Statistical Methods for Integrating Mixed-type Biomarkers and Phenotypes in Neurodegenerative Disease Modeling

在神经退行性疾病模型中整合混合型生物标志物和表型的统计方法

• 批准号: 10583203
• 负责人: Yuanjia Wang
• 金额: $ 51.43万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-15 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10583203
• 关键词: Address; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease related dementia; Autopsy; Back; Behavioral; Biological Markers; Biology; Blood; Brain; Brain imaging; Cellular Phone; Cessation of life; Clinical; Clinical Data; Clinical Research; Clinical Trials; Clinical Trials Design; Cohort Studies; Collaborations; Communities; Data; Degenerative Disorder; Diagnosis; Diagnostic; Differential Equation; Digital biomarker; Dimensions; Disease; Disease Progression; Disease model; Disease susceptibility; Early Diagnosis; Early Intervention; Ensure; Environmental Risk Factor; Event; Face; Family; Family history of; Family member; Funding; Genetic; Genetic Counseling; Genetic Risk; Genomics; Goals; Health; Health Status; Healthcare Systems; Heritability; Heterogeneity; Hospitals; Individual; International; Joints; Laboratories; Learning; Liquid substance; Manuscripts; Measurement; Measures; Methods; Modeling; Modernization; Neurodegenerative Disorders; Neurologist; Non-linear Models; Nonlinear Dynamics; Observational Study; Parkinson Disease; Participant; Pathologic; Pathology; Patients; Pattern; Persons; Phenotype; Police; Policies; Population; Population Heterogeneity; Precision Medicine Initiative; Process; Property; Publishing; Recording of previous events; Records; Research; Research Personnel; Research Priority; Risk; Site; Source; Space Models; Speech; Staging; Statistical Methods; Surveys; Symptoms; System; Testing; Therapeutic; Time; Translating; Translational Research; Universities; Walking; Work; analytical method; biomarker discovery; clinical biomarkers; clinical decision-making; clinical diagnosis; clinical diagnostics; clinical translation; cognitive task; cohort; comorbidity; data space; diagnostic accuracy; diagnostic criteria; digital; digital models; dynamic system; effective therapy; experimental study; improved; machine learning method; multimodal data; nervous system disorder; neuroimaging; neuropathology; neuropsychiatry; novel; optimal treatments; patient variability; personalized medicine; pre-clinical; precision medicine; predictive modeling; progression marker; recruit; response; risk prediction; semiparametric; simulation; specific biomarkers; statistical learning; statistics; theories; timeline; treatment response; trial design

Project Summary: Neurological disorders pose an immense burden on patients, families, and health care systems, thus un- derscoring the urgent need to develop disease-modifying treatment. Research on Alzheimer's disease and other degenerative diseases faces unique challenges, including the fact that these disorders typically have slow pro- gression, the diagnostic criteria rely on clinical symptoms due to a lack of highly sensitive and specific biomarkers in many diseases, and there is substantial disease and subject heterogeneity. Precision medicine initiative has re- cently been launched in diverse populations, providing access to data on hundreds of thousands of individuals and millions of families history records. International consortia of neurological disorders (e.g., National Alzheimer's Coordinating Center, Parkinson's Progression Markers Initiative) were established to unify multi-modality data in several large existing cohorts and provide access to new biomarker data. Digital phenotypes as real-world as- sessments of patients are being tested as digital biomarkers to track disease progression and potentially serve as digital endpoints for clinical trials. The main goal of this proposal is to develop modern analytic methods for these new data types and studies in order to address the emerging challenges in research on personalized disease susceptibility, progression, diagnostics, and treatments. We will build data-generative models that will lever- age complementary contributions from multi-type biomarkers, including genomic measures, brain neuroimaging, biofluids, comprehensive neuropsychiatric assessments, and digital biomarkers. These methods will be applied to carefully selected clinical data collected by the investigative team or available from large consortia in order to guide genetic counseling and risk prediction, assist disease staging and clinical trial design, and optimize person- alized treatment policies. Specifically, in Aim 1 we will jointly analyze co-morbidities from family data to estimate co-heritability and dissect whether shared phenotypic co-variation is due to latent environmental factors, genetic factors, or both. In Aim 2, we will develop an integrated, multi-domain dynamic system for mixed-type biomarkers (e.g., neuroimaging, genomics, fluid, clinical, neuropsychiatric markers) using differential equations modeled on a novel progression scale that uses latent processes. In Aim 3, we will leverage gold-standard neuropathological diagnosis based on postmortem brain autopsy combined with antemortem biomarkers to improve clinical diagno- sis of neurological disorders. In Aim 4, we will model multi-domain digital phenotypes to learn optimal treatment policy from digital biomarkers. In each aim, we will establish theoretical properties using modern empirical pro- cess theory and statistical learning theory. Together, the state-of-the-art analytic methods proposed here will substantially improve analytic accuracy, and our combined statistical and clinical expertise will ensure that our methods are translated directly back to the clinical and translational research community.

项目概要： 神经系统疾病给患者、家庭和医疗保健系统带来了巨大的负担，因此， 迫切需要开发改善疾病的治疗。阿尔茨海默病和其他疾病的研究 退行性疾病面临着独特的挑战，包括这些疾病通常具有缓慢的前 由于缺乏高度敏感和特异性的生物标志物，诊断标准依赖于临床症状 在许多疾病中，存在实质性的疾病和受试者异质性。精准医疗计划重新- 在不同人群中推出，提供数十万人的数据访问， 数百万家庭的历史记录。国际神经系统疾病联盟（例如，国家阿尔茨海默氏症 协调中心，帕金森病进展标志物倡议），以统一多模态数据， 几个大的现有队列，并提供新的生物标志物数据的访问。数字表型和现实世界一样- 患者的评估正在作为数字生物标志物进行测试，以跟踪疾病进展，并可能作为 临床试验的数字终点。该提案的主要目标是为这些问题开发现代分析方法。 新的数据类型和研究，以应对个性化疾病研究中出现的挑战 易感性、进展、诊断和治疗。我们将建立数据生成模型，将杠杆- 来自多种类型生物标志物的年龄互补贡献，包括基因组测量，脑神经成像， 生物体液、全面的神经精神评估和数字生物标志物。这些方法将应用于 仔细挑选研究小组收集的或从大型财团获得的临床数据， 指导遗传咨询和风险预测，协助疾病分期和临床试验设计，优化个人- 化的治疗政策。具体而言，在目标1中，我们将联合分析来自家庭数据的合并症，以估计 共遗传力和解剖是否共享表型共变异是由于潜在的环境因素，遗传 的因素，或两者。在目标2中，我们将开发一个集成的、多域的混合型生物标志物动态系统 (e.g.,神经成像、基因组学、神经胶质、临床、神经精神学标记物）使用微分方程建模， 一种使用潜在过程的新型进展量表。在目标3中，我们将利用黄金标准神经病理学 基于死后脑解剖结合生前生物标志物的诊断，以提高临床诊断， 神经系统疾病的姐姐在目标4中，我们将对多域数字表型进行建模，以学习最佳治疗方法 从数字生物标志物的政策。在每一个目标，我们将建立理论属性使用现代经验亲， cess理论和统计学习理论。总之，这里提出的最先进的分析方法将 大大提高分析准确性，我们的统计和临床专业知识将确保我们的 方法被直接翻译回临床和转化研究社区。