Leveraging genetics and environment to predict presymptomatic multiple sclerosis

利用遗传学和环境来预测症状前多发性硬化症

• 批准号: 8656454
• 负责人: Zongqi Xia
• 金额: $ 19.35万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8656454
• 关键词: Address; Affect; Algorithms; Area; Bioinformatics; Biological; Biological Markers; Blood; Blood Tests; Brain; Caliber; Chairperson; Child; Clinical; Clinical Management; Cohort Studies; Comorbidity; Complex; Computerized Medical Record; Data; Demyelinating Diseases; Development; Diagnosis; Diffusion Magnetic Resonance Imaging; Disease; Doctor of Philosophy; Early Intervention; Educational Activities; Environment; Environmental Exposure; Environmental Risk Factor; Epidemiology; Epstein-Barr Virus Infections; Event; Exhibits; Faculty; Family; Family member; First Degree Relative; Future; Genetic; Genetic Research; Genetic Risk; Genomics; Goals; Health; Health Transition; Healthcare Systems; Hospital Departments; Hospitals; Human; Human Genetics; Immune; Immunology; Incidental Findings; Individual; Inflammatory; Institutes; Institution; Investigation; Knowledge; Knowledge acquisition; Lead; Lesion; Life; Light; Magnetic Resonance Imaging; Marketing; Measures; Mentors; Methods; Molecular; Multiple Sclerosis; Multiple Sclerosis Lesions; Nerve Degeneration; Neurodegenerative Disorders; Neurologic; Neurologic Symptoms; Neurologist; Neurology; Neurosciences; Onset of illness; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Population; Populations at Risk; Predisposition; Process; Prospective Studies; Publishing; Relapse; Research; Research Personnel; Resources; Risk; Risk Estimate; Risk Factors; Role; Siblings; Smoking; Societies; Stratification; Structure; Symptoms; Syndrome; System; Techniques; Testing; Training; Translating; Translational Research; Translations; Vitamin D; Woman; Work; base; brain volume; career; career development; cerebral atrophy; clinical application; clinical care; clinically relevant; cohort; cost; cost effective; design; disability; disorder risk; efficacy testing; high risk; innovation; instructor; interest; medical schools; member; multidisciplinary; nervous system disorder; neuroimaging; neuroinflammation; novel; novel strategies; patient oriented research; peripheral blood; post-doctoral training; predictive modeling; prevent; progressive neurodegeneration; prospective; sample collection; skills; socioeconomics; tool; virtual; white matter

DESCRIPTION (provided by applicant): Candidate: I am a board-certified neurologist at the Brigham and Women's Hospital (BWH) and instructor at the Harvard Medical School (HMS). My research interest lies in the translation of discoveries in human genetics to clinical application n multiple sclerosis (MS) and related neuroimmunological and neurodegerative disorders. Leveraging my PhD thesis work in basic cellular and molecular neuroscience and ongoing post- doctoral training in statistical genetics and translational genomics, I will gain proficiency in th following new research areas through mentored project and structured educational activities: (1) lead a team of researchers and direct patient-oriented research; (2) design and implement studies that incorporate innovative biomarkers and neuroimaging outcomes of neuroinflammation and neurodegeneration; (3) devise clinically relevant applications of genetic information such as a genetic risk score for biomarker selection, disease prediction, and risk stratification; (4) pursue translational genetics research using Electronic Medical Record (EMR)-derived data and bioinformatics tools. These new translational research skills will enable me to achieve the career goal of making the transition to an independent investigator. Environment: I have assembled a multidisciplinary team of mentor (Dr. Philip De Jager, an expert in incorporating human genomics techniques into the study of complex neurologic disorders) and co-mentors (Dr. Issac Kohane, an expert in bioinformatics and predictive modeling; Dr. Daniel Reich, an expert in MS neuroimaging), consultants and senior faculty members with complementary expertise who will guide my research and promote my career development. This multi-layered mentor structure is embedded in a highly collaborative environment of unparalleled intellectual caliber that is part of Harvard-affiliated institutions: BWH Department o Neurology, BWH Institute of Neurosciences, Broad Institute, and HMS. Dr. Martin Samuels (Chairman of BWH Department of Neurology) and Dr. David Silbersweig (Chairman of BWH Institute of Neurosciences) are both supportive of my career plan. Research: An important challenge facing clinical care in multiple sclerosis (MS) is the lack of robust predictive tools to guide individualized risk stratification for subjects at risk of developing MS. The overall goal of the study is to test the efficacy of an algorithm that integrates existing genetic and environmental data into a single, individual estimate of the risk of developing MS. We hypothesize that such an algorithm can provide MS risk stratification for asymptomatic subjects at risk of MS, such as family members or patients with Radiologically Isolated Syndrome (RIS: asymptomatic individuals with incidental findings of MS-like lesions on brain magnetic resonance imaging, MRI). First, we will calculate our MS genetic and environmental risk score (GERSMS) in a cohort of 500 neurologically asymptomatic first-degree relatives of MS patients and assess whether the 100 subjects with the highest GERSMS exhibit an MS-associated peripheral blood biomarker profile when compared to the 100 subjects with the lowest GERSMS. Second, we will assess the efficacy of GERSMS in predicting the presence of MRI-defined MS lesions and other MS-related MRI outcomes in neurologically asymptomatic first- degree relatives and assess whether the 50 subjects with the highest GERSMS have more MS-like lesions, smaller brain volume or greater diffusion tensor imaging (DTI) changes that suggest loss of white matter tract integrity when compared to the 50 subjects with the lowest GERSMS. Finally, we will assess the efficacy of GERSMS in predicting conversion to clinical MS in RIS patients identified from existing EMR-derived data. Innovation: This proposal is innovative since it applies a novel approach to address an understudied question: how do we identify asymptomatic individuals who are at the highest risk of developing multiple sclerosis? The proposed study leverages a recently published, robust analytic method that is further enhanced by the most up-to-date genetic information and validated epidemiological data to produce a novel single estimate of risk for MS. We will test the method in two unique sample collections of asymptomatic subjects: (a) asymptomatic first-degree relatives of MS patients, (b) RIS patients identified from one of the largest EMR systems Specifically, we will assess the efficacy of this method in the selection of cutting-edge blood biomarkers and sensitive neuroimaging measures. If validated, our approach has the potential to make the study of asymptomatic subjects feasible by identifying the subset of subjects at the highest risk of transitioning from health to MS, thus opening up a whole new area of investigation in MS that could ultimately shed light on the design of strategies to prevent the onset of MS. Finally, the utilization of an EMR-derived virtual cohort for translational genetics research opens a rich resource for tackling challenging translational research questions that cannot be easily addressed by traditional cohort studies: (1) unforeseen co-morbidities of neurological disorders, (2) unrecognized neurological complications of medications on the market, particularly new immune modulating biologic agents. These future studies are not restricted to the field of MS and create future areas to develop independent investigation.

