Application of advanced methodology to osteoarthritis phenotyping

骨关节炎（OA）是高度流行的，导致人群中的大量发病率，并且缺乏有效的治疗。 干预措施，以防止发病和进展。重要的是，像许多其他慢性疾病一样，OA不是 单一疾病，而是由多个亚组或表型组成的异质性疾病， 不同的潜在病理生理机制。越来越明显的是， 在临床研究和试验中发现特定的OA表型是推动该领域向前发展的关键。整体 这一系列工作的目标是识别和了解膝关节骨关节炎（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;这个探索性的R21项目使用OA倡议 (OAI)数据将调查新的方法来支持KOA的表型。成功的治疗 骨关节炎将需要有针对性的，并在特定的骨关节炎表型进行测试。我们假设 对KOA表型的理解是OA精准医学的关键一步，将导致更多的 长期的临床研究。为了解决这一重要的临床问题，我们提出了一个 在这个项目中，我们将把创新的机器学习方法和验证策略应用于来自 大型、公开可用的OAI队列。我们将利用这个大型数据集，沿着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来聚类最好的特征。 使用双聚类和一种新的 显著性检验（SigClust）由Co-I Marron开发。对于目标2，我们将测试相关性的特定假设 OA的结果，如有和没有OA之间的差异，或那些谁做或不发展 新的或恶化的疾病，使用另一组机器学习方法（方向投影排列 [DiProPerm]假设检验和距离加权判别[DWD]），也由co-I Marron开发， 在整个队列中以及在目标1中的任何确定的集群中。为了实现这些目标，本提案 涉及统计学，生物统计学，计算机科学， 风湿病学和流行病学。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， 亚组的潜在机制，并使用这些信息来通知和重点未来的临床研究。在 从长远来看，我们预计这一战略将导致更加个性化和成功的管理， 数百万人受到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.