 AD 患者数量在未来 20 年内翻一番。 AD 的定义是逐渐恶化的记忆丧失、认知能力下降、行为改变，并最终导致从病理生理学角度来看，AD 的特点是有毒蛋白质沉积物逐渐积累并扩散。通过大脑，最终导致广泛的神经元细胞死亡和脑萎缩（CA）。尽管我们对 AD 和相关痴呆 (ADRD) 病理生理学的理解取得了重大进展，但目前尚不存在明确的生前诊断工具或药物治疗方法。 ADRD 已被证明特别具有挑战性，因为生物过程通常先于 ADRD 的发生临床症状长达二十年，因此，在干预期间，进展未被注意到迄今为止，已开发出五种生物标志物来可视化已建立的 AD。标志性特征：β-淀粉样蛋白和 tau 蛋白的毒性沉积以及与皮质相关的神经变性然而，这些生物标志物是侵入性和资源密集型的措施。涉及暴露于淀粉样蛋白和 tau PET 中的放射性示踪剂或用于脑脊液免疫测定的腰椎穿刺。在这个项目中，我们提出了一种新的 AD 机械生物学疾病模型，该模型可以预测类朊病毒蛋白质进展以及随后在空间和时间上的器官水平的结构变化。推论方法利用医学图像和基于物理的建模来提供特定于主题的模拟 AD 的这些共同特征，目的是尽量减少接触侵入性措施。我们的模型是一种可靠的生物标志物，可以早期诊断痴呆症类型并监测疾病进展情况回归将允许开发更有效和个性化的治疗策略。根据我们的假设，我们将使用来自阿尔茨海默病的纵向生物标志物数据（淀粉样蛋白/tau PET 和 MRI）神经影像计划 (ADNI) 并在十个受试者中验证我们的模型对于每个受试者，我们将重建他们的模型。计算机模型中的大脑，使用他们最初的 PET 扫描来校准我们的 AD 进展模型，并比较我们的该方法的目的是对后续扫描的生物标志物进展进行数值预测。现有的整合技术可将单个生物标志物的时间和空间模式可视化为 AD 和 ADRD 的非侵入性疾病模型通过捕获 AD 和 ADRD 的基本机制，我们首次可以系统地研究个体痴呆症的器官水平特征。与公共卫生特别相关，因为痴呆症类型的早期诊断和跟踪的可靠工具疾病进展将对疾病管理产生重大临床影响，并最大限度地减少频繁暴露证明机械生物学在 AD 病理学中的作用必然会取得进展。我们对其他神经退行性疾病的基本了解，例如帕金森病、肌萎缩侧索硬化症硬化症（ALS）和慢性创伤性脑病（CTE）。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

An inverse modelling study on the local volume changes during early morphoelastic growth of the fetal human brain.

胎儿人脑早期形态弹性生长过程中局部体积变化的逆模型研究。

DOI：
发表时间：
2021
期刊：
影响因子：
0
作者：
Wang, Z;Martin, B;Weickenmeier, J;Garikipati, K
通讯作者：
Garikipati, K

Brain Shape Changes Associated With Cerebral Atrophy in Healthy Aging and Alzheimer's Disease.

健康老龄化和阿尔茨海默氏病中与脑萎缩相关的大脑形状变化。

DOI：
发表时间：
2021-07
期刊：
影响因子：
0
作者：
Blinkouskaya, Yana;Weickenmeier, Johannes
通讯作者：
Weickenmeier, Johannes

Brain aging mechanisms with mechanical manifestations.

具有机械表现的脑衰老机制。

DOI：
发表时间：
2021-12
期刊：
影响因子：
5.3
作者：
Blinkouskaya, Yana;Caçoilo, Andreia;Gollamudi, Trisha;Jalalian, Shima;Weickenmeier, Johannes
通讯作者：
Weickenmeier, Johannes

Exploring the multiphysics of the brain during development, aging, and in neurological diseases.

探索大脑在发育、衰老和神经系统疾病过程中的多物理现象。