权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

骨性关节炎(OA)非常普遍，在人群中造成相当大的发病率，并且缺乏有效的治疗方法预防发病和进展的干预措施。重要的是，和许多其他慢性疾病一样，骨性关节炎并不是一种单一的疾病，而是一种由多个亚群或表型组成的异质性疾病，不同的潜在病理生理机制。越来越清楚的是，考虑到临床研究和试验中的特定OA表型是推动该领域向前发展的关键。整体而言这项工作的目标是识别和了解膝骨性关节炎(KOA)的潜在表型。为了更好地为未来的研究工作和治疗提供信息；这一探索性的R21项目使用了OA倡议 (OAI)数据将调查支持KOA表型的新方法。成功的治疗方法 OA将需要针对特定选择的OA表型并在其中进行测试。我们的假设是，一个理解膝骨性关节炎的表型，这是在骨性关节炎中迈向精确医学的关键一步，将导致更多长期的成功临床研究。为了解决这个重要的临床问题，我们提出了一种项目中，我们将创新的机器学习方法和验证策略应用于来自大规模、可公开获得的OAI队列。我们将利用这一大型数据集，以及当地的统计专业知识，生物统计学和机器学习方法，以解决这种异质性的表型问题疾病。在目标1中，我们将利用数据驱动的无监督学习方法，对最好的特征进行分类在OAI数据集中定义和区分KOA的表型，使用双聚类和一个新的显著性检验(SigClust)是由合作者Marron开发的。对于目标2，我们将测试相关的特定假设对OA结果的影响，例如患有和不患有OA的人之间的差异，或者有或不发展的人之间的差异新的或恶化的疾病，使用另一套机器学习方法(方向-投影-排列 [DiProPerm]假设检验和距离加权判别[DWD])，也是由Co-I Marron开发的，在目标1的全部队列和任何确定的集群中。为了解决这些目标，本提案涉及统计学、生物统计学、计算机科学、风湿病和流行病学。这项工作将通过满足以下关键需求对该领域产生重大影响准确定义OA表型，发现与这些表型相关的关键特征，链接表型分组到潜在的机制，并使用这些信息来通知和关注未来的临床研究。在从长远来看，我们预计这一战略将导致更个性化和成功的管理数以百万计的人受到OA的影响。

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?

通过人工智能，骨关节炎表型的分层有多可行？

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.