Imaging neurodegeneration in multiple sclerosis

多发性硬化症的影像学神经退行性变

• 批准号: 10330016
• 负责人: PETER A CALABRESI
• 金额: $ 59.65万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10330016
• 关键词: Address; African American; Algorithms; Atrophic; Biological; Biology; Brain; Brain imaging; C3 gene; Caring; Cerebrum; Characteristics; Clinical; Clinical Management; Clinical assessments; Complement; Complex; Custom; Data Pooling; Data Set; Decision Making; Development; Disabled Persons; Disease; Disease Outcome; Disease Progression; Enhancing Lesion; Ethnic Origin; Female; Ganglionic Layer; Genes; Genetic; Genetic Load; Genetic Predisposition to Disease; Genetic Risk; Genetic Variation; Genotype; Grant; Health; Image; Individual; Inflammatory; Inner Plexiform Layer; Life; Magnetic Resonance Imaging; Measures; Methodology; Methods; Modeling; Monitor; Multiple Sclerosis; Nerve Degeneration; Neuraxis; Neurodegenerative Disorders; Neurons; Neuroprotective Agents; Optical Coherence Tomography; Outcome; Pathology; Pathway interactions; Patient Care; Patient Care Management; Patients; Persons; Population Characteristics; Predisposition; Quality of life; Race; Resolution; Retina; Retinal Degeneration; Retinal Diseases; Retinal Ganglion Cells; Risk; Severity of illness; Site; Structure; Thalamic structure; Therapeutic; Thick; Thinness; Time; Toxic effect; Variant; Visual; aggressive therapy; axonal degeneration; burden of illness; caucasian American; central nervous system demyelinating disorder; clinical predictors; cognitive function; cohort; disability; disability risk; disease prognosis; efficacy testing; follow-up; ganglion cell; genetic analysis; genetic variant; gray matter; high resolution imaging; high risk; imaging genetics; imaging study; improved; individual patient; insight; large datasets; male; multiple sclerosis patient; multiple sclerosis treatment; negative affect; neuronal survival; novel; precision medicine; prediction algorithm; predictive modeling; predictive tools; recruit; research clinical testing; retinal imaging; risk variant; sex; tool

Project Summary One of the significant challenges facing treatment of people with multiple sclerosis (MS) is determining their individual likelihood of progression, as this information would significantly influence the type of therapy selected. Thus, developing specific tools to monitor and predict progression is critical to better manage patient care and to understand mechanisms of disease. We have been developing a multi-faceted approach to more readily monitor (through imaging) and predict (through both imaging and genetic analysis) disease progression in a real-time fashion. In the past cycle of this grant we demonstrated the utility of high resolution spectral domain optical coherence tomography (SD-OCT) and magnetic resonance imaging (3T MRI) in estimating disease burden in different CNS compartments. We showed that retinal degeneration occurs throughout the disease course and mirrors grey matter compartment atrophy in the cerebrum. A critical finding validating the clinical utility of this approach was that in a multicenter analysis of pooled data, a single OCT at baseline predicted risk of disability progression at 5 years of follow up. As MS is thought to have a strong genetic component, we sought to investigate whether there was an underlying genetic predisposition towards progression, which was made possible by the ability to utilize OCT in a real-time fashion to monitor degeneration and correlate with clinical outcome. We thus expanded the imaging study to include a genetic component in which we conducted a gene array to evaluate genetic variation among people with heterogeneous courses of MS and have preliminarily found that several gene variants in network pathways appear to be associated with more rapid rates of retinal neurodegeneration. The large data set that will be generated from these studies will also allow a corollary analysis in which we can begin to develop a risk profile model in which other population characteristics known to be associated with disease such as sex and ethnicity can be incorporated. The central hypotheses of the proposed studies are; that retinal ganglion layer thickness, thalamic and GM volumes predict 10 year disability across MS subtypes, that patients with high genetic load for gene variants in specific network pathways undergo faster neurodegeneration, and that combinations of OCT, MRI and genetic load measures may be used to develop clinically meaningful individual predictive scores for precision medicine. Aim 1: To determine whether baseline retinal ganglion layer thickness and thalamic and GM volumes predict 10 year disease outcomes. Aim 2: To determine whether genetic variation, sex and ethnicity influence rates of GCIP, thalamic, GM atrophy, and disability accumulation. Aim 3: To develop an algorithm disease progression model to predict disease outcome.

项目摘要 多发性硬化症（MS）患者治疗面临的重大挑战之一是确定 他们的个人进展可能性，因为这一信息将显著影响治疗类型 选定.因此，开发特定的工具来监测和预测进展对于更好地管理患者至关重要。 关心和了解疾病的机制。我们一直在开发一种多方面的方法， 易于监测（通过成像）和预测（通过成像和遗传分析）疾病进展 以实时的方式。在过去的周期中，我们展示了高分辨率光谱的实用性， 域光学相干断层扫描（SD-OCT）和磁共振成像（3 T MRI）在估计 不同CNS区室的疾病负担。我们发现视网膜变性发生在 病程和反映大脑灰质室萎缩。一个关键的发现证实了 这种方法的临床效用是，在对汇总数据的多中心分析中，基线时的单次OCT 预测5年随访时残疾进展的风险。由于MS被认为具有很强的遗传性， 成分，我们试图调查是否有潜在的遗传倾向， 进展，这是由于能够利用OCT以实时方式监测 变性并与临床结果相关。因此，我们扩大了成像研究，包括遗传 我们进行了一个基因阵列，以评估遗传变异的人之间， 并初步发现了网络通路中的几种基因变异 似乎与视网膜神经变性的更快速率有关。这一大型数据集将 从这些研究中产生的信息也将允许我们进行推论分析，从而我们可以开始制定风险预测 在该模型中，已知与疾病相关的其他人口特征，如性别和种族 可以合并。提出的研究的中心假设是：视网膜神经节层厚度， 丘脑和GM体积可预测MS亚型的10年残疾， 对于特定网络通路中的基因变异，神经退行性变更快， OCT、MRI和遗传负荷测量可用于开发具有临床意义的个体预测评分 用于精准医疗 目的1：确定是否基线视网膜神经节层厚度和丘脑和GM体积预测 10年疾病结局 目的2：确定遗传变异、性别和种族是否影响GCIP、丘脑、GM的发生率 萎缩和残疾积累。 目的3：开发一种预测疾病结局的算法疾病进展模型。