Application of advanced methodology to osteoarthritis phenotyping

先进方法在骨关节炎表型分析中的应用

基本信息

批准号：
10083187
负责人：
Amanda E Nelson
金额：
$ 16.06万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-15 至 2021-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10083187
关键词：
Address Affect Age-Years Arthritis Biomechanics Biometry Cartilage Chronic Clinical Clinical Research Clinical Trials Clinical Trials Design Data Data Set Degenerative polyarthritis Discrimination Disease Epidemiology Etiology Failure Fibrinogen Future General Population Goals Heterogeneity Individual Inflammation Injury Intervention Joints Knee Injuries Knee Osteoarthritis Link Machine Learning Meniscus structure of joint Methodology Morbidity - disease rate Non obese Obesity Outcome Pain Patients Persons Pharmaceutical Preparations Phenotype Population Progressive Disease Public Health Randomized Research Methodology Resources Rheumatology Risk Factors Structure Subgroup Symptoms Syndrome Synovial Membrane Techniques Testing Time Tissues Validation Visit Work base bone cohort common treatment computer science demographics design disability drug development effective intervention experience improved injured innovation interdisciplinary collaboration joint destruction large datasets loss of function machine learning method novel precision medicine prevent statistics unsupervised learning

项目摘要

Osteoarthritis (OA) is highly prevalent, contributes to substantial morbidity in the population, and lacks effective interventions to prevent onset and progression. Importantly, and like many other chronic conditions, OA is not a single disease but rather a heterogeneous condition consisting of multiple subgroups, or phenotypes, with differing underlying pathophysiological mechanisms. It is becoming increasingly clear that consideration of specific OA phenotypes in clinical studies and trials is critically needed to move the field forward. The overall goal of this line of work is to identify and understand potential phenotypes of knee osteoarthritis (KOA) to better inform future research efforts and treatments; this exploratory R21 project using OA Initiative (OAI) data will investigate novel methodology to support phenotyping in KOA. Successful treatments for OA will need to be targeted to, and tested in, specifically chosen OA phenotypes. Our hypothesis is that an understanding of KOA phenotypes, a key step toward Precision Medicine in OA, will lead to more successful clinical studies in the long-term. To approach this important clinical problem, we propose a project in which we will apply innovative machine learning methods and validation strategies to data from the large, publicly available OAI cohort. We will leverage this large dataset, along with local expertise in statistics, biostatistics and machine learning methodology, to tackle the problem of phenotyping this heterogeneous disease. In Aim 1, we will utilize a data-driven, unsupervised learning approach, to cluster features that best define and discriminate among phenotypes of KOA in the OAI dataset, using biclustering and a novel significance test (SigClust) developed by co-I Marron. For Aim 2, we will test specific hypotheses of relevance to OA outcomes, such as differences between those with and without OA, or those who do or do not develop new or worsening disease, using another set of machine learning methods (Direction-projection-permutation [DiProPerm] hypothesis testing, and Distance-Weighted Discrimination [DWD]), also developed by co-I Marron, in the full cohort and in any identified clusters from Aim 1. In order to address these aims, this proposal involves interdisciplinary collaborations among experts in statistics, biostatistics, computer science, rheumatology, and epidemiology. This work will significantly impact the field by fulfilling a critical need to accurately define OA phenotypes, discover the key features associated with these phenotypes, link phenotype subgroups to underlying mechanisms and use this information to inform and focus future clinical studies. In the long term, we expect that this strategy will lead to more personalized and successful management of the millions of people affected by OA.

骨关节炎（OA）高度普遍，有助于人群的大量发病率，并且缺乏有效防止发作和进展的干预措施。重要的是，与许多其他慢性疾病一样，OA也不是一种疾病，但是由多个亚组或表型组成的异质疾病不同的潜在病理生理机制。越来越清楚的是临床研究和试验中的特定OA表型至关重要。总体这项工作的目标是识别和了解膝盖骨关节炎（KOA）的潜在表型更好地告知未来的研究工作和治疗；使用OA计划的探索性R21项目（OAI）数据将研究支持KOA表型的新方法。成功治疗 OA将需要针对并在特殊选择的OA表型中进行定位并进行测试。我们的假设是了解KOA表型是OA中精确医学的关键一步，将导致更多长期成功的临床研究。为了解决这个重要的临床问题，我们提出了我们将在其中将创新的机器学习方法和验证策略应用于数据大型，公共可用的OAI队列。我们将利用这个大数据集，以及统计学的本地专业知识，生物统计学和机器学习方法，以解决表型的问题疾病。在AIM 1中，我们将利用一种数据驱动的，无监督的学习方法，将其用于最佳的特征使用二次和新颖 Co-I Marron开发的显着性测试（Sigclust）。对于AIM 2，我们将测试特定相关性的假设 OA结果，例如有或没有OA的人或那些或不发展的人之间的差异使用另一套机器学习方法（方向预测 - 渗透），新的或恶化的疾病 [二倍体]假设检验和距离加权歧视[DWD]），也由Co-i Marron开发在AIM 1的完整队列和任何已确定的群集中。为了解决这些目标，该提案涉及统计，生物统计学，计算机科学专家之间的跨学科合作风湿病学和流行病学。这项工作将通过满足至关重要的需求，从而极大地影响该领域准确定义OA表型，发现与这些表型相关的关键特征，链接表型基础机制的亚组，并使用此信息来为未来的临床研究提供信息和关注。在长期，我们希望这种策略将导致对数以百万计的人受OA影响。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

How feasible is the stratification of osteoarthritis phenotypes by means of artificial intelligence?

DOI：
10.1080/23808993.2021.1848424
发表时间：
2021
期刊：
Expert review of precision medicine and drug development
影响因子：
1.2
作者：
Nelson AE
通讯作者：
Nelson AE

Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative.

双聚类揭示了探索性分析中潜在的膝关节 OA 表型：来自骨关节炎倡议的数据。

DOI：
10.1371/journal.pone.0266964
发表时间：
2022
期刊：
PLOS ONE
影响因子：
3.7
作者：
Nelson, Amanda F.;Keefe, Thomas F.;Schwartz, Todd M.;Callahan, Leigh;Loeser, Richard;Golightly, Yvonne;Arbeeva, Liubov;Marron, J. S.
通讯作者：
Marron, J. S.

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.