描述（由申请人提供）：候选人：我是布里格姆妇女医院（BWH）的委员会认证的神经学家和哈佛医学院（HMS）的讲师。我的研究兴趣在于将人类遗传学的发现转化为多发性硬化症（MS）和相关神经免疫学和神经退行性疾病的临床应用。利用我在基础细胞和分子神经科学方面的博士论文工作以及正在进行的统计遗传学和翻译基因组学博士后培训，我将通过指导项目和结构化的教育活动熟练掌握以下新的研究领域：（1）领导研究团队并指导以患者为导向的研究;（2）设计和实施研究，纳入创新的生物标志物和神经炎症和神经变性的神经影像学结果;（3）设计遗传信息的临床相关应用，例如用于生物标志物选择、疾病预测和风险分层的遗传风险评分;（4）利用电子病历（EMR）衍生的数据和生物信息学工具进行转化遗传学研究。这些新的翻译研究技能将使我能够实现过渡到独立调查员的职业目标。工作环境：我组建了一个多学科的导师团队（Philip De Jager博士，将人类基因组学技术纳入复杂神经系统疾病研究的专家）和共同导师（Issac Kohane博士，生物信息学和预测建模专家;丹尼尔赖希博士，MS神经影像学专家），顾问和具有互补专业知识的高级教师，他们将指导我的研究并促进我的职业发展。这种多层次的导师结构嵌入在一个高度协作的环境中，该环境具有无与伦比的智力水平，是哈佛附属机构的一部分：BWH神经病学系，BWH神经科学研究所，Broad研究所和HMS。Martin Samuels博士（BWH神经病学系主任）和大卫Silbersweig博士（BWH神经科学研究所主席）都支持我的职业规划。研究：多发性硬化症（MS）临床护理面临的一个重要挑战是缺乏强大的预测工具， 指导有MS风险的受试者的个体化风险分层。 该研究旨在测试一种算法的有效性，该算法将现有的遗传和环境数据整合到对MS发展风险的单个个体估计中。我们假设这种算法可以为处于MS风险的无症状受试者（如家庭成员或放射性孤立综合征患者）提供MS风险分层（RIS：在脑磁共振成像（MRI）上偶然发现MS样病变的无症状个体）。首先，我们将在500名MS患者的神经系统无症状一级亲属中计算MS遗传和环境风险评分（GERSMS），并评估GERSMS最高的100名受试者与GERSMS最低的100名受试者相比是否表现出MS相关的外周血生物标志物特征。其次，我们将评估GERSMS在预测神经学上无症状的一级亲属中MRI定义的MS病变和其他MS相关MRI结果的存在方面的有效性，并评估GERSMS最高的50名受试者是否具有更多的MS样病变，更小的脑体积或更大的扩散张量成像（DTI）与GERSMS最低的50名受试者相比，这些变化表明白色束完整性丧失。最后，我们将评估GERSMS在预测从现有EMR数据中识别的RIS患者转化为临床MS方面的有效性。创新：这项建议是创新的，因为它应用了一种新的方法来解决一个未充分研究的问题：我们如何识别无症状的个体谁是发展多发性硬化症的最高风险？这项拟议的研究利用了最近发表的强大的分析方法，该方法通过最新的遗传信息和经验证的流行病学数据进一步增强，以产生MS风险的新的单一估计。（a）MS患者的无症状一级亲属，（B）从最大的EMR系统之一鉴定的RIS患者。我们将评估该方法在选择尖端血液生物标志物和敏感神经影像学测量中的功效。如果得到验证，我们的方法有可能通过识别从健康过渡到MS的风险最高的受试者子集来使无症状受试者的研究可行，从而开辟了MS研究的全新领域，最终可以阐明预防MS发作的策略的设计。 转化遗传学研究的队列为解决传统队列研究难以解决的具有挑战性的转化研究问题提供了丰富的资源：（1）不可预见的神经系统疾病合并症，（2）市场上药物，特别是新的免疫调节生物制剂的未识别神经系统并发症。这些未来的研究并不局限于MS领域，并创造未来的领域，以发展独立的调查。