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

项目总结： 神经性疾病给患者、家庭和卫生保健系统带来了巨大的负担，因此， 嘲讽了开发疾病修正疗法的迫切需要。阿尔茨海默病等的研究进展 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促进性- 由于缺乏高度敏感和特异的fic生物标志物，诊断标准依赖于临床症状。 在很多疾病中，都有实质性的疾病和学科的异质性。精准医疗计划重新启动- Centrly在不同的人群中启动，提供对数十万个人和 数以百万计的家庭历史记录。国际神经疾病联合会(例如，国家阿尔茨海默氏症协会 协调中心，帕金森进展标记物倡议)成立，以统一多模式数据在 几个现有的大型队列，并提供访问新的生物标记物数据。数字表型就像现实世界一样- 患者的病情正在作为数字生物标记物进行测试，以跟踪疾病的进展，并有可能成为 临床试验的数字终端。这项提议的主要目标是发展现代分析方法来分析这些 新的数据类型和研究，以应对个性化疾病研究中新出现的挑战 敏感度、进展、诊断和治疗。我们将构建数据生成模型，该模型将利用- 多种类型生物标记物的年龄互补贡献，包括基因组测量、脑神经成像、 BioflUID、全面的神经精神评估和数字生物标记物。这些方法将被应用于 由调查小组收集或从大财团获得的仔细挑选的临床数据，以便 指导遗传咨询和风险预测，辅助疾病分期和临床试验设计，优化人-机 标准化的治疗政策。具体地说，在目标1中，我们将联合分析家庭数据中的并存疾病以估计 共同遗传力和解剖共享表型协同变异是否由潜在的环境因素，遗传 因素，或两者兼而有之。在目标2中，我们将为混合型生物标志物开发一个集成的、多领域的动态系统 (例如，神经成像、基因组学、flUID、临床、神经精神病学标记物) 一种使用潜在过程的新的进度量表。在目标3中，我们将利用黄金标准的神经病理学 尸检结合生前生物标志物诊断提高临床诊断率 神经性疾病的SIS。在目标4中，我们将对多领域数字表型进行建模，以学习最佳治疗 来自数字生物标记物的政策。在每个目标中，我们将使用现代经验PRO建立理论性质。 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 大幅提高分析准确性，我们的统计和临床专业知识相结合将确保我们的 方法被直接翻译回临床和翻译研究社区。

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.