Application of advanced methodology to osteoarthritis phenotyping

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。

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.