Towards Better Understanding of ALS using a Multi-Marker Discovery Approach from a Multi-Modal Database (ALS4M)

使用多模态数据库的多标记发现方法更好地理解 ALS (ALS4M)

• 批准号: 10704220
• 负责人: Xing Song
• 金额: $ 29.98万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10704220
• 关键词: Towards; Better; Understanding; ALS; using

PROJECT SUMMARY / ABSTRACT The overarching goal of this study is to use new large multi-modal data resources and machine-learning-based data mining algorithm to better understand risk factors and improve diagnosis for people with Amyotrophic lateral sclerosis (ALS). Amyotrophic lateral sclerosis (ALS) is a rare, fatal neurodegenerative disorder, with 90% sporadic cases do not have genetic causes and their contributing risk factors are largely unknown. Most of what is known about ALS risk factors comes from epidemiological studies using registry data, which historically forms the main standardized big data source to help describe the natural history, epidemiology, and burden of disease; however, the strength of evidence resulting from these studies varies greatly. One potential major limitation to registry data are the fields collected are based upon known potential risk factors, which have restricted its usability for exploring novel associations and causalities. Moreover, ALS is a rare disease with low prevalence, thus making it infeasible to study its etiology using traditional observational study design due to statistical power constraints. The digitization of healthcare records and the capacity to link to other relevant data sources now enables a more representative, enriched and statistically powerful study population; and ideal for leveraging machine-learning-driven, hypothesis-generating models to identify new risk factors and patterns identify new risk factors important for understanding, diagnosing, or treating people with ALS. Building on established well-integrated real world big data source and established ensemble embedded feature selection framework, an established multi-marker (biomarker, clinical marker, geo-marker, socio-marker) discovery algorithm will be developed to discover novel, generalizable risk factors (Aim 1); new symptomatic patterns for early diagnosis (Aim 2), and effective clinical care pathways for ALS (Aim 3). To best translate findings into clinical insights, a multi-disciplinary and multi-stakeholder team has been assembled, including not only investigators with diverse expertise in statistics, machine learning, clinical research informatics, neurology, computer science, epidemiology, but also an engaging patient advisory board with diverse social background. The proposed work will be one of the first pilot studies applying AI/ML-based, hypothesis-generating algorithms on statistically powerful real-world data to bridge the knowledge gap on ALS risk factors. The work will not only provide CDC agency of toxic substance and disease registry (ATSDR) with empirical evidence to better prioritize future decisions on expanding the ALS registry risk factor survey but serve to inform better designed proposals for future etiological studies and targeted trials for ALS. This study will also provide an exemplar framework which can be generalizable to advance research of other rare and complex disease domains by leveraging real world evidence.

项目摘要 /摘要 这项研究的总体目标是使用新的大型多模式数据资源和基于机器的学习资源 数据挖掘算法以更好地理解危险因素并改善肌萎缩症患者的诊断 侧硬化症（ALS）。肌萎缩性侧索硬化症（ALS）是一种罕见的致命神经退行性疾病，具有 90％的零星病例没有遗传原因，其促成风险因素在很大程度上未知。最多 关于ALS风险因素的知识来自使用注册表数据流行病学研究， 从历史上看 疾病负担；但是，这些研究产生的证据强度差异很大。一个潜力 注册表数据的主要限制是收集的领域是基于已知的潜在危险因素，这些风险因素已 限制了其探索新型关联和因果关系的可用性。此外，ALS是一种罕见的疾病，低 患病率，因此，使用传统的观察性研究设计研究其病因是不可行的 统计功率约束。医疗保健记录的数字化以及链接到其他相关的能力 数据源现在使一个更具代表性，丰富且统计上强大的研究人群可以进行。和 利用机器学习驱动的假设生成模型的理想选择，以确定新的风险因素和 模式确定新的风险因素对于理解，诊断或治疗ALS的人很重要。建筑 在既定良好的现实世界大数据源和建立的合奏嵌入式功能上 Selection框架，已建立的多标记（生物标记物，临床标记，地球标志物，社会标记） 将开发发现算法以发现新颖的可推广风险因素（AIM 1）；新的症状 早期诊断的模式（AIM 2）和ALS的有效临床护理途径（AIM 3）。最好翻译 临床见解的结果，一个多学科和多方利益相关者团队已经组装了，包括不包括 只有在统计，机器学习，临床研究信息学，神经病学，神经病学方面具有多样专业知识的研究人员， 计算机科学，流行病学，也是具有不同社会背景的引人入胜的患者顾问委员会。 拟议的工作将是最早采用基于AI/ML的假设生成算法的试点研究之一 关于统计强大的现实世界数据，以弥合ALS风险因素的知识差距。这项工作不仅会 向CDC有毒物质和疾病登记处（ATSDR）提供经验证据以更好 优先考虑扩大ALS注册处风险因素调查的未来决定，但有助于告知设计更好 未来病因研究的建议和ALS的针对性试验。这项研究还将提供一个典范 可以推广的框架可以通过 利用现实世界的证据